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'''Redwood Center for Theoretical Neuroscience''' <br />
Friedrich T. Sommer, Ph.D. <br />
Helen Wills Neuroscience Institute <br />
University of California, Berkeley <br />  
132 Barker, MC #3190 <br />
Redwood Center for Theoretical Neuroscience - HWNI <br />
Berkeley, CA 94720-3190 <br />
575A Evans Hall MC# 3198 <br />
phone (510) 643-4010 <br />
Berkeley, CA 94720-3198 <br />
efax (413) 618-4731 <br />
phone (510) 642-7251 <br />
<fsommer at berkeley dot edu>
fax (510) 642-7206 <br />
email F $ 0 M M E R (a) B E R K E L E Y * E D U (please retype)<br />
<br style="clear:both;" />
<br style="clear:both;" />
Researcher in Residence, Intel Labs & Adjunct Professor, Redwood Center for Theoretical Neuroscience & [http://neuroscience.berkeley.edu/ Helen Wills Neuroscience Institute], University of California, Berkeley <br />
Faculty member (Hochschuldozent), Department of Computer Science, [http://www.uni-ulm.de/ University of Ulm]<br />
<br />
Previous appointments:<br />
2005-2011 Associate Adjunct Professor, University of California, Berkeley<br />
2009 Acting director of the Redwood Center for Theoretical Neuroscience at UC Berkeley<br />
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== Research Interests ==
== Research Interests ==


I am interested how neurons in the brain collaborate to produce human memory, perception and cognition. To study this question I use computational models of the brain and advanced data analysis techniques. For a full list of my research publications, see the [[Media:fsommer_cv.pdf|cv]].
Many impressive capabilities of the brain are not yet understood, for example, how unsupervised learning shapes the brains of animals and humans while controlling closed action-perception loops with the environment, or the virtually unlimited capacity of our long-term memory and its close connection to spatial navigation.
 
In collaboration with experimental neuroscience labs, my lab investigates the theoretical principles of learning and perception and their biological bases in the circuit dynamics of the brain. To study these issues we develop computational models, advanced techniques of data analysis, and strategies for sharing neurophysiology data [http://www.crcns.org CRCNS.org].
 
At the same time, I am interested in neurobiological design principles for building artificial intelligence systems. I am currently on a partial leave from the University to apply these ideas to the development of neuromorphic computing at Intel Labs.  
 
For a full list of publications, see [https://scholar.google.com/citations?hl=en&view_op=list_works&gmla=AJsN-F6OQfburHGgSijJ71YjBkMzeChlIy-MFcZM2jCPQk78E2IGxggVw2f18ll6GXAXpj70ExNW52y9VETzujZFBE3a5ChB99X6jB_IWauOzL7Ilp7M2CE&user=lA-oLkgAAAAJ Google Scholar Profile]
 
==Journal articles, book chapters and submissions==
 
==== Submissions/arXiv'ed manuscripts ====
Z. Li, Y. Chen, Y. LeCun, F. T. Sommer: Neural Manifold Clustering and Embedding. https://arxiv.org/abs/2201.10000 (2022)
 
C. Warner, F. T. Sommer: A probabilistic latent variable model for detecting structure in binary data. https://arxiv.org/abs/2201.11108 (2022)
 
E. P. Frady, D. Kleyko, C. J. Kymn, B. A. Olshausen, F. T. Sommer: Computing on functions using randomized vector representations. https://arxiv.org/abs/2109.03429 (2021)
 
D. Kleyko, M. Davies, E. P. Frady, P. Kanerva, S. J. Kent, B. A. Olshausen, E. Osipov, J. M. Rabaey, D. A. Rachkovskij, A. Rahimi, F. T. Sommer: Vector Symbolic Architectures as computing framework for nanoscale hardware. https://arxiv.org/pdf/2106.05268.pdf (2021)
 
C. Warner, F. T. Sommer: A Model for Image Segmentation in Retina [https://arxiv.org/abs/2005.02567 arXiv] (2020)
 
Z. Li, F. T. Sommer: The amplitude-phase complex Boltzmann machine [https://arxiv.org/abs/2005.01862 arXiv] (2020)
 
C. Bybee, E. P. Frady, F. T. Sommer: Deep learning in spiking phasor neural networks, in preparation
 
==== Recent Publications ====
D. Toker, I. Pappas, J. D. Lendner, J. Frohlich, D. M. Mateos, S. Muthukumaraswamy, R. Carhart-Harris, M. Pfaff, P. M. Vesta, M. M. Monti, F. T. Sommer, R. T. Knight, M. D'Esposito: Consciousness is supported by near-critical cortical electrodynamics. PNAS https://www.pnas.org/content/119/7/e2024455119 (2022)


== Recent Publications ==
D. Kleyko, E. P. Frady, F. T. Sommer: Cellular Automata Can Reduce Memory Requirements of Collective-State Computing, IEEE Transactions on Neural Networks and Learning Systems. Print ISSN: 2162-237X, Online ISSN: 2162-2388, Digital Object Identifier:  [https://ieeexplore.ieee.org/document/9586079 10.1109/TNNLS.2021.3119543] (2021)<br>
(Earlier version [https://arxiv.org/abs/2010.03585 arXiv] (2020))


K. Koeppsell, X. Wang, Y. Wei, V. Vaingankar, J. A. Hirsch, F. T. Sommer: Dynamical coding opens a novel channel for visual information to reach cortex. (2006) submitted
E. P. Frady, D. Kleyko, F. T. Sommer: Variable Binding for Sparse Distributed Representations: Theory and Applications. IEEE Transactions on Neural Networks and Learning Systems [https://ieeexplore.ieee.org/document/9528907 10.1109/TNNLS.2021.3105949] (2021)<br>
(Earlier version [https://arxiv.org/abs/2009.06734 arXiv] (2020))


M. Rehn, F. T. Sommer: A network that uses few active neurons to code visual input predicts the diverse shapes of cortical receptive fields. (2006) submitted
Zengyi Li, Yubei Chen, F. T. Sommer: A Neural Network MCMC Sampler That Maximizes Proposal Entropy. Entropy 23(3), 269;
[https://www.mdpi.com/1099-4300/23/3/269?utm_campaign=releaseissue_entropyutm_medium=emailutm_source=releaseissueutm_term=titlelink4 doi:10.3390/e23030269] (2021)<br>
(Earlier version [https://arxiv.org/abs/2010.03587 arXiv] (2020))


M. Rehn, F. T. Sommer: Storing and restoring visual input with collaborative rank coding and associative memory.  
E. P. Frady, S. J. Kent, B. A. Olshausen and F. T. Sommer: Resonator Networks, 1: An Efficient Solution for Factoring High-Dimensional, Distributed Representations of Data Structures. Neural Computation 32 (12):  [https://doi.org/10.1162/neco_a_01331 2311–2331] (2020)
Neurocomputing (2006) in press [http://redwood.berkeley.edu/~fsommer/papers/rehnsommer05.pdf  pdf]
 
S. J. Kent, E. P. Frady, F. T. Sommer and B. A. Olshausen: Resonator Networks, 2: Factorization Performance and Capacity Compared to Optimization-Based Methods. Neural Computation 32 (12): [https://doi.org/10.1162/neco_a_01329 2332–2388] (2020)
 
D. Toker, F. T. Sommer, M Desposito, M: A simple method for detecting chaos in nature. Communications Biology [https://www.nature.com/articles/s42003-019-0715-9 3, 11] (2020)
 
E. P. Frady, G. Orchard, D. Florey, N. Imam, R. Liu, J. Mishra, J. Tse, A. Wild, F. T. Sommer, M. Davies: Neuromorphic Nearest-Neighbor Search Using Intel's Pohoiki Springs. NICE '20: Proceedings of the Neuro-inspired Computational Elements WorkshopMarch 2020  [https://doi.org/10.1145/3381755.3398695 Article No.: 23, Pages 1–10] (2020)<br>
(Earlier version [https://arxiv.org/abs/2004.12691 arXiv] (2020))
 
Z. Li, Y. Chen, F. T. Sommer: Annealed Denoising Score Matching: Learning energy based models in high dimensional spaces [https://openreview.net/forum?id=HJeFmkBtvB ICLR 2020 Open Review] (2020)
 
J. A. Livezey, A. F. Bujan, F. T. Sommer: Learning Overcomplete, low coherence dictionaries with linear inference. Journal of Machine Learning Research [http://jmlr.org/papers/v20/18-703.html 20(174):1−42] (2019)
 
E. P. Frady, F. T. Sommer: Robust computation with rhythmic spike patterns. Proceedings of the National Academy of Sciences  [https://doi.org/10.1073/pnas.1902653116 September 3, 116 (36) 18050-18059] (2019), [https://neuroscience.berkeley.edu/new-model-of-neural-processing-could-help-us-understand-the-brain-and-create-better-ai/ UCB press release]<br>
(Earlier version [https://arxiv.org/abs/1901.07718 arXiv] (2019))
 
D. Toker, F. T. Sommer: Information integration in large brain networks. [https://doi.org/10.1371/journal.pcbi.1006807 PLOS Computational Biology] (2019)<br>
(Earlier version [https://arxiv.org/abs/1708.02967 arXiv] (2018))
 
E. P. Frady, D. Kleyko, F. T. Sommer: A theory of sequence indexing and working memory in recurrent neural networks. Neural Computation, 30(6), 1449-1513.  (2018)
 
K. E. Bouchard, J. B. Aimone, M. Chun, T. Dean, M. Denker, M. Diesmann, D. D. Donofrio, L. M. Frank, N. Kasthuri, C. Koch, O. Rübel, H. D. Simon, F. T. Sommer, Prabhat: International neuroscience initiatives through the lens of high-performance computing. Computer, 51(4): 50-59 (2018)
 
==== Publications 2017 - 2003 ====
C. Soto-Sánchez, X. Wang, V. Vaingankar, F. T. Sommer, J. A. Hirsch: The  spatial scale of receptive fields in the visual sector of the cat’s thalamic reticular nucleus. Nature Communications 8, 800 (2017) doi:10.1038/s41467-017-00762-7 (2017)
 
K. E. Bouchard, A. F. Bujan, E. F. Chang, F. T. Sommer: Sparse coding of ECoG signals identifies interpretable components for speech control in human sensorimotor cortex. IEEE, EMBC (2017)
 
K. E. Bouchard, J. B. Aimone, M. Chun, T. Dean, M. Denker, M. Diesmann, D. Donofrio, L. M. Frank, N. Kasthuri, C. Koch, O. Rübel, H. Simon, F. T. Sommer, Prabhat: High-Performance Computing in Neuroscience for Data-Driven Discovery, Integration, and Dissemination.  [http://www.cell.com/neuron/fulltext/S0896-6273(16)30785-1?_returnURL=http%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0896627316307851%3Fshowall%3Dtrue Neuron] 92:628-631 (2016)
 
A. Knoblauch, F. T. Sommer: Structural plasticity, effectual connectivity and memory in cortex.  [http://journal.frontiersin.org/article/10.3389/fnana.2016.00063/abstract Frontiers in Neuroanatomy] (2016)


F. T. Sommer, P. Kanerva: Can neural models of cognition benefit from the advantages of connectionism?
J. A. Livezey, A. F. Bujan, F. T. Sommer: On degeneracy control in overcomplete ICA [http://arxiv.org/abs/1606.03474 arXiv] (2016)
Behavoral Brain Sciences (2006) in press [http://redwood.berkeley.edu/~fsommer/papers/sommerkanerva05.pdf  pdf]
   
   
F. T. Sommer, T. Wennekers: Synfire chains with conductance-based neurons: internal timing and coordination with timed input.
V. Suresh, U.M.  Çiftçioğlu, X. Wang, B. M. Lala, K. R. Ding, W. A. Smith, F. T. Sommer, J. A. Hirsch: Synaptic Contributions to Receptive Field Structure and Response Properties in the Rodent Lateral Geniculate Nucleus of the Thalamus. [http://www.jneurosci.org/content/36/43/10949.long Journal of Neuroscience] 36(43), 10949-10963 (2016)
Neurocomputing 65-66 (2005449 - 454  [http://redwood.berkeley.edu/~fsommer/papers/sommerwennekers05.pdf  pdf]
 
J. L. Teeters, K.  Godfrey, R.  Young, C.  Dang, C. Friedsam, B.  Wark, H.  Asari, S. 
Peron, N.  Li, A.  Peyrache, G. Denisov, J. H. Siegle, S. R.  Olsen, C.  Martin, M. Chun, S. Tripathy, T. J. Blanche, K. D. Harris, G. Buzsaki, C.  Koch, M. Meister, K. Svoboda, F. T. Sommer: Neurodata Without Borders: Creating 
a common data format for neurophysiology. [http://www.cell.com/neuron/fulltext/S0896-6273(15)00919-8?_returnURL=http%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0896627315009198%3Fshowall%3Dtrue Neuron] 88:629-634 (2015)
 
C. J. Hillar and F. T. Sommer: When can dictionary learning uniquely recover sparse data from subsamples? [http://ieeexplore.ieee.org/document/7165675/ IEEE Transactions on Information Theory] 61(11):6290-6297 (2015). (Earlier [http://arxiv.org/abs/1106.3616 arXiv version] (2013))
 
S. Mobin, J. Arnemann, F. T. Sommer: Information-based learning by agents in unbounded state spaces. Advances in Neural Information Processing Systems [https://papers.nips.cc/paper/5266-information-based-learning-by-agents-in-unbounded-state-spaces NIPS] 26, MIT Press (2014).
 
