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Hillar C, Tran N (2018) Robust Exponential Memory in Hopfield Networks. Journal of Mathematical Neuroscience, 8:1. pdf


Albanna B, Hillar C, Sohl-Dickstein J, DeWeese M (2017) Minimum and Maximum Entropy Distributions for Binary Systems with Known Means and Pairwise Correlations. Entropy, 19, 427. pdf

Cheung B, Weiss E, Olshausen BA (2017) Emergence of foveal image sampling from learning to attend in visual scenes. International Conference on Learning Representations (ICLR) Conference. arXiv:1611.09430

Engel JH, Eryilmaz SB, Kim S, BrightSky M, Lam C, Lung HL, Olshausen BA, Wong HS (2017) Opportunities for Analog Coding in Emerging Memory Devices. arXiv:1701:06063

Hillar C, Marzen S (2017) Neural network coding of natural images with applications to pure mathematics. Contemporary Mathematics: Algebraic and Geometric Methods in Discrete Mathematics, Vol. 685, 2017 (pp. 189-222). reprint

Hillar C, Marzen S (2017) Revisiting perceptual distortion for natural images: mean discrete structural similarity index. IEEE Data Compression Conference (DCC), 2017 (pp. 241-249). reprint


Anderson AG, Ratnam K, Roorda A, Olshausen BA (2016) A Neural Model of High-Acuity Vision in the Presence of Fixational Eye Movements. 50th Asilomar Conference on Signals, Systems and Computers, November 6-9, 2016. IEEE Signal Processing Society. pdf

Anderson AG, Berg CP, Mossing DP, Olshausen BA (2016) DeepMovie Using Optical Flow and Deep Neural Networks to Stylize Movies. Arxiv Technical Report. May 2016. [1]

Marzen S, DeDeo S (2016) Weak universality in sensory tradeoffs. Physical Review E, 94. reprint


V.M. Carels and M.R. DeWeese. Rats Exert Executive Control. Neuron 86, pp. 1324-1326 (2015)

B. Cheung, J. A. Livezey, A. K. Bansal, B. A. Olshausen (2015) Discovering Hidden Factors of Variation in Deep Networks. Presented at International Conference on Learning Representations 2015 Workshop. pdf

J. P. Crutchfield and S. Marzen, “Signatures of Infinity: Nonergodicity and Resource Scaling in Prediction, Complexity, and Learning”, Physical Review E 91 (2015) 050106(R). PRE Editor's Suggestion arXiv link

F. Effenberger, C. Hillar. Discovery of Salient Low-Dimensional Dynamical Structure in Neuronal Population Activity Using Hopfield Networks. International Workshop on Similarity-Based Pattern Recognition. Springer International Publishing, 2015. pdf

E. Hagen, T. V. Ness, A. Khosrowshahi, C. Sørensen, M. Fyhn, T. Hafting, F. Franke, G. T. Einevoll (2015) ViSAPy: A Python tool for biophysics-based generation of virtual spiking activity for evaluation of spike-sorting algorithms. Journal of Neuroscience Methods. Volume 245, 182–204. link

C. J. Hillar and F. Effenberger, Robust Discovery of Temporal Structure in Multi-neuron Recordings Using Hopfield Networks. Procedia Computer Science 53 (2015): 365-374. pdf

C. J. Hillar and F. T. Sommer: When can dictionary learning uniquely recover sparse data from subsamples? IEEE Transactions on Information Theory, 61, no. 11 (2015): 6290-6297. pdf

C. Hillar, A. Wibisono. "A Hadamard-type lower bound for symmetric diagonally dominant positive matrices." Linear Algebra and its Applications 472 (2015): 135-141. pdf

S.E. Marzen and J. P. Crutchfield, “Informational and Causal Architecture of Discrete-Time Renewal Processes”, Entropy, 17, 4891-4917. (2015) reprint

S. E. Marzen, M. R. DeWeese, and J. P. Crutchfield, “Time Resolution Dependence of Information Measures for Spiking Neurons: Scaling and Universality”, (2015) submitted. arXiv link

R. Mehta, S. Marzen, and C. Hillar. Exploring discrete approaches to lossy compression schemes for natural image patches. In Signal Processing Conference (EUSIPCO), 2015 23rd European, pp. 2236-2240. IEEE, 2015. pdf

S. Mobin, J. Arnemann, F. T. Sommer (2015) Information-based learning by agents in unbounded state spaces. Advances in Neural Information Processing Systems 26, MIT Press.