G. Agarwal, I. H. Stevenson, A. Berényi, K. Mizuseki, G. Buzsáki, F. T. Sommer: Spatially distributed local fields in the hippocampus encode rat position. [http://science.sciencemag.org/content/344/6184/626.long Science] 344 (2014): 626-630.
 
A. Knoblauch, E. Koerner, U. Koerner, F. T. Sommer: Structural synaptic plasticity has high memory capacity and can explain graded amnesia, catastrophic forgetting, and the spacing effect. [http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0096485 PLOS ONE] (2014)


D. George, F. T. Sommer: Computing with inter-spike  inverval codes in networks of integrate and fire neurons.
L. M. Martinez, M. Molano-Mazon, X. Wang, F. T. Sommer, J. A. Hirsch: Statistical wiring of thalamic receptive fields optimizes spatial sampling of the retinal image. [http://www.cell.com/neuron/fulltext/S0896-6273(13)01145-8 Neuron] 81 (2014) 943-956 [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4114508/ PubMed pdf] (2014)
Neurocomputing  65-66 (2005) 414 - 420 [http://redwood.berkeley.edu/~fsommer/papers/georgesommer05.pdf  pdf]


L. M. Martinez, Q. Wang, R. C. Reid, C. Pillai, J.-M. Alonso, F. T. Sommer, J. A. Hirsch: Receptive field structure varies with layer in the primary visual cortex.
J. A. Hirsch, X. Wang, V. Vaingankar, F. T. Sommer: Inhibitory circuits in the visual thalamus. Chapter in: The New Visual Neurosciences, Eds.: Leo M. Chalupa and John S. Werner, MIT Press (2013)
Nature Neuroscience 8 (12) (2005) 372 - 379 [http://redwood.berkeley.edu/~fsommer/papers/martinezetal05.pdf  pdf]


A. Knoblauch, F. T. Sommer: Spike-timing dependent plasticity can form "zero-lag" links for cortical oscillations.
F. T. Sommer: Neural oscillatons and synchrony as mechanisms for coding, communication and computation in the visual system. Chapter in: The New Visual Neurosciences, Eds.: Leo M. Chalupa and John S. Werner, MIT Press (2013) [http://www.rctn.org/w/images/c/c4/Sommer13chapt_vis_neurosci.pdf pdf]
Neurocomputing 52-54 (2004) 301 - 306  [http://redwood.berkeley.edu/~fsommer/papers/KnoblauchSommer04.pdf   pdf]


M. Rehn, F. T. Sommer: A network for the rapid formation of binary sparse representations of sensory input.
D. Y. Little, F. T. Sommer: Learning and exploration in action-perception loops. [http://www.frontiersin.org/neural_circuits/10.3389/fncir.2013.00037/abstract Frontiers in Neural Circuits]. doi: 10.3389/fncir.2013.00037 (2013). Earlier [http://arxiv.org/abs/1112.1125 arXiv version]: Learning in embodied action-perception loops through exploration (2011)
Technical Report RNI-04-(2004)


G. Glatting, F. M. Mottaghy, J. Karitzky, A. Baune, F. T. Sommer, G. B. Landwehrmeyer, S. N. Reske: Improving binding potential analysis in [11C]raclopide PET studies using cluster analysis.
D. Y. Little, F. T. Sommer: Maximal mutual information, not minimal entropy, for escaping the "Dark Room". Comment on "Whatever next? Predictive brains, situated agents, and the future of cognitive science." in Behavioral Brain Sciences 2013 Jun;36(3):220-221. doi: 10.1017/S0140525X12002415 [http://www.ncbi.nlm.nih.gov/pubmed/23663756]
Medical Physics 31 (4) (2004) 902-906 [http://redwood.berkeley.edu/~fsommer/papers/glattingetal04.pdf  pdf]


J. A. Hirsch, L. M. Martinez, C. Pillai, J.-M. Alonso, Q. Wang, F. T. Sommer: Functionally distinct inhibitory neurons at the first stage of visual cortical processing.
G. Agarval, F. T. Sommer: Measuring information in spike trains about intrinsic brain signals. Chapter in: [http://books.google.com/books?id=KTHUIMUpQCUC&pg=PA137&lpg=PA137&dq=agarwal+sommer+information+theory&source=bl&ots=78JmjIQQPb&sig=Rt9ATxPLJDx-2kdf-g5LKbxlwYI&hl=en&sa=X&ei=QKiCUevFOsm2igLKiIGICw&ved=0CGcQ6AEwBw#v=onepage&q=agarwal%20sommer%20information%20theory&f=false Spike timing: Mechanisms and functions], Eds.: P. M. DiLorenzo, J. D. Victor, CRC Press - Taylor & Francis Group 137-152 (2013)
Nature Neuroscience 6 (12) (2003) 1300 - 1308 [http://redwood.berkeley.edu/~fsommer/papers/hirschetal03.pdf  pdf]


F. T. Sommer, T. Wennekers: Models of distributed associative memory networks in the brain
V. Vaingankar, C. Soto-Sanchez, X. Wang, F. T. Sommer, J. A. Hirsch: Neurons in the thalamic reticular nucleus are selective for diverse and complex visual features. [http://journal.frontiersin.org/article/10.3389/fnint.2012.00118/abstract Frontiers in Integrative Neuroscience] 6:118. DOI: 10.3389/fnint.2012.00118  (2012)
Theory in Biosciences (122) (2003) 70 - 86  [http://redwood.berkeley.edu/~fsommer/papers/sommerwennekers03.pdf  pdf]


Eds: F. T. Sommer, A. Wichert: Exploratory analysis and data modeling in functional neuroimaging
C. Hillar, F. T. Sommer: Comment on the article "Distilling free-form natural laws from experimental data" [http://arxiv.org/abs/1210.7273 arXiv] (2012)
MIT Press, Boston, MA (2003) [http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=9191 link to publisher (table of contents, etc.)]
A. Knoblauch, F. T. Sommer: Synaptic plasticity, conduction delays, and inter-areal phase relations of spike activity in a model of reciprocally connected areas
Neurocomputing (52-54) (2003) 301-306 [http://redwood.berkeley.edu/~fsommer/papers/knoblauchsommer03.pdf  pdf]


V. Schmitt, R. Koetter, F. T. Sommer: The impact of thalamo-cortical projections on activity spread in cortex
X. Wang, F. T. Sommer, J. A. Hirsch: Inhibitory circuits for visual processing in thalamus. [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3767471/ Current Opinion in Neurobiology] 21 (2011) 726-733 [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3767471/ PubMed pdf]
Neurocomputing (52-54) (2003) 919-924 [http://redwood.berkeley.edu/~fsommer/papers/schmittetal03.pdf  pdf]


== Synopsis of Research ==
X. Wang, V. Vaingankar, C. Soto Sanchez, F. T. Sommer, J. A. Hirsch: Thalamic interneurons and relay cells use complementary synaptic mechanisms for visual processing. [http://www.nature.com/neuro/journal/v14/n2/full/nn.2707.html Nature Neuroscience] 14 (2011) 224-231 [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3767474/ PubMed pdf]


=== Computational models of the brain ===
F. T. Sommer: Associative memory and learning. Chapter in [https://link.springer.com/referenceworkentry/10.1007%2F978-1-4419-1428-6_375 Encyclopedia of the Sciences of Learning], Ed.: N. Seel, Springer (2011)


Behavior can be linked to computations in the brain and studying computational models of the brain can reveal the basic types of computation possible in nerve tissue. A computational model of the brain is a mathematical description of the brain that relates state changes in the brain to computation. Thus, a computational model of the brain consists of two components, first a description of the dynamics of brain states, such as neural activity patterns, synaptic states, etc., and second a representational scheme how brain states relate to behavioral entities, such as sensory input or memories.  
G. Isely, C. Hillar, F. T. Sommer: Decyphering subsampled data: Adaptive compressive sampling as a principle of brain communication. Advances in Neural Information Processing Systems [http://papers.nips.cc/paper/4093-deciphering-subsampled-data-adaptive-compressive-sampling-as-a-principle-of-brain-communication NIPS] 23. Eds: J. Lafferty and C. K. I. Williams and J. Shawe-Taylor and R.S. Zemel and A. Culotta (2011) 910-918 [http://www.google.com/url?sa=t&source=web&cd=1&ved=0CBoQFjAA&url=http%3A%2F%2Fbooks.nips.cc%2Fpapers%2Ffiles%2Fnips23%2FNIPS2010_1099.pdf&ei=aPFvTdLvLZHGsAPZ-5i_Cw&usg=AFQjCNGKtk3i_t5iSyxpdMcqMoLFhKf8OA&sig2=dnZxP1kl2pav20u7wloNSw pdf]


Examples of computational brain models are abstract neural networks. They describe the dynamics of neural and synaptic states and relate the dynamics to computation, neural states represent computational operands and synaptic states correspond to computating operations. Abstract neural networks provide "skeletons" of a brain theory but they are crude models of the actual biophysics of the brain. This shortcoming of abstract neural networks illustrates a dilemma of computational models of the brain, the dilemma between simplicity and richness. In order to rule out beforehand as little as possible, a computational brain model should reflect the experimental findings as detailed as possible. On the other hand a computational theory of a behavior is basically an algorithm and the cleanest form to instantiate such a hypothesis is by the simplest neural network that can do the job efficiently and is not incompatible with neurobiology (following Occam's razor principle).
C. Hillar, F. T. Sommer: Ramsey theory reveals the conditions when sparse coding on subsampled data is unique. [https://arxiv.org/abs/1106.3616v1 arXiv]  (2010)


==== Models of associative memory ====
X. Wang, J. A. Hirsch, F. T. Sommer: Recoding of sensory information across the retinothalamic synapse. [http://www.jneurosci.org/content/30/41/13567 Journal of Neuroscience] 30: 13567-13577 [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3842493/  PubMed pdf] (2010)
Obviously, no single computational brain model can escape the richness/simplicity dilemma. The way I study associative memory function of the brain is to investigate chains of models that vary in the faithfulness of the biophysical description. The starting point of the chain is an abstract neural network model corresponding to the hypothetical function. Features can be added to the abstract model step by step, reflecting neurobiological features. Thus the computational function can be first analyzed in the abstract model. The predictions of biophysical brain properties arising from the functional hypothesis can be assessed in the more detailed models. Qualitative changes in the model behavior induced by certain model features can be easily traced in the chain of models.  


Neuronal  associative memories are abstract neural networks that implement the basic mechanisms of learning and association as postulated in Hebb's theory (Hebb 1949, Hayek 1954, see also James 1892). Neural associative memories have been proposed as computational models for local strongly connected cortical circuits (Palm 1982, Hopfield 1982, Amit 1989). The computational function is the storage and error-tolerant recall of distributed activity patternsThe memory recall is called associative pattern completion if it involves the completion of a noisy pattern according involving memory. Another recall variant possible in associative memories is pattern recognition (Palm & Sommer 1992) where inputs are just classified as "known" or "unknown".
K. Koepsell, X. Wang, J. A. Hirsch, F. T. Sommer: Exploring the function of neural oscillations in early sensory systems. Focused review in [http://frontiersin.org/neuroscience/neuroscience/paper/10.3389/neuro.01/010.2010/ Frontiers in Neuroscience] 4: 53-61 (2010)


A variety of different abstract models of associative memory has been proposed in the literature that
A. Knoblauch, G. Palm, F. T. Sommer: Memory capacities for synaptic and structural plasticity. [http://www.mitpressjournals.org/doi/abs/10.1162/neco.2009.08-07-588?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed Neural Computation] 22 (2010) 289-341 [http://rctn.org/fsommer/papers/knoblauchpalmsommer10.pdf pdf]
could all serve as starting point in a chain of computational models for memory in the brain.  
My choice of an abstract model of associative memory relies on the observation that nature often finds
efficient solutions.


* How to measure the efficiency of associative memories?
G. Monaci, P. Vandergheynst, F. T. Sommer: Learning bimodal structure in audio-visual data. [https://pdfs.semanticscholar.org/f4e6/71d0644ad0a468e7b7f7e0e0b653d5b2c38e.pdf IEEE Transactions on Neural Networks] 20 (2009) 1898-1910 [http://rctn.org/fsommer/papers/Monaci_etal_IEEE_NN09.pdf pdf]


Information capacity, that is, the amount of information that can be stored, has become the standard measure for the efficiency of associative memories. However, the traditional measures of capacity do not take into account all relevant flows of information during learning and retrieval. In particular, they neglect the loss due to retrieval errors as well as the information contained in the noisy patterns during pattern completion tasks. For definitions of information capacity that take into account all these factors see (Sommer 1993, Palm & Sommer 1996).
K. Koepsell, X. Wang, V. Vaingankar, Y. Wei, Q. Wang, D. L. Rathbun, W. M. Usrey, J. A. Hirsch, F. T. Sommer: Retinal oscillations carry visual information to cortex. [http://www.frontiersin.org/systemsneuroscience/paper/10.3389/neuro.06/004.2009/ Frontiers in Systems Neuroscience] (2009)


* Should memory representations be dense or sparse? 
K. Koepsell, F. T. Sommer: Information transmission in oscillatory neural activity. [https://link.springer.com/article/10.1007/s00422-008-0273-6 Biological Cybernetics] 99 (2008) 403-416 [http://rctn.org/fsommer/papers/koepsellsommer08bicy.pdf pdf]


An argument for sparse memory representations in the brain follows from the analysis of synaptic learning rules in associative memory. The learning rules that are particularly relevant for the brain are local learning rules, that is, rules of synaptic plasticity that only depend on pre- and post-synaptic activity.
J. L. Teeters, K. D. Harris, K. J. Millman, B. A. Olshausen, F. T. Sommer: Data sharing for computational neuroscience. [https://link.springer.com/article/10.1007/s12021-008-9009-y Neuroinformatics] 6 (2008) 47-55 [http://rctn.org/fsommer/papers/teetersetal08.pdf  pdf]
Elisabeth Gardner (1988) found that local learning rules store sparse memory patterns more efficiently than nonsparse patterns and that for sparse patterns local learning cannot be outperformed by nonlocal learning. Thus, sparse memory representations arise from the optimal use of local synaptic learning, a property of synaptic plasticity well confirmed in physiological studies. Gardner's analysis allows this deep fundamental insight, however, it is not constructive, for instance, it only takes into account the learning process and not the recall process. Thus, the question remained:


* What concrete associative memory models process sparse memory representations efficiently?  
X. Wang, Y. Wei, V. Vaingankar, Q. Wang, K. Koepsell, F. T. Sommer, J. A. Hirsch: Feedforward excitation and inhibition evoke dual modes of firing in the cat’s visual thalamus during naturalistic viewing. [http://www.cell.com/neuron/fulltext/S0896-6273(07)00496-5 Neuron] 55 (2007) 465-478. [http://rctn.org/fsommer/papers/wangetal07neuron.pdf  pdf] See also the preview about this paper: P. Reinagel: The inner life of bursts. Neuron 55 (2007) 339-341


A general analysis of local learning rules -assessing the capacity of storage and retrieval in a pattern association task- is described in (Sommer 1993; Palm & Sommer 1996). For sparse memory patterns, the analysis characterizes the class of efficient local learning rules. How different superposition schemes for memory traces (in particular, linear superposition as in the Hopfield model and clipped superposition as in the Willshaw model) compare in terms of efficiency in sparse pattern recognition is analyzed in (Palm & Sommer 1992; Sommer 1993).
F. T. Sommer: Bunte Theorien für graue Zellen. [http://www.gehirn-und-geist.de/artikel/872207 Gehirn und Geist], Juni (2007) 70-76


The analyses of sparse associative memory indicate that the classical Willshaw-Steinbuch model (Steinbuch, 1961; Willshaw et al, 1969) is among the most efficient models. However, (Palm & Sommer 1992; Sommer 1993) show for this model that the learning provides a higher capacity than the retrieval, i.e., the retrieval in the original model is an information bottleneck. This result raises the question whether the Willshaw model can be improved by modified retrieval.
M. Rehn, F. T. Sommer: A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields. [https://link.springer.com/article/10.1007/s10827-006-0003-9 Journal of Computational Neuroscience] 22 (2) 135-146 (2007). [http://rctn.org/fsommer/papers/rehnsommer07jcns.pdf pdf]


The autoassociative Willshaw model with iterative retrieval was analyzed in (Sommer 1993; Schwenker, Sommer and Palm 1996). It is shown that the modified retrieval retains the asymptotic information capacity of the original model. However, for (large) finite-sized networks  iterative retrieval has the following advantages: 1) A significant increase in recall precision. 2) The asymptotic capacity value can be reached in networks of already moderate sizes -- the original model does not reach asymptotic performance at practical network sizes. 3) Iterative retrieval is fast. The typical number of required iteration steps is low (<4).
M. Rehn, F. T. Sommer: Storing and restoring visual input with collaborative rank coding and associative memory.
[http://www.sciencedirect.com/science/article/pii/S0925231205004066 Neurocomputing] 69 (10-12) (2006) 1219-1223 [http://rctn.org/fsommer/papers/rehnsommer06neurocomp.pdf  pdf]


In bidirectional associative memories (Kosko 1988) with sparse patterns, naive iterative retrieval does not provide the same improvement as for autoassociation. (Sommer & Palm 1998, Sommer & Palm 1999) explain why and suggest a novel and very efficient iterative retrieval in bidirectional associative memories, called crosswise bidirectional retrieval (see also below).
F. T. Sommer, P. Kanerva: Can neural models of cognition benefit from the advantages of connectionism?
Behavoral and Brain Sciences 29 (1) 86-87 (2006) [http://rctn.org/fsommer/papers/sommerkanerva05.pdf  pdf]
F. T. Sommer, T. Wennekers: Synfire chains with conductance-based neurons: internal timing and coordination with timed input.
[http://www.sciencedirect.com/science/article/pii/S092523120400400X Neurocomputing] 65-66 (2005) 449 - 454  [http://rctn.org/fsommer/papers/sommerwennekers05.pdf  pdf]


Having identified efficient instances of sparse associative memory models these can be used in models of neuronal circuits of the brain.  
D. George, F. T. Sommer: Computing with inter-spike  inverval codes in networks of integrate and fire neurons.
[http://www.sciencedirect.com/science/article/pii/S0925231204004230 Neurocomputing]  65-66 (2005) 414 - 420 [http://rctn.org/fsommer/papers/georgesommer05.pdf  pdf]


* What are the properties of cell assemblies?
L. M. Martinez, Q. Wang,  R. C. Reid, C. Pillai, J.-M. Alonso, F. T. Sommer, J. A. Hirsch: Receptive field structure varies with layer in the primary visual cortex.
[http://www.nature.com/neuro/journal/v8/n3/full/nn1404.html Nature Neuroscience] 8 (12) (2005) 372 - 379 [http://rctn.org/fsommer/papers/martinezetal05.pdf  pdf]


If Hebb's theory were true and brain function would be based on cell assemblies, what would their properties be, i.e., how many cells do typically form an assembly and how many assemblies "fit" in a local circuit of cortical tissue?  (Sommer 2000analyzes a model of a square millimeter of cortex (number of neurons and connection densities were taken from neuroanatomical studies, cell excitability was estimated based on physiological studies). The study reveals that the local synapses are used most efficiently if the size of the assemblies is a few hundred cells and the number of assemblies is in the range between ten and sixty thousand. Due to the incomplete connectivity in the network there arises an interesting extension in functionality: A small set of assemblies (~5) can be recalled simultaneously and not just a single one as in classical associative memories.
A. Knoblauch, F. T. Sommer: Spike-timing dependent plasticity can form "zero-lag" links for cortical oscillations.
[http://www.sciencedirect.com/science/article/pii/S0925231204000372 Neurocomputing] 52-54 (2004) 301 - 306 [http://rctn.org/fsommer/papers/KnoblauchSommer04.pdf  pdf]


* How is associative memory reflected in temporal structure of neural activity?
G. Glatting, F. M. Mottaghy, J. Karitzky, A. Baune, F. T. Sommer, G. B. Landwehrmeyer, S. N. Reske: Improving binding potential analysis in [11C]raclopide PET studies using cluster analysis.
[http://onlinelibrary.wiley.com/doi/10.1118/1.1668392/abstract;jsessionid=0883CB4A1FA57FB25969CE9AEEEB9C8D.f03t03 Medical Physics] 31 (4) (2004) 902-906 [http://rctn.org/fsommer/papers/glattingetal04.pdf  pdf]


Simulation studies with associative networks of conduction-based spiking neurons (two-compartment neurons a la Pinsky & Rinzel, 1994) are described in (Sommer & Wennekers 2000, Sommer & Wennekers 2001). It is revealed that associative memory recall can be completed extremely fast, that is, in 25-60ms. Gamma-oscillations can indicate iterative recall (that reaches higher retrieval precision) with latencies of 60-260ms.
J. A. Hirsch, L. M. Martinez, C. Pillai, J.-M. Alonso, Q. Wang, F. T. Sommer: Functionally distinct inhibitory neurons at the first stage of visual cortical processing.
[http://www.nature.com/neuro/journal/v6/n12/full/nn1152.html Nature Neuroscience] 6 (12) (2003) 1300 - 1308 [http://rctn.org/fsommer/papers/hirschetal03.pdf  pdf]


=== Organization of meso- and macroscopic activity patterns in the brain ===
F. T. Sommer, T. Wennekers: Models of distributed associative memory networks in the brain.
[http://www.sciencedirect.com/science/article/pii/S1431761304700749 Theory in Biosciences] (122) (2003) 70 - 86  [http://rctn.org/fsommer/papers/sommerwennekers03.pdf  pdf]
A. Knoblauch, F. T. Sommer: Synaptic plasticity, conduction delays, and inter-areal phase relations of spike activity in a model of reciprocally connected areas.
[http://www.sciencedirect.com/science/article/pii/S0925231202007920 Neurocomputing] (52-54) (2003) 301-306 [http://redwood.berkeley.edu/fsommer/papers/knoblauchsommer03.pdf  pdf]


While neural network models described in the previous section help understanding computations of local brain circuits, cognitive functions ultimately rely on the meso- and macroscopic organization of neural activity in the brain. The studies in this section address how macroscopic activity flow can establish cooperative interactions even between remote brain regions.
== Workshops, Conferences, Seminar Series and Teaching Courses ==
'''Workshop and conference organization:'''


* Can large cell assemblies be integrated by cortico-cortical projections?
* [https://simons.berkeley.edu/workshops/brain2018-2 Targeted Discovery in Brain Data], co-organizer, Brain and Computation program 2018 at the  Simon Center for the Theory of Computing, UC Berkeley
* Open Data Ecosystem for the Neurosciences. Ronald Reagan Building and International Trade Center, Washington, DC on July 25-26, 2016, co-organizer
* [http://nips.cc/Conferences/2013/Program/event.php?ID=3699 NIPS Workshop: High-dimensional Statistical Inference in the Brain], Lake Tahoe, 2013, co-organizer
* [http://www.neuroinf.org/pipermail/comp-neuro/2010-August/002253.html Workshop on Perception and Action], Santa Fe Institute, 2010, co-organizer, see [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3083716/ article about this workshop]
* [http://www.cosyne.org/c/index.php?title=Cosyne_09 COSYNE 2009 Program], member of program committee
* [http://www.cosyne.org/c/index.php?title=Cosyne_08_workshops COSYNE 2008 Workshops], workshop chair
* [http://www.cosyne.org/c/index.php?title=Cosyne_07_Workshops/submissions COSYNE 2007 Workshops], workshop chair
* [http://nips.cc/Conferences/2005/ NIPS 2005 Program], program chair for Neuroscience
* [http://www.gatsby.ucl.ac.uk/~maneesh/cns2002workshops/neural_assemblies.html CNS 2002 Workshop: Neural assemblies], co-organizer
* NIPS 2000 Workshop: Exploratory analysis and data modeling in functional neuroimaging, organizer


Reciprocal connectivity is the most common type of cortico-cortical projections reported by neuroanatomical tracer studies. Thus it is likely that reciprocal connections play an important role in large-scale integration of neural representations or cell assemblies. (Sommer & Wennekers 2003) lay out how bidirectional association in reciprocal projections could provide such an integration and how this ties into earlier work about distributed representations, such as the theories of Wickelgren, Edelman, Damasio, Mesulam and others.
'''Seminar series organization:'''


Macroscopically distributed cell assemblies would easily form, if already a single reciprocal connection would express associative memory function. In (Sommer & Wennekers, 2000) a bidirectional associative memory model with conductance-based neurons is investigated that, in fact, performs efficiently.  A more abstract model that is very robust with respect to cross talk --and therefore might be a good computational model of a cortico-cortical projection-- is proposed in (Sommer & Palm 1998, Sommer, Wennekers & Palm 1998, Sommer & Palm1999).
* [http://redwood.berkeley.edu/all_seminars.php Redwood Seminar Series], Talks at the Redwood Center, UC Berkeley, 2005-present (most talks you can watch online via the link), co-organizer


* What causes coherent oscillations in distant brain regions? Does it require learning?
'''Teaching at UC Berkeley:'''