J. Sohl-Dickstein, S. Teng, C. Rodgers, M.R. DeWeese, and N. Harper. A device for human ultrasonic echolocation. IEEE Transactions on Biomedical Engineering (in press) (2015)

J Sohl-Dickstein, E Weiss, N Maheswaranathan, S Ganguli. Deep Unsupervised Learning using Nonequilibrium Thermodynamics. Under Review preprint

J Sohl-Dickstein, CM Wang, BA Olshausen. Learning and inference in high dimensional Lie group models, and their application to natural movies. Under Revision preprint

W. W. Sprague, E. A. Cooper, I. Tošić, M. S. Banks, Stereopsis is adaptive for the natural environment, Science Advances, Vol. 1, no. 4, e1400254, 2015

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)


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. pdf Supplement

J. P. Crutchfield, R. G. James, S. Marzen and D. P. Varn, “Understanding and Designing Complex Systems: Response to ‘A framework for optimal high-level descriptions in science and engineering---preliminary report’”, (2014). arXiv link

J. H. Engel, S. B. Eryilmaz, SangBum Kim, M. BrightSky, Chung Lam, H.-L. Lung, B. A. Olshausen, H.-S.P. Wong (2014) ”Capacity optimization of emerging mem- ory systems: A shannon-inspired approach to device characterization,” Electron Devices Meeting (IEDM), 2014 IEEE International , vol., no., pp.29.4.1,29.4.4, 15- 17 Dec. 2014 link

A. K. Fletcher and S. Rangan, Scalable Inference for Neuronal Connectivity from Calcium Imaging, Proc. 28th Ann. Conf. Neural Information Processing Systems, NIPS (2014).

Harper NS, Scott BH, Semple MN, McAlpine D (2014) The neural code for auditory space depends on sound frequency and head size in an optimal manner. PLOS ONE 9: e108154 article link

C. Hillar, R. Mehta, and K. Koepsell, A Hopfield recurrent neural network trained on natural images performs state-of-the-art image compression, IEEE International Conference on Image Processing (ICIP), 2014, pp. 4092 - 4096. pdf

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 (2014)

P. Kanerva (2014) Computing with 10,000-Bit Words. Fifty-second Annual Allerton Conference Allerton House, UIUC, Illinois, USA October 1 - 3, 2014. pdf

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)

U. Köster, J. Sohl-Dickstein, C. M. Gray, B. A. Olshausen (2014) Modeling higher-order correlations within cortical microcolumns. PLOS Computational Biology, 10(7). [2]

M. S. Lewicki, B. A. Olshausen, A. Surlykke, C. F. Moss (2014) Scene analysis in the natural environment. Frontiers in Psychology, 5, article 199. pdf

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

S. Marzen and J. P. Crutchfield, “Predictive Rate-Distortion for Infinite-Order Markov Processes”, Journal of Statistics Physics, 163, 1312-1338. (2014) link

S. Marzen and J. P. Crutchfield, “Information Anatomy of Stochastic Equilibria”, Entropy 16 (2014) 4713-4748. reprint

Mizuseki K, Diba K, Pastalkova E, Teeters J, Sirota A, Buzsáki G.; Neurosharing: large-scale data sets (spike, LFP) recorded from the hippocampal-entorhinal system in behaving rats [v2; ref status: indexed,] F1000Research 2014, 3:98 (doi: 10.12688/f1000research.3895.2)

B. A. Olshausen (2014) Perception as an inference problem. In: The Cognitive Neurosciences V, M. Gazzaniga, R. Mangun, Eds. MIT Press. pdf