In recordings of neuronal activity, coherent oscillations mostly occur in phase, even if the recording sites in cortex are far apart of each other. For fast (gamma range) oscillations this finding is puzzling given the large delay times reported in long-range projections. Modeling studies using reciprocal excitatory couplings with such delay times predict anti-phase rather than in-phase correlation. In  (Knoblauch & Sommer 2002, Knoblauch & Sommer 2003) the conditions are studied under which reciprocal cortical connections with realistic delays can express coherent gamma oscillations. It is demonstrated that learning based on spike-timing dependent synaptic plasticity (Markram et al. 1997, Poo et al. 1998) can provide robust zero lag coherence over long-range projections -- zero-lag links.
International summer courses
* [https://crcns.org/course Modeling and Mining of Neuroscience Data], UC Berkeley 2013-present (paused in 2020/2021), organizer, moderator


* How can macroscopic activity patterns form through cortico-cortical connections?
Semester courses
* Theoretical and computational neuroscience (2003, 2005; 2007; 2009; 2010; teaching participation at course MCB262/PSYCH290P/[http://redwood.berkeley.edu/wiki/VS265:_Neural_Computation VS265]) 
* Neural Computation (2006; teaching participation at course [[VS298]])


Neuronography experiments (MCulloch et al, Pribham et al) revealed that epileptiform activity elicited by local application of strychnine entails persistent patterns of activity involving the activity of many brain areas. (Sommer & Koetter 1997, Koetter & Sommer 2000) investigates in a computer model the relation between the anatomy of cortico-cortical  projections and the expression of persistent macroscopic activity patterns. In the model the connection weights between brain areas can be either simple cortex connectivity schemes such as nearest neighbor connections or data about cortico-cortical projections gathered by neuroanatomical tracer studies and  collatedin the CoCoMac database. The comparison between different connectivity schemes shows that neuroanatomical data can best explain the measured activity patterns. It is concluded that long-range connections are crucial in the formation of patterns that have been observed experimentally. Furthermore, the simulations indicate multisynaptic reverberating activity propagation and clearly rule out the hypothesis that just monosynaptic spread would produce the patterns -- as was speculated in the experimental literature. (V. Schmitt et al 2003) investigates the influence of thalamocortical connections in a similar model. 
'''Teaching at University of Ulm:'''


* How to reveal organization of neuronal activity by Neuroimaging?
Summer course:
* Statistics of natural signals (2005)


Imaging methods like positron emission tomography (PET) and functional magnetic resonance (fMRI) provide the first (albeit indirect) windows to macroscopic activation patterns in the working brain. The spatio-temporal data sets provided by this methods are usually searched for functional activity using regression analysis based on temporal shapes that are estimated based on the timing in the experimental paradigm. However, in short-lasting events and in most cognitive tasks the temporal shape cannot be reliably predicted. In these cases the detection of functional activity requires analysis methods based on weaker assumptions about the signal course. (Baune et al. 1999) describes a new cluster analysis method for detecting regions of fMRI activation. The method requires no information about the time course of the activation and is applied to detect timing differences in the activation of supplementary motor cortex and motor cortex during a voluntary movement task.
Semester courses (1997-2002):
* Information retrieval and associative memory
* Computational Neuroscience
* Theoretical methods for the interpretation of medical functional imaging data
* Information Retrieval
* Associative memories: conventional and neuronal
* Neural Cell Assemblies


(Wichert et al. 2003) describes the extension of the method of Baune et al. for event-related designs. A new method of experimental design/data processing is proposed that yields volumes of data where all slices are perfectly timed. This avoids the artifacts introduced by usual data preprocessing methods based on phase-shifting. In (Wichert et al. 2003) the exploratory method is applied to reveal functional activity during a n-back working memory task.
==Earlier Edited Book and Software for Neuroimaging==


In (Baune et al., 2001, Ruckgaber et al.., 2001) a cluster analysis method was developed to detect microgilia activation which is a very sensitive indicator for brain lesions.
Eds: F. T. Sommer, A. Wichert: Exploratory analysis and data modeling in functional neuroimaging.
Mathematical analysis of associative memories 
MIT Press, Boston, MA (2003)  


* Bayesian theory of  associative memory
Tool for analysis of functional neuroimaging: The brain positioning software (BPS) gives access to the Brodmann atlas and can be called directly from the SPM data analysis software. For description, see: Schmitt V., Wichert A., Grothe J., Sommer F. T.: [http://link.springer.com/chapter/10.1007%2F978-1-4615-1079-6_17 The brain positioning software. A Practical guide of neuroscience databases.] Ed.: R. Kötter, Kluwer, NY, 2002


An attempt to tame the zoo of associative memory models proposed in the literature is the Bayesian theory of associative memory described in (Sommer & Dayan 1998). In this theory the optimal retrieval dynamics can be derived from the uncertainties about the input pattern and the synaptic weights. Our analysis explains the success of many model modifications proposed on heuristic basics, for instance, addition of a ferromagnetic term, of site-dependent thresholds, diagonal terms, various threshold strategies, etc.
== Earlier Neuroscience Publications by Theme ==


* Combinatorial analysis of the Willshaw model
For a brief description of some of the research projects, see [[Synopsis of Research|synopsis of research]].


The full combinatorial analysis of the finite Willshaw model can be found in (Sommer & Palm 1999). It predicts distributions of the dendritic potentials and retrieval errors for arbitrary network sizes and all possible types of input noise.
=== Computational models and analysis of cell physiology in the early visual system===
X. Wang, V. Vaingankar, C. Soto Sanchez, F. T. Sommer, J. A. Hirsch: Thalamic interneurons and relay cells use complementary synaptic mechanisms for visual processing. Nature Neuroscience 14 (2011) 224-231


* Signal-to-noise analysis of local synaptic learning rules
X. Wang, J. A. Hirsch, F. T. Sommer: Recoding of sensory information across the retinothalamic synapse. Journal of Neuroscience 30 (2010) 13567-13577


A general signal-to-noise analysis of local learning rules is given in (Sommer 1993; Palm & Sommer 1996). The final result is basically one formula, equation (3.23) in (Palm & Sommer 1996) calculating the S/N for arbitrary learning rules, sparseness levels and input errors. These papers also contain the full information-theoretical treatment of learning and retrieval in associative memories that lead to new definitions of information capacity.
X. Wang, Y. Wei, V. Vaingankar, Q. Wang, K. Koepsell, F. T. Sommer, J. A. Hirsch: Feedforward excitation and inhibition evoke dual modes of firing in the cat’s visual thalamus during naturalistic viewing. Neuron 55 (2007) 465-478. See also the preview about this paper: P. Reinagel: The inner life of bursts. Neuron 55 (2007) 339-341


* Asymptotic analysis of sparse Hopfield- and Willshaw-models performing pattern recognition
M. Rehn, F. T. Sommer: A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields. J. Comp. Neurosci. 22 (2) 135-146 (2007). [http://rctn.org/fsommer/papers/rehnsommer07jcns.pdf pdf]
Earlier online prepublication [http://dx.doi.org/10.1007/s10827-006-0003-9 SpringerLink DOI 10.1007/s10827-006-003-9] (2006)


The asymptotic analysis of the sparse Hopfield and Willshaw model is provided in (Palm & Sommer 1992). We use elementary analysis information theory and can avoid the cumbersome Replica trick used in the earlier analysis of the Hopfield model (Tsodyks & Feigelman, 1988).
M. Rehn, F. T. Sommer: Storing and restoring visual input with collaborative rank coding and associative memory.
Neurocomputing 69 (10-12) (2006) 1219-1223 [http://rctn.org/fsommer/papers/rehnsommer06neurocomp.pdf  pdf]


== Selected Publications by Theme ==
L. M. Martinez, Q. Wang,  R. C. Reid, C. Pillai, J.-M. Alonso, F. T. Sommer, J. A. Hirsch: Receptive field structure varies with layer in the primary visual cortex.
Nature Neuroscience 8 (12) (2005) 372 - 379 [http://rctn.org/fsommer/papers/martinezetal05.pdf  pdf]


(for a complete listing, see [[Media:fsommer_cv.pdf|cv]])
J. A. Hirsch, L. M. Martinez, C. Pillai, J.-M. Alonso, Q. Wang, F. T. Sommer: Functionally distinct inhibitory neurons at the first stage of visual cortical processing.
Nature Neuroscience 6 (12) (2003) 1300 - 1308 [http://rctn.org/fsommer/papers/hirschetal03.pdf   pdf]


=== Mechanisms of memory in realistic neural networks ===
=== Mechanisms of memory in realistic neural networks ===
'''Structural plasticity'''
A. Knoblauch, E. Koerner, U. Koerner, F. T. Sommer: Structural synaptic plasticity has high memory capacity and can explain graded amnesia, catastrophic forgetting, and the spacing effect. to appear in PLOS ONE (2014)
A. Knoblauch, G. Palm, F. T. Sommer: Memory capacities for synaptic and structural plasticity. Neural Computation 22 (2010) 289-341 [http://rctn.org/fsommer/papers/knoblauchpalmsommer10.pdf pdf]


'''Long-term memory'''
'''Long-term memory'''


F. T. Sommer, T. Wennekers : Associative memory in networks of spiking neurons
F. T. Sommer, T. Wennekers: Associative memory in networks of spiking neurons
Neural Networks 14 (6-7) Special Issue: Spiking Neurons in Neuroscience and Technology (2001) 825 - 834 [http://redwood.berkeley.edu/~fsommer/papers/sommerwennekers01.pdf  pdf]   
Neural Networks 14 (6-7) Special Issue: Spiking Neurons in Neuroscience and Technology (2001) 825 - 834 [http://rctn.org/fsommer/papers/sommerwennekers01.pdf  pdf]   
   
   
F. T. Sommer, T. Wennekers: Modeling studies on the computational function of fast temporal structure in cortical circuit activity
F. T. Sommer, T. Wennekers: Modeling studies on the computational function of fast temporal structure in cortical circuit activity
Journal of Physiology - Paris 94 (5/6) (2000) 473-488 [http://redwood.berkeley.edu/~fsommer/papers/sommerwennekers00.pdf  pdf]   
Journal of Physiology - Paris 94 (5/6) (2000) 473-488 [http://rctn.org/fsommer/papers/sommerwennekers00.pdf  pdf]   
      
      
F. T. Sommer: On cell assemblies in a cortical column
F. T. Sommer: On cell assemblies in a cortical column
Neurocomputing (32-33) (2000) 517 - 522 [http://redwood.berkeley.edu/~fsommer/papers/sommer00.pdf  pdf]   
Neurocomputing (32-33) (2000) 517 - 522 [http://rctn.org/fsommer/papers/sommer00.pdf  pdf]   


T. Wennekers, F. T. Sommer: Gamma-oscillations support optimal retrieval in associative memories of two-compartment neurons
T. Wennekers, F. T. Sommer: Gamma-oscillations support optimal retrieval in associative memories of two-compartment neurons
Neurocomputing 26-27 (1999) 573 - 578 [http://redwood.berkeley.edu/~fsommer/papers/ws99.pdf  pdf]   
Neurocomputing 26-27 (1999) 573 - 578 [http://rctn.org/fsommer/papers/ws99.pdf  pdf]   


T.Wennekers, F.T.Sommer, G.Palm: Iterative Retrieval in Associative Memories by Threshold Control of Different Neural Models
T. Wennekers, F. T. Sommer, G. Palm: Iterative Retrieval in Associative Memories by Threshold Control of Different Neural Models
In: Supercomputers in Brain Research: From Tomography to Neural Networks World Scientific Publishing Comp (1995) 301-319
In: Supercomputers in Brain Research: From Tomography to Neural Networks World Scientific Publishing Comp (1995) 301-319


Line 176: Line 280:


A. Knoblauch, T. Wennekers, F. T. Sommer: Is voltage dependent synaptic transmission in NMDA receptors a robust mechanism for working memory?
A. Knoblauch, T. Wennekers, F. T. Sommer: Is voltage dependent synaptic transmission in NMDA receptors a robust mechanism for working memory?
Neurocomputing (44-46) (2002) 19-24 [http://redwood.berkeley.edu/~fsommer/papers/kws02.pdf  pdf]   
Neurocomputing (44-46) (2002) 19-24 [http://rctn.org/fsommer/papers/kws02.pdf  pdf]   


U. Vollmer, F. T. Sommer: Coexistence of short and long term memory in a model network of realistic neurons
U. Vollmer, F. T. Sommer: Coexistence of short and long term memory in a model network of realistic neurons
Neurocomputing (38-40) (2001) 1031 - 1036 [http://redwood.berkeley.edu/~fsommer/papers/vs01.pdf  pdf] 
Neurocomputing (38-40) (2001) 1031 - 1036 [http://rctn.org/fsommer/papers/vs01.pdf  pdf]   
 
'''Physiology and information processing in early vision'''
 
J. A. Hirsch, L. M. Martinez, C. Pillai, J.-M. Alonso, Q. Wang, F. T. Sommer: Functionally distinct inhibitory neurons at the first stage of visual cortical processing.
Nature Neuroscience 6 (12) (2003) 1300 - 1308 [http://redwood.berkeley.edu/~fsommer/papers/hirschetal03.pdf  pdf]   
   
   
'''Large-scale integration of cortical representations'''
'''Large-scale integration of cortical representations'''


F. T. Sommer, T. Wennekers : Models of distributed associative memory networks in the brain
F. T. Sommer, T. Wennekers: Models of distributed associative memory networks in the brain
Theory in Biosciences (122) (2003) 70 - 86 [http://redwood.berkeley.edu/~fsommer/papers/sommerwennekers03.pdf  pdf]   
Theory in Biosciences (122) (2003) 70 - 86 [http://rctn.org/fsommer/papers/sommerwennekers03.pdf  pdf]   


Associative memory in reciprocal cortico-cortical projections
Associative memory in reciprocal cortico-cortical projections


F. T. Sommer, T. Wennekers : Associative memory in a pair of cortical cell groups with reciprocal projections
F. T. Sommer, T. Wennekers: Associative memory in a pair of cortical cell groups with reciprocal projections
Neurocomputing (38-40) (2001) 1575 - 1580 [http://redwood.berkeley.edu/~fsommer/papers/sw01.pdf  pdf]   
Neurocomputing (38-40) (2001) 1575 - 1580 [http://rctn.org/fsommer/papers/sw01.pdf  pdf]   


F. T. Sommer, T. Wennekers, G. Palm: Bidirectional completion of cell assemblies in the cortex
F. T. Sommer, T. Wennekers, G. Palm: Bidirectional completion of cell assemblies in the cortex
Computational Neuroscience: Trends in Research 1998, Plenum Press, New York, (1998) [http://redwood.berkeley.edu/~fsommer/papers/cns97swp.ps  ps]   
Computational Neuroscience: Trends in Research 1998, Plenum Press, New York, (1998) [http://rctn.org/fsommer/papers/cns97swp.ps  ps]   


'''Large-scale integration relying on oscillations'''
'''Large-scale integration relying on neural oscillations'''


A. Knoblauch, F. T. Sommer: Spike-timing dependent plasticity can form "zero-lag" links for cortical oscillations Neurocomputing (58-60) 185 - 190 (2004) [http://redwood.berkeley.edu/~fsommer/papers/KnoblauchSommer04.pdf  pdf]   
A. Knoblauch, F. T. Sommer: Spike-timing dependent plasticity can form "zero-lag" links for cortical oscillations Neurocomputing (58-60) 185 - 190 (2004) [http://rctn.org/fsommer/papers/KnoblauchSommer04.pdf  pdf]   


A. Knoblauch, F. T. Sommer: Synaptic plasticity, conduction delays, and inter-areal phase relations of spike activity in a model of reciprocally connected areas
A. Knoblauch, F. T. Sommer: Synaptic plasticity, conduction delays, and inter-areal phase relations of spike activity in a model of reciprocally connected areas
Neurocomputing (52-54) (2003) 301-306 [http://redwood.berkeley.edu/~fsommer/papers/knoblauchsommer03.pdf  pdf]
Neurocomputing (52-54) (2003) 301-306 [http://rctn.org/fsommer/papers/knoblauchsommer03.pdf  pdf]


=== Large-scale models of cortical activity spread ===
=== Models of macroscopic activity spread in cortex ===


V. Schmitt, R. Koetter, F. T. Sommer: The impact of thalamo-cortical projections on activity spread in cortex
V. Schmitt, R. Koetter, F. T. Sommer: The impact of thalamo-cortical projections on activity spread in cortex
Neurocomputing  (2003) (52-54) (2003) 919-924 [http://redwood.berkeley.edu/~fsommer/papers/schmittetal03.pdf  pdf]
Neurocomputing  (2003) (52-54) (2003) 919-924 [http://rctn.org/fsommer/papers/schmittetal03.pdf  pdf]
   
   
R. Kötter  and F. T. Sommer: Global relationship between anatomical connectivity and activity propagation in the cerebral cortex
R. Kötter  and F. T. Sommer: Global relationship between anatomical connectivity and activity propagation in the cerebral cortex
Phil. Trans. R. Soc. Lond. B (355) (2000) 127 - 134 [http://redwood.berkeley.edu/~fsommer/papers/KoetterSommer2000.pdf  pdf]
Phil. Trans. R. Soc. Lond. B (355) (2000) 127 - 134 [http://rctn.org/fsommer/papers/KoetterSommer2000.pdf  pdf]


F.T.Sommer, R. Kötter: Simulating a Network of Cortical Areas Using Anatomical Connection Data in the Cat
F. T. Sommer, R. Kötter: Simulating a Network of Cortical Areas Using Anatomical Connection Data in the Cat
Computational Neuroscience: Trends in Research 1997, Plenum Press, New York (1997) 511-517 [http://redwood.berkeley.edu/~fsommer/papers/cns96lv.ps  ps]
Computational Neuroscience: Trends in Research 1997, Plenum Press, New York (1997) 511-517 [http://rctn.org/fsommer/papers/cns96lv.ps  ps]


R. Koetter, P. Nielsen, J. Dyhrfjeld, F. T. Sommer, G. Northoff: Multi-level integration of quantitative neuroanatimical data
R. Koetter, P. Nielsen, J. Dyhrfjeld, F. T. Sommer, G. Northoff: Multi-level integration of quantitative neuroanatimical data
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Ed.: G. A. Ascoli, Humana Press Inc., Totowa, NJ (2002)
Ed.: G. A. Ascoli, Humana Press Inc., Totowa, NJ (2002)


=== Theory of sparse associative memory ===
=== Theory of associative memory ===
 
'''Review on capacity analysis of memory networks'''
 
A. Knoblauch, G. Palm, F. T. Sommer: Memory capacities for synaptic and structural plasticity. Neural Computation, Volume 22, Issue 2, pp. 289-341 (2010) [http://rctn.org/fsommer/papers/knoblauchpalmsommer10.pdf pdf]


'''Bayesian theory of autoassociative memory'''
'''Bayesian theory of autoassociative memory'''


F. T. Sommer, P. Dayan: Bayesian Retrieval in Associative Memories with Storage Errors
F. T. Sommer, P. Dayan: Bayesian Retrieval in Associative Memories with Storage Errors.
IEEE Transactions on Neural Networks 9 (4) (1998) 705-713 [http://redwood.berkeley.edu/~fsommer/papers/sommerdayan98.pdf  pdf]
IEEE Transactions on Neural Networks 9 (4) (1998) 705-713 [http://rctn.org/fsommer/papers/sommerdayan98.pdf  pdf]


'''Bidirectional sparse associative memory'''
'''Bidirectional sparse associative memory'''


F. T. Sommer, G. Palm: Improved Bidirectional Retrieval of Sparse Patterns Stored by Hebbian Learning
F. T. Sommer, G. Palm: Improved Bidirectional Retrieval of Sparse Patterns Stored by Hebbian Learning.
Neural Networks 12 (2) (1999) 281 - 297 [http://redwood.berkeley.edu/~fsommer/papers/sommerpalm99.pdf  pdf]
Neural Networks 12 (2) (1999) 281 - 297 [http://rctn.org/fsommer/papers/sommerpalm99.pdf  pdf]


F. T. Sommer, G. Palm: Bidirectional Retrieval from Associative Memory
F. T. Sommer, G. Palm: Bidirectional Retrieval from Associative Memory.
Advances in Neural Information Processing Systems 10, MIT Press, Cambridge, MA (1998) 675 - 681 [http://redwood.berkeley.edu/~fsommer/papers/sommerpalm98nips.PDF  pdf]
Advances in Neural Information Processing Systems 10, MIT Press, Cambridge, MA (1998) 675 - 681 [http://rctn.org/fsommer/papers/sommerpalm98nips.PDF  pdf]
      
      
'''Analysis of recurrent sparse autoassociative memories'''
'''Capacity analysis of recurrent sparse autoassociative memories'''


F.Schwenker, F.T.Sommer, G.Palm: Iterative Retrieval of sparsely coded associative memory patterns
F. Schwenker, F. T. Sommer, G. Palm: Iterative Retrieval of sparsely coded associative memory patterns.
Neural Networks 9 (1996) 445-455 [http://redwood.berkeley.edu/~fsommer/papers/schwenkeretal95.pdf  pdf]
Neural Networks 9 (1996) 445-455 [http://rctn.org/fsommer/papers/schwenkeretal95.pdf  pdf]


'''Analysis of sparse pattern recognition'''
'''Capacity analysis of sparse pattern recognition'''


G.Palm, F.T.Sommer: Information capacity in recurrent Mc.Culloch-Pitts networks with sparsely coded memory states
G. Palm, F. T. Sommer: Information capacity in recurrent Mc.Culloch-Pitts networks with sparsely coded memory states.
Network 3 (1992) 177-186 [http://redwood.berkeley.edu/~fsommer/papers/palmsommer92.pdf  pdf]   
Network 3 (1992) 177-186 [http://rctn.org/fsommer/papers/palmsommer92.pdf  pdf]   
   
   
G.Palm, F.T.Sommer: Information and pattern capacities in neural associative memories with feedback for sparse memory patterns
G. Palm, F. T. Sommer: Information and pattern capacities in neural associative memories with feedback for sparse memory patterns.
In: Neural Network Dynamics, Springer New York (1992). Eds.: J.G.Taylor, E.R.Caianello, R.M.J.Cotterill, J.W.Clark, 3-18  
In: Neural Network Dynamics, Springer New York (1992). Eds.: J.G.Taylor, E.R.Caianello, R.M.J.Cotterill, J.W.Clark, 3-18  
      
      
'''Analysis of local learning  rules'''
'''Analysis of local learning  rules'''


G.Palm, F.T.Sommer: Associative data Storage and Retrieval in Neural Nets
G. Palm, F. T. Sommer: Associative data Storage and Retrieval in Neural Nets.
In: Models of Neural Networks III, Springer New York (1996) Eds: E.Domany, J.L.van Hemmen, K.Schulten, 79-118  
In: Models of Neural Networks III, Springer New York (1996) Eds: E.Domany, J.L.van Hemmen, K.Schulten, 79-118    
[http://redwood.berkeley.edu/~fsommer/papers/palmsommer95.ps  ps]
[http://rctn.org/wiki/Image:Palmssommer95.pdf pdf]


Book, PhD-Thesis (in german)
Book, PhD-Thesis (in german)


F. T. Sommer: Theorie neuronaler Assoziativspeicher - Lokales Lernen und iteratives Retrieval von Information
F. T. Sommer: Theorie neuronaler Assoziativspeicher - Lokales Lernen und iteratives Retrieval von Information.
Verlag Hänsel-Hohenhausen (1993) ISBN 3-89349-901-6    [http://redwood.berkeley.edu/~fsommer/papers/dis.ps  ps]
Verlag Hänsel-Hohenhausen (1993) ISBN 3-89349-901-6    [http://rctn.org/fsommer/papers/dis.ps  ps]


=== Neuroimaging ===
=== Neuroimaging ===
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''' Edited book '''
''' Edited book '''


Eds: F. T. Sommer, A. Wichert: Exploratory analysis and data modeling in functional neuroimaging
Eds: F. T. Sommer, A. Wichert: Exploratory analysis and data modeling in functional neuroimaging.
MIT Press, Boston, MA (2003)    link to publisher (table of contents, etc.)
MIT Press, Boston, MA (2003)  
 
'''General issues of Neuroimaging'''
'''General issues of Neuroimaging'''


F. T. Sommer, J. A. Hirsch, A. Wichert: Theories, data analysis and simulation models in neuroimaging - an overview
F. T. Sommer, J. A. Hirsch, A. Wichert: Theories, data analysis and simulation models in neuroimaging - an overview
In  Exploratory analysis and data modeling in functional neuroimaging.
In  Exploratory analysis and data modeling in functional neuroimaging.
Eds.: F.T. Sommer and A. Wichert,  MIT Press, Boston, MA (2003)  [http://redwood.berkeley.edu/~fsommer/papers/sommeretal03.pdf  pdf]   
Eds.: F.T. Sommer and A. Wichert,  MIT Press, Boston, MA (2003)  [http://rctn.org/fsommer/papers/sommeretal03.pdf  pdf]   


V. Schmitt, A. Wichert, J. Grothe, F. T. Sommer: The brain positioning software
V. Schmitt, A. Wichert, J. Grothe, F. T. Sommer: The brain positioning software.
In: A practical guide of neuroscience databases and associated tools, Ed. R. Koetter, Kluwer, NY (2002)
In: A practical guide of neuroscience databases and associated tools, Ed. R. Koetter, Kluwer, NY (2002)
   
   
'''Unsupervised method of detecting functional activity in Neuroimaging'''
'''Unsupervised method of detecting functional activity in Neuroimaging'''


A. Wichert, B. Abler, J. Grothe, H. Walter, F. T. Sommer: Exploratory analysis of event-related fMRI demonstrated in a working memory study  
A. Wichert, B. Abler, J. Grothe, H. Walter, F. T. Sommer: Exploratory analysis of event-related fMRI demonstrated in a working memory study.
In  Exploratory analysis and data modeling in functional neuroimaging.
In  Exploratory analysis and data modeling in functional neuroimaging.
Eds.: F.T. Sommer and A. Wichert,  MIT Press, Boston, MA (2003) [http://redwood.berkeley.edu/~fsommer/papers/wichertetal03.pdf  pdf]   
Eds.: F.T. Sommer and A. Wichert,  MIT Press, Boston, MA (2003) [http://rctn.org/fsommer/papers/wichertetal03.pdf  pdf]   


A. Wichert, H. Walter, G. Groen, A. Baune, J. Grothe, A. Wunderlich, F. T. Sommer: Detection of delay selective activity during a working memory task by explorative data analysis
A. Wichert, H. Walter, G. Groen, A. Baune, J. Grothe, A. Wunderlich, F. T. Sommer: Detection of delay selective activity during a working memory task by explorative data analysis.
Neuroimage (13) (2001)  282
Neuroimage (13) (2001)  282


A. Baune, F. T. Sommer, M. Erb, D. Wildgruber, B. Kardatzki, G. Palm, W. Grodd:  Dynamical Cluster Analysis of Cortical fMRI Activation
A. Baune, F. T. Sommer, M. Erb, D. Wildgruber, B. Kardatzki, G. Palm, W. Grodd:  Dynamical Cluster Analysis of Cortical fMRI Activation.
NeuroImage 6 (5) (1999) 477 - 489 [http://redwood.berkeley.edu/~fsommer/papers/bauneetal99.pdf  pdf]   
NeuroImage 6 (5) (1999) 477 - 489 [http://rctn.org/fsommer/papers/bauneetal99.pdf  pdf]   


'''Analysis techniques in Positron Emission Tomography'''
'''Analysis techniques in Positron Emission Tomography'''


G. Glatting, F. M. Mottaghy, J. Karitzky, A. Baune, F. T. Sommer, G. B. Landwehrmeyer, S. N. Reske: Improving binding potential analysis in [11C]raclopide PET studies using cluster analysis.
G. Glatting, F. M. Mottaghy, J. Karitzky, A. Baune, F. T. Sommer, G. B. Landwehrmeyer, S. N. Reske: Improving binding potential analysis in [11C]raclopide PET studies using cluster analysis.
Medical Physics 31 (4) (2004) 902-906 [http://redwood.berkeley.edu/~fsommer/papers/glattingetal04.pdf  pdf]   
Medical Physics 31 (4) (2004) 902-906 [http://rctn.org/fsommer/papers/glattingetal04.pdf  pdf]   


A. Baune, A. Wichert, G. Glatting, F. T. Sommer: Dynamical cluster analysis for the detection of microglia activation
A. Baune, A. Wichert, G. Glatting, F. T. Sommer: Dynamical cluster analysis for the detection of microglia activation
in Artificial Neural Nets and Genetic Algorithms. Eds. V. Kurkova, N. C. Stelle, R. Neruda, M. Karny. Springer, Wien (2001) 442 - 445
in Artificial Neural Nets and Genetic Algorithms. Eds. V. Kurkova, N. C. Stelle, R. Neruda, M. Karny. Springer, Wien (2001) 442 - 445
   
   
J. Ruckgaber, G. Glatting, J. Karitzky, A. Baune, F. T. Sommer, B. Neumaier, S. N. Reske: Clusteranalyse in der Positronen-Emissions-Tomographie des Hirns mit C-11-PK11195
J. Ruckgaber, G. Glatting, J. Karitzky, A. Baune, F. T. Sommer, B. Neumaier, S. N. Reske: Clusteranalyse in der Positronen-Emissions-Tomographie des Hirns mit C-11-PK11195.
Nuklearmedizin (40) (2001) A95
Nuklearmedizin (40) (2001) A95


=== Neural associative memories in information technology ===
=== Neural associative memories in information technology ===
M. Rehn, F. T. Sommer: Storing and restoring visual input with collaborative rank coding and associative memory.
Neurocomputing 69 (10-12) (2006) 1219-1223 [http://rctn.org/fsommer/papers/rehnsommer06neurocomp.pdf  pdf]


G. Palm, F. Schwenker, F. T. Sommer, A. Strey: Neural associative memory
G. Palm, F. Schwenker, F. T. Sommer, A. Strey: Neural associative memory.
In Associative Processing and Processors, Eds. A. Krikelis and C. C. Weems, IEEE CS Press, Los Alamitos, CA, USA (1997) 307-326 [http://redwood.berkeley.edu/~fsommer/papers/psss95.ps  ps]   
In Associative Processing and Processors, Eds. A. Krikelis and C. C. Weems, IEEE CS Press, Los Alamitos, CA, USA (1997) 307-326 [http://rctn.org/fsommer/papers/psss95.ps  ps]   
    
    
F.T.Sommer, F.Schwenker, G.Palm: Assoziative Speicher als Module in informationsverarbeitenden Systemen
F. T. Sommer, F. Schwenker, G. Palm: Assoziative Speicher als Module in informationsverarbeitenden Systemen.
In: Contributions to the Workshop Aspekte Neuronalen Lernens, Eds. L.Cromme, J. Wille, T. Kolb Tech Report, TU Cottbus M-01/1995 (1995)
In: Contributions to the Workshop Aspekte Neuronalen Lernens, Eds. L.Cromme, J. Wille, T. Kolb Tech Report, TU Cottbus M-01/1995 (1995)


G.Palm, F.Schwenker, F.T.Sommer: Associative memory and sparse similarity preserving codes
G. Palm, F. Schwenker, F. T. Sommer: Associative memory and sparse similarity preserving codes.
In: From Statistics to Neural Networks: Theory and Pattern Recognition Applications, Ed. V.Cherkassky, Springer NATO ASI Series F, New York (1994) 282-302
In: From Statistics to Neural Networks: Theory and Pattern Recognition Applications, Ed. V.Cherkassky, Springer NATO ASI Series F, New York (1994) 282-302


== Workshops and Teaching Courses ==


'''Workshops and conferences:'''
== Earlier Publications in Physics ==


* NIPS 2005: Program chair for Neuroscience
P. Frodl, F. T. Sommer, K. Hau, F. Wahl: On the effective interaction of two hydrogen centres in Niobium.
* CNS 2002 workshop: Neural assemblies
Z. f. Naturforsch. 43a (1990) 857-866
* NIPS 2000 workshop: Explorative analysis and data modeling in functional neuroimaging


'''Graduate teaching (US):'''
K. Hau, P. Frodl, M. Gnirß, F. T. Sommer, F. Wahl: A Microscopic Theory of a alpha-Phase Hydrogen in Niobium.
Z. f. physikalische Chemie 163 (1989) 549-554
F. T. Sommer, K. Hau, P. Frodl, F. Wahl: Calculation of the excitation energies of a hydrogen impurity in Niobium.
Z. f. Naturforsch. 43a (1988) 923-929


* Theoretical and computational neuroscience (2003, 2005; teaching participation at course MCB262/PSYCH290P, UC Berkeley)  
K. Hau, P. Frodl, F. T. Sommer, F. Wahl: A microscopic theory of a single hydrogen centre in Niobium.
Z. f. Naturforsch. 43a (1988) 914-922


'''Graduate teaching (semester courses held at University of Ulm):'''


* Information retrieval and associative memory
==Roots==
* Computational Neuroscience
 
* Theoretical methods for the interpretation of medical functional imaging data
[http://neurotree.org/neurotree/tree.php?pid=12684 NeuroTree entry]
* Information Retrieval
 
* Associative memories: conventional and neuronal
==Funding==
* Neural Cell Assemblies
Currently, my research is funded by National Institute of Health, the National Science Foundation and the Kavli Foundation.
 
In the past, I have received support from the Hawkins-Strauss trust, the German Science Foundation, the Ministry of Education and Research of Baden-Wuerttemberg and by the Wilhelm-Schweizer-Zinnfiguren GmbH. I enjoyed a free and broad academic training due to the public university system of the Federal Republic of Germany.

Revision as of 03:33, 2 September 2022

Fritz.jpg

Friedrich T. Sommer, Ph.D.
University of California, Berkeley
Redwood Center for Theoretical Neuroscience - HWNI
575A Evans Hall MC# 3198
Berkeley, CA 94720-3198
phone (510) 642-7251
fax (510) 642-7206
email F $ 0 M M E R (a) B E R K E L E Y * E D U (please retype)

Researcher in Residence, Intel Labs & Adjunct Professor, Redwood Center for Theoretical Neuroscience & Helen Wills Neuroscience Institute, University of California, Berkeley
Faculty member (Hochschuldozent), Department of Computer Science, University of Ulm

Previous appointments:
2005-2011 Associate Adjunct Professor, University of California, Berkeley
2009 Acting director of the Redwood Center for Theoretical Neuroscience at UC Berkeley



Research Interests

Many impressive capabilities of the brain are not yet understood, for example, how unsupervised learning shapes the brains of animals and humans while controlling closed action-perception loops with the environment, or the virtually unlimited capacity of our long-term memory and its close connection to spatial navigation.

In collaboration with experimental neuroscience labs, my lab investigates the theoretical principles of learning and perception and their biological bases in the circuit dynamics of the brain. To study these issues we develop computational models, advanced techniques of data analysis, and strategies for sharing neurophysiology data CRCNS.org.

At the same time, I am interested in neurobiological design principles for building artificial intelligence systems. I am currently on a partial leave from the University to apply these ideas to the development of neuromorphic computing at Intel Labs.

For a full list of publications, see Google Scholar Profile

Journal articles, book chapters and submissions

Submissions/arXiv'ed manuscripts

Z. Li, Y. Chen, Y. LeCun, F. T. Sommer: Neural Manifold Clustering and Embedding. https://arxiv.org/abs/2201.10000 (2022)

C. Warner, F. T. Sommer: A probabilistic latent variable model for detecting structure in binary data. https://arxiv.org/abs/2201.11108 (2022)

E. P. Frady, D. Kleyko, C. J. Kymn, B. A. Olshausen, F. T. Sommer: Computing on functions using randomized vector representations. https://arxiv.org/abs/2109.03429 (2021)

D. Kleyko, M. Davies, E. P. Frady, P. Kanerva, S. J. Kent, B. A. Olshausen, E. Osipov, J. M. Rabaey, D. A. Rachkovskij, A. Rahimi, F. T. Sommer: Vector Symbolic Architectures as computing framework for nanoscale hardware. https://arxiv.org/pdf/2106.05268.pdf (2021)

C. Warner, F. T. Sommer: A Model for Image Segmentation in Retina arXiv (2020)

Z. Li, F. T. Sommer: The amplitude-phase complex Boltzmann machine arXiv (2020)

C. Bybee, E. P. Frady, F. T. Sommer: Deep learning in spiking phasor neural networks, in preparation

Recent Publications

D. Toker, I. Pappas, J. D. Lendner, J. Frohlich, D. M. Mateos, S. Muthukumaraswamy, R. Carhart-Harris, M. Pfaff, P. M. Vesta, M. M. Monti, F. T. Sommer, R. T. Knight, M. D'Esposito: Consciousness is supported by near-critical cortical electrodynamics. PNAS https://www.pnas.org/content/119/7/e2024455119 (2022)

D. Kleyko, E. P. Frady, F. T. Sommer: Cellular Automata Can Reduce Memory Requirements of Collective-State Computing, IEEE Transactions on Neural Networks and Learning Systems. Print ISSN: 2162-237X, Online ISSN: 2162-2388, Digital Object Identifier: 10.1109/TNNLS.2021.3119543 (2021)
(Earlier version arXiv (2020))

E. P. Frady, D. Kleyko, F. T. Sommer: Variable Binding for Sparse Distributed Representations: Theory and Applications. IEEE Transactions on Neural Networks and Learning Systems 10.1109/TNNLS.2021.3105949 (2021)
(Earlier version arXiv (2020))

Zengyi Li, Yubei Chen, F. T. Sommer: A Neural Network MCMC Sampler That Maximizes Proposal Entropy. Entropy 23(3), 269; doi:10.3390/e23030269 (2021)
(Earlier version arXiv (2020))

E. P. Frady, S. J. Kent, B. A. Olshausen and F. T. Sommer: Resonator Networks, 1: An Efficient Solution for Factoring High-Dimensional, Distributed Representations of Data Structures. Neural Computation 32 (12): 2311–2331 (2020)

S. J. Kent, E. P. Frady, F. T. Sommer and B. A. Olshausen: Resonator Networks, 2: Factorization Performance and Capacity Compared to Optimization-Based Methods. Neural Computation 32 (12): 2332–2388 (2020)

D. Toker, F. T. Sommer, M Desposito, M: A simple method for detecting chaos in nature. Communications Biology 3, 11 (2020)

E. P. Frady, G. Orchard, D. Florey, N. Imam, R. Liu, J. Mishra, J. Tse, A. Wild, F. T. Sommer, M. Davies: Neuromorphic Nearest-Neighbor Search Using Intel's Pohoiki Springs. NICE '20: Proceedings of the Neuro-inspired Computational Elements WorkshopMarch 2020 Article No.: 23, Pages 1–10 (2020)
(Earlier version arXiv (2020))

Z. Li, Y. Chen, F. T. Sommer: Annealed Denoising Score Matching: Learning energy based models in high dimensional spaces ICLR 2020 Open Review (2020)

J. A. Livezey, A. F. Bujan, F. T. Sommer: Learning Overcomplete, low coherence dictionaries with linear inference. Journal of Machine Learning Research 20(174):1−42 (2019)

E. P. Frady, F. T. Sommer: Robust computation with rhythmic spike patterns. Proceedings of the National Academy of Sciences September 3, 116 (36) 18050-18059 (2019), UCB press release
(Earlier version arXiv (2019))

D. Toker, F. T. Sommer: Information integration in large brain networks. PLOS Computational Biology (2019)
(Earlier version arXiv (2018))

E. P. Frady, D. Kleyko, F. T. Sommer: A theory of sequence indexing and working memory in recurrent neural networks. Neural Computation, 30(6), 1449-1513. (2018)

K. E. Bouchard, J. B. Aimone, M. Chun, T. Dean, M. Denker, M. Diesmann, D. D. Donofrio, L. M. Frank, N. Kasthuri, C. Koch, O. Rübel, H. D. Simon, F. T. Sommer, Prabhat: International neuroscience initiatives through the lens of high-performance computing. Computer, 51(4): 50-59 (2018)

Publications 2017 - 2003

C. Soto-Sánchez, X. Wang, V. Vaingankar, F. T. Sommer, J. A. Hirsch: The spatial scale of receptive fields in the visual sector of the cat’s thalamic reticular nucleus. Nature Communications 8, 800 (2017) doi:10.1038/s41467-017-00762-7 (2017)

K. E. Bouchard, A. F. Bujan, E. F. Chang, F. T. Sommer: Sparse coding of ECoG signals identifies interpretable components for speech control in human sensorimotor cortex. IEEE, EMBC (2017)

K. E. Bouchard, J. B. Aimone, M. Chun, T. Dean, M. Denker, M. Diesmann, D. Donofrio, L. M. Frank, N. Kasthuri, C. Koch, O. Rübel, H. Simon, F. T. Sommer, Prabhat: High-Performance Computing in Neuroscience for Data-Driven Discovery, Integration, and Dissemination. Neuron 92:628-631 (2016)

A. Knoblauch, F. T. Sommer: Structural plasticity, effectual connectivity and memory in cortex. Frontiers in Neuroanatomy (2016)

J. A. Livezey, A. F. Bujan, F. T. Sommer: On degeneracy control in overcomplete ICA arXiv (2016)

V. Suresh, U.M. Çiftçioğlu, X. Wang, B. M. Lala, K. R. Ding, W. A. Smith, F. T. Sommer, J. A. Hirsch: Synaptic Contributions to Receptive Field Structure and Response Properties in the Rodent Lateral Geniculate Nucleus of the Thalamus. Journal of Neuroscience 36(43), 10949-10963 (2016)

J. L. Teeters, K. Godfrey, R. Young, C. Dang, C. Friedsam, B. Wark, H. Asari, S. 
Peron, N. Li, A. Peyrache, G. Denisov, J. H. Siegle, S. R. Olsen, C. Martin, M. Chun, S. Tripathy, T. J. Blanche, K. D. Harris, G. Buzsaki, C. Koch, M. Meister, K. Svoboda, F. T. Sommer: Neurodata Without Borders: Creating 
a common data format for neurophysiology. Neuron 88:629-634 (2015)

C. J. Hillar and F. T. Sommer: When can dictionary learning uniquely recover sparse data from subsamples? IEEE Transactions on Information Theory 61(11):6290-6297 (2015). (Earlier arXiv version (2013))

S. Mobin, J. Arnemann, F. T. Sommer: Information-based learning by agents in unbounded state spaces. Advances in Neural Information Processing Systems NIPS 26, MIT Press (2014).

G. Agarwal, I. H. Stevenson, A. Berényi, K. Mizuseki, G. Buzsáki, F. T. Sommer: Spatially distributed local fields in the hippocampus encode rat position. Science 344 (2014): 626-630.

A. Knoblauch, E. Koerner, U. Koerner, F. T. Sommer: Structural synaptic plasticity has high memory capacity and can explain graded amnesia, catastrophic forgetting, and the spacing effect. PLOS ONE (2014)

L. M. Martinez, M. Molano-Mazon, X. Wang, F. T. Sommer, J. A. Hirsch: Statistical wiring of thalamic receptive fields optimizes spatial sampling of the retinal image. Neuron 81 (2014) 943-956 PubMed pdf (2014)

J. A. Hirsch, X. Wang, V. Vaingankar, F. T. Sommer: Inhibitory circuits in the visual thalamus. Chapter in: The New Visual Neurosciences, Eds.: Leo M. Chalupa and John S. Werner, MIT Press (2013)

F. T. Sommer: Neural oscillatons and synchrony as mechanisms for coding, communication and computation in the visual system. Chapter in: The New Visual Neurosciences, Eds.: Leo M. Chalupa and John S. Werner, MIT Press (2013) pdf

D. Y. Little, F. T. Sommer: Learning and exploration in action-perception loops. Frontiers in Neural Circuits. doi: 10.3389/fncir.2013.00037 (2013). Earlier arXiv version: Learning in embodied action-perception loops through exploration (2011)

D. Y. Little, F. T. Sommer: Maximal mutual information, not minimal entropy, for escaping the "Dark Room". Comment on "Whatever next? Predictive brains, situated agents, and the future of cognitive science." in Behavioral Brain Sciences 2013 Jun;36(3):220-221. doi: 10.1017/S0140525X12002415 [1]

G. Agarval, F. T. Sommer: Measuring information in spike trains about intrinsic brain signals. Chapter in: Spike timing: Mechanisms and functions, Eds.: P. M. DiLorenzo, J. D. Victor, CRC Press - Taylor & Francis Group 137-152 (2013)

V. Vaingankar, C. Soto-Sanchez, X. Wang, F. T. Sommer, J. A. Hirsch: Neurons in the thalamic reticular nucleus are selective for diverse and complex visual features. Frontiers in Integrative Neuroscience 6:118. DOI: 10.3389/fnint.2012.00118 (2012)

C. Hillar, F. T. Sommer: Comment on the article "Distilling free-form natural laws from experimental data" arXiv (2012)

X. Wang, F. T. Sommer, J. A. Hirsch: Inhibitory circuits for visual processing in thalamus. Current Opinion in Neurobiology 21 (2011) 726-733 PubMed pdf

X. Wang, V. Vaingankar, C. Soto Sanchez, F. T. Sommer, J. A. Hirsch: Thalamic interneurons and relay cells use complementary synaptic mechanisms for visual processing. Nature Neuroscience 14 (2011) 224-231 PubMed pdf

F. T. Sommer: Associative memory and learning. Chapter in Encyclopedia of the Sciences of Learning, Ed.: N. Seel, Springer (2011)

G. Isely, C. Hillar, F. T. Sommer: Decyphering subsampled data: Adaptive compressive sampling as a principle of brain communication. Advances in Neural Information Processing Systems NIPS 23. Eds: J. Lafferty and C. K. I. Williams and J. Shawe-Taylor and R.S. Zemel and A. Culotta (2011) 910-918 pdf

C. Hillar, F. T. Sommer: Ramsey theory reveals the conditions when sparse coding on subsampled data is unique. arXiv (2010)

X. Wang, J. A. Hirsch, F. T. Sommer: Recoding of sensory information across the retinothalamic synapse. Journal of Neuroscience 30: 13567-13577 PubMed pdf (2010)

K. Koepsell, X. Wang, J. A. Hirsch, F. T. Sommer: Exploring the function of neural oscillations in early sensory systems. Focused review in Frontiers in Neuroscience 4: 53-61 (2010)

A. Knoblauch, G. Palm, F. T. Sommer: Memory capacities for synaptic and structural plasticity. Neural Computation 22 (2010) 289-341 pdf

G. Monaci, P. Vandergheynst, F. T. Sommer: Learning bimodal structure in audio-visual data. IEEE Transactions on Neural Networks 20 (2009) 1898-1910 pdf

K. Koepsell, X. Wang, V. Vaingankar, Y. Wei, Q. Wang, D. L. Rathbun, W. M. Usrey, J. A. Hirsch, F. T. Sommer: Retinal oscillations carry visual information to cortex. Frontiers in Systems Neuroscience (2009)

K. Koepsell, F. T. Sommer: Information transmission in oscillatory neural activity. Biological Cybernetics 99 (2008) 403-416 pdf

J. L. Teeters, K. D. Harris, K. J. Millman, B. A. Olshausen, F. T. Sommer: Data sharing for computational neuroscience. Neuroinformatics 6 (2008) 47-55 pdf

X. Wang, Y. Wei, V. Vaingankar, Q. Wang, K. Koepsell, F. T. Sommer, J. A. Hirsch: Feedforward excitation and inhibition evoke dual modes of firing in the cat’s visual thalamus during naturalistic viewing. Neuron 55 (2007) 465-478. pdf See also the preview about this paper: P. Reinagel: The inner life of bursts. Neuron 55 (2007) 339-341

F. T. Sommer: Bunte Theorien für graue Zellen. Gehirn und Geist, Juni (2007) 70-76

M. Rehn, F. T. Sommer: A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields. Journal of Computational Neuroscience 22 (2) 135-146 (2007). pdf

M. Rehn, F. T. Sommer: Storing and restoring visual input with collaborative rank coding and associative memory. Neurocomputing 69 (10-12) (2006) 1219-1223 pdf

F. T. Sommer, P. Kanerva: Can neural models of cognition benefit from the advantages of connectionism? Behavoral and Brain Sciences 29 (1) 86-87 (2006) pdf

F. T. Sommer, T. Wennekers: Synfire chains with conductance-based neurons: internal timing and coordination with timed input. Neurocomputing 65-66 (2005) 449 - 454 pdf

D. George, F. T. Sommer: Computing with inter-spike inverval codes in networks of integrate and fire neurons. Neurocomputing 65-66 (2005) 414 - 420 pdf

L. M. Martinez, Q. Wang, R. C. Reid, C. Pillai, J.-M. Alonso, F. T. Sommer, J. A. Hirsch: Receptive field structure varies with layer in the primary visual cortex. Nature Neuroscience 8 (12) (2005) 372 - 379 pdf

A. Knoblauch, F. T. Sommer: Spike-timing dependent plasticity can form "zero-lag" links for cortical oscillations. Neurocomputing 52-54 (2004) 301 - 306 pdf

G. Glatting, F. M. Mottaghy, J. Karitzky, A. Baune, F. T. Sommer, G. B. Landwehrmeyer, S. N. Reske: Improving binding potential analysis in [11C]raclopide PET studies using cluster analysis. Medical Physics 31 (4) (2004) 902-906 pdf

J. A. Hirsch, L. M. Martinez, C. Pillai, J.-M. Alonso, Q. Wang, F. T. Sommer: Functionally distinct inhibitory neurons at the first stage of visual cortical processing. Nature Neuroscience 6 (12) (2003) 1300 - 1308 pdf

F. T. Sommer, T. Wennekers: Models of distributed associative memory networks in the brain. Theory in Biosciences (122) (2003) 70 - 86 pdf

A. Knoblauch, F. T. Sommer: Synaptic plasticity, conduction delays, and inter-areal phase relations of spike activity in a model of reciprocally connected areas. Neurocomputing (52-54) (2003) 301-306 pdf

Workshops, Conferences, Seminar Series and Teaching Courses

Workshop and conference organization:

Seminar series organization:

  • Redwood Seminar Series, Talks at the Redwood Center, UC Berkeley, 2005-present (most talks you can watch online via the link), co-organizer

Teaching at UC Berkeley:

International summer courses

Semester courses

  • Theoretical and computational neuroscience (2003, 2005; 2007; 2009; 2010; teaching participation at course MCB262/PSYCH290P/VS265)
  • Neural Computation (2006; teaching participation at course VS298)

Teaching at University of Ulm:

Summer course:

  • Statistics of natural signals (2005)

Semester courses (1997-2002):

  • Information retrieval and associative memory
  • Computational Neuroscience
  • Theoretical methods for the interpretation of medical functional imaging data
  • Information Retrieval
  • Associative memories: conventional and neuronal
  • Neural Cell Assemblies

Earlier Edited Book and Software for Neuroimaging

Eds: F. T. Sommer, A. Wichert: Exploratory analysis and data modeling in functional neuroimaging. MIT Press, Boston, MA (2003)

Tool for analysis of functional neuroimaging: The brain positioning software (BPS) gives access to the Brodmann atlas and can be called directly from the SPM data analysis software. For description, see: Schmitt V., Wichert A., Grothe J., Sommer F. T.: The brain positioning software. A Practical guide of neuroscience databases. Ed.: R. Kötter, Kluwer, NY, 2002

Earlier Neuroscience Publications by Theme

For a brief description of some of the research projects, see synopsis of research.

Computational models and analysis of cell physiology in the early visual system

X. Wang, V. Vaingankar, C. Soto Sanchez, F. T. Sommer, J. A. Hirsch: Thalamic interneurons and relay cells use complementary synaptic mechanisms for visual processing. Nature Neuroscience 14 (2011) 224-231

X. Wang, J. A. Hirsch, F. T. Sommer: Recoding of sensory information across the retinothalamic synapse. Journal of Neuroscience 30 (2010) 13567-13577

X. Wang, Y. Wei, V. Vaingankar, Q. Wang, K. Koepsell, F. T. Sommer, J. A. Hirsch: Feedforward excitation and inhibition evoke dual modes of firing in the cat’s visual thalamus during naturalistic viewing. Neuron 55 (2007) 465-478. See also the preview about this paper: P. Reinagel: The inner life of bursts. Neuron 55 (2007) 339-341

M. Rehn, F. T. Sommer: A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields. J. Comp. Neurosci. 22 (2) 135-146 (2007). pdf Earlier online prepublication SpringerLink DOI 10.1007/s10827-006-003-9 (2006)

M. Rehn, F. T. Sommer: Storing and restoring visual input with collaborative rank coding and associative memory. Neurocomputing 69 (10-12) (2006) 1219-1223 pdf

L. M. Martinez, Q. Wang, R. C. Reid, C. Pillai, J.-M. Alonso, F. T. Sommer, J. A. Hirsch: Receptive field structure varies with layer in the primary visual cortex. Nature Neuroscience 8 (12) (2005) 372 - 379 pdf

J. A. Hirsch, L. M. Martinez, C. Pillai, J.-M. Alonso, Q. Wang, F. T. Sommer: Functionally distinct inhibitory neurons at the first stage of visual cortical processing. Nature Neuroscience 6 (12) (2003) 1300 - 1308 pdf

Mechanisms of memory in realistic neural networks

Structural plasticity

A. Knoblauch, E. Koerner, U. Koerner, F. T. Sommer: Structural synaptic plasticity has high memory capacity and can explain graded amnesia, catastrophic forgetting, and the spacing effect. to appear in PLOS ONE (2014)

A. Knoblauch, G. Palm, F. T. Sommer: Memory capacities for synaptic and structural plasticity. Neural Computation 22 (2010) 289-341 pdf

Long-term memory

F. T. Sommer, T. Wennekers: Associative memory in networks of spiking neurons Neural Networks 14 (6-7) Special Issue: Spiking Neurons in Neuroscience and Technology (2001) 825 - 834 pdf

F. T. Sommer, T. Wennekers: Modeling studies on the computational function of fast temporal structure in cortical circuit activity Journal of Physiology - Paris 94 (5/6) (2000) 473-488 pdf

F. T. Sommer: On cell assemblies in a cortical column Neurocomputing (32-33) (2000) 517 - 522 pdf

T. Wennekers, F. T. Sommer: Gamma-oscillations support optimal retrieval in associative memories of two-compartment neurons Neurocomputing 26-27 (1999) 573 - 578 pdf

T. Wennekers, F. T. Sommer, G. Palm: Iterative Retrieval in Associative Memories by Threshold Control of Different Neural Models In: Supercomputers in Brain Research: From Tomography to Neural Networks World Scientific Publishing Comp (1995) 301-319

Short-term memory

A. Knoblauch, T. Wennekers, F. T. Sommer: Is voltage dependent synaptic transmission in NMDA receptors a robust mechanism for working memory? Neurocomputing (44-46) (2002) 19-24 pdf

U. Vollmer, F. T. Sommer: Coexistence of short and long term memory in a model network of realistic neurons Neurocomputing (38-40) (2001) 1031 - 1036 pdf

Large-scale integration of cortical representations

F. T. Sommer, T. Wennekers: Models of distributed associative memory networks in the brain Theory in Biosciences (122) (2003) 70 - 86 pdf

Associative memory in reciprocal cortico-cortical projections

F. T. Sommer, T. Wennekers: Associative memory in a pair of cortical cell groups with reciprocal projections Neurocomputing (38-40) (2001) 1575 - 1580 pdf

F. T. Sommer, T. Wennekers, G. Palm: Bidirectional completion of cell assemblies in the cortex Computational Neuroscience: Trends in Research 1998, Plenum Press, New York, (1998) ps

Large-scale integration relying on neural oscillations

A. Knoblauch, F. T. Sommer: Spike-timing dependent plasticity can form "zero-lag" links for cortical oscillations Neurocomputing (58-60) 185 - 190 (2004) pdf

A. Knoblauch, F. T. Sommer: Synaptic plasticity, conduction delays, and inter-areal phase relations of spike activity in a model of reciprocally connected areas Neurocomputing (52-54) (2003) 301-306 pdf

Models of macroscopic activity spread in cortex

V. Schmitt, R. Koetter, F. T. Sommer: The impact of thalamo-cortical projections on activity spread in cortex Neurocomputing (2003) (52-54) (2003) 919-924 pdf

R. Kötter and F. T. Sommer: Global relationship between anatomical connectivity and activity propagation in the cerebral cortex Phil. Trans. R. Soc. Lond. B (355) (2000) 127 - 134 pdf

F. T. Sommer, R. Kötter: Simulating a Network of Cortical Areas Using Anatomical Connection Data in the Cat Computational Neuroscience: Trends in Research 1997, Plenum Press, New York (1997) 511-517 ps

R. Koetter, P. Nielsen, J. Dyhrfjeld, F. T. Sommer, G. Northoff: Multi-level integration of quantitative neuroanatimical data Chapter in Computational Neuroanatomy: Principles and Methods. Ed.: G. A. Ascoli, Humana Press Inc., Totowa, NJ (2002)

Theory of associative memory

Review on capacity analysis of memory networks

A. Knoblauch, G. Palm, F. T. Sommer: Memory capacities for synaptic and structural plasticity. Neural Computation, Volume 22, Issue 2, pp. 289-341 (2010) pdf

Bayesian theory of autoassociative memory

F. T. Sommer, P. Dayan: Bayesian Retrieval in Associative Memories with Storage Errors. IEEE Transactions on Neural Networks 9 (4) (1998) 705-713 pdf

Bidirectional sparse associative memory

F. T. Sommer, G. Palm: Improved Bidirectional Retrieval of Sparse Patterns Stored by Hebbian Learning. Neural Networks 12 (2) (1999) 281 - 297 pdf

F. T. Sommer, G. Palm: Bidirectional Retrieval from Associative Memory. Advances in Neural Information Processing Systems 10, MIT Press, Cambridge, MA (1998) 675 - 681 pdf

Capacity analysis of recurrent sparse autoassociative memories

F. Schwenker, F. T. Sommer, G. Palm: Iterative Retrieval of sparsely coded associative memory patterns. Neural Networks 9 (1996) 445-455 pdf

Capacity analysis of sparse pattern recognition

G. Palm, F. T. Sommer: Information capacity in recurrent Mc.Culloch-Pitts networks with sparsely coded memory states. Network 3 (1992) 177-186 pdf

G. Palm, F. T. Sommer: Information and pattern capacities in neural associative memories with feedback for sparse memory patterns. In: Neural Network Dynamics, Springer New York (1992). Eds.: J.G.Taylor, E.R.Caianello, R.M.J.Cotterill, J.W.Clark, 3-18

Analysis of local learning rules

G. Palm, F. T. Sommer: Associative data Storage and Retrieval in Neural Nets. In: Models of Neural Networks III, Springer New York (1996) Eds: E.Domany, J.L.van Hemmen, K.Schulten, 79-118 pdf

Book, PhD-Thesis (in german)

F. T. Sommer: Theorie neuronaler Assoziativspeicher - Lokales Lernen und iteratives Retrieval von Information. Verlag Hänsel-Hohenhausen (1993) ISBN 3-89349-901-6 ps

Neuroimaging

Edited book

Eds: F. T. Sommer, A. Wichert: Exploratory analysis and data modeling in functional neuroimaging. MIT Press, Boston, MA (2003)

General issues of Neuroimaging

F. T. Sommer, J. A. Hirsch, A. Wichert: Theories, data analysis and simulation models in neuroimaging - an overview In Exploratory analysis and data modeling in functional neuroimaging. Eds.: F.T. Sommer and A. Wichert, MIT Press, Boston, MA (2003) pdf

V. Schmitt, A. Wichert, J. Grothe, F. T. Sommer: The brain positioning software. In: A practical guide of neuroscience databases and associated tools, Ed. R. Koetter, Kluwer, NY (2002)

Unsupervised method of detecting functional activity in Neuroimaging

A. Wichert, B. Abler, J. Grothe, H. Walter, F. T. Sommer: Exploratory analysis of event-related fMRI demonstrated in a working memory study. In Exploratory analysis and data modeling in functional neuroimaging. Eds.: F.T. Sommer and A. Wichert, MIT Press, Boston, MA (2003) pdf

A. Wichert, H. Walter, G. Groen, A. Baune, J. Grothe, A. Wunderlich, F. T. Sommer: Detection of delay selective activity during a working memory task by explorative data analysis. Neuroimage (13) (2001) 282

A. Baune, F. T. Sommer, M. Erb, D. Wildgruber, B. Kardatzki, G. Palm, W. Grodd: Dynamical Cluster Analysis of Cortical fMRI Activation. NeuroImage 6 (5) (1999) 477 - 489 pdf

Analysis techniques in Positron Emission Tomography

G. Glatting, F. M. Mottaghy, J. Karitzky, A. Baune, F. T. Sommer, G. B. Landwehrmeyer, S. N. Reske: Improving binding potential analysis in [11C]raclopide PET studies using cluster analysis. Medical Physics 31 (4) (2004) 902-906 pdf

A. Baune, A. Wichert, G. Glatting, F. T. Sommer: Dynamical cluster analysis for the detection of microglia activation in Artificial Neural Nets and Genetic Algorithms. Eds. V. Kurkova, N. C. Stelle, R. Neruda, M. Karny. Springer, Wien (2001) 442 - 445

J. Ruckgaber, G. Glatting, J. Karitzky, A. Baune, F. T. Sommer, B. Neumaier, S. N. Reske: Clusteranalyse in der Positronen-Emissions-Tomographie des Hirns mit C-11-PK11195. Nuklearmedizin (40) (2001) A95

Neural associative memories in information technology

M. Rehn, F. T. Sommer: Storing and restoring visual input with collaborative rank coding and associative memory. Neurocomputing 69 (10-12) (2006) 1219-1223 pdf

G. Palm, F. Schwenker, F. T. Sommer, A. Strey: Neural associative memory. In Associative Processing and Processors, Eds. A. Krikelis and C. C. Weems, IEEE CS Press, Los Alamitos, CA, USA (1997) 307-326 ps

F. T. Sommer, F. Schwenker, G. Palm: Assoziative Speicher als Module in informationsverarbeitenden Systemen. In: Contributions to the Workshop Aspekte Neuronalen Lernens, Eds. L.Cromme, J. Wille, T. Kolb Tech Report, TU Cottbus M-01/1995 (1995)

G. Palm, F. Schwenker, F. T. Sommer: Associative memory and sparse similarity preserving codes. In: From Statistics to Neural Networks: Theory and Pattern Recognition Applications, Ed. V.Cherkassky, Springer NATO ASI Series F, New York (1994) 282-302


Earlier Publications in Physics

P. Frodl, F. T. Sommer, K. Hau, F. Wahl: On the effective interaction of two hydrogen centres in Niobium. Z. f. Naturforsch. 43a (1990) 857-866

K. Hau, P. Frodl, M. Gnirß, F. T. Sommer, F. Wahl: A Microscopic Theory of a alpha-Phase Hydrogen in Niobium. Z. f. physikalische Chemie 163 (1989) 549-554

F. T. Sommer, K. Hau, P. Frodl, F. Wahl: Calculation of the excitation energies of a hydrogen impurity in Niobium. Z. f. Naturforsch. 43a (1988) 923-929

K. Hau, P. Frodl, F. T. Sommer, F. Wahl: A microscopic theory of a single hydrogen centre in Niobium. Z. f. Naturforsch. 43a (1988) 914-922


Roots

NeuroTree entry

Funding

Currently, my research is funded by National Institute of Health, the National Science Foundation and the Kavli Foundation.

In the past, I have received support from the Hawkins-Strauss trust, the German Science Foundation, the Ministry of Education and Research of Baden-Wuerttemberg and by the Wilhelm-Schweizer-Zinnfiguren GmbH. I enjoyed a free and broad academic training due to the public university system of the Federal Republic of Germany.