C. Rodgers and M. R. DeWeese. Neural correlates of task switching in prefrontal cortex and primary auditory cortex in a novel stimulus selection task for rodents. Neuron (2014). pdf

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 (2014)

J. Sohl-Dickstein, M. Mudigonda, M.R. DeWeese. Hamiltonian Monte Carlo Without Detailed Balance. Proceedings of the 31st International Conference on Machine Learning (Beijing) (2014). pdf

I. Tošić and S. Drewes, Learning joint intensity-depth sparse representations, IEEE Transactions on Image Processing 23 (5), 2122 - 2132, 2014

P. R. Zulkowski and M. R. DeWeese. (2014) Optimal finite-time erasure of a classical bit. Physical Review E. 89(5):052140. pdf


M. Mudigonda, N. Muller, A.Joshi, C.Hillar, F.Sommer, Learning non-local features for classification using compressed sensing and sparse coding, Workshop in high dimensional neural processing, NIPS 2013

G. Agarwal, 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 (2013) 137-152 Google Books

C.J. Hillar, L.H. Lim, Most tensor problems are NP-hard, Journal of the ACM, 60 (2013), no. 6, Art. 45. pdf

C. Hillar, A. Wibisono. "Maximum entropy distributions on graphs." (2013). (under review) arXiv:1301.3321

T. Hromádka, A.M. Zador, and M. R. DeWeese. (2013) Up-states are rare in awake auditory cortex. Journal of Neurophysiology, 109(8):1989-95. pdf

P. King, J. Zylberberg, and M. R. DeWeese. (2013) Inhibitory interneurons decorrelate excitatory cells to drive sparse code formation in a spiking model of V1. Journal of Neuroscience, 33(13):5475–85. pdf

Koster U, Olshausen BA (2013) Testing our conceptual understanding of V1 function. arXiv:1311.0778

D. Y. Little, F. T. Sommer: Learning and exploration in action-perception loops. Frontiers in Neural Circuits, 22 March 2013 doi: 10.3389/fncir.2013.00037 online (The 2011 arXiv version of this paper: Learning in embodied action-perception loops through exploration arXiv)

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 [3]

B. A. Olshausen, M. S. Lewicki (2013) What natural scene statistics can tell us about cortical representation. In: The New Visual Neurosciences. J. Werner, L.M. Chalupa, Eds. MIT Press. pdf

B. A. Olshausen (2013) Highly overcomplete sparse coding. In: SPIE Proceedings vol. 8651: Human Vision and Electronic Imaging XVIII, (B.E. Rogowitz, T.N. Pappas, H. de Ridder, Eds.), Feb. 4-7, 2013, San Francisco, California. pdf

P. R. Zulkowski, D. A. Sivak, and M. R. DeWeese. Optimal control of transitions between nonequilibrium steady states. Public Library of Science ONE. 8(12):e82754 (2013). pdf

J. Zylberberg and M. R. DeWeese. (2013) Sparse coding models can exhibit decreasing sparseness while learning sparse codes for natural images. PLoS Computational Biology. 9(8):e1003182. pdf


C. K. Abbey, A. Nosratieh, J. Sohl-Dickstein, K. Yang, J. M. Boone. (2012) Non-Gaussian statistical properties of breast images. Medical physics. pdf

C. F. Cadieu, B. A. Olshausen (2012) Learning intermediate-level representations of form and motion from natural movies. Neural Computation, 24(4):827-66. pdf

R. T. Canolty, C. F. Cadieu, K. Koepsell, R. T. Knight, J. M. Carmena. (2012) Multivariate phase-amplitude cross- frequency coupling in neurophysiological signals. IEEE Trans. Biomed. Eng. 59 (1), 8 – 11. pdf

R. T. Canolty, C. F. Cadieu, K. Koepsell, K. Ganguly, R. T. Knight, J. M. Carmena. (2012) Detecting event-related changes of multivariate phase coupling in dynamic brain networks. J. Neurophys. 107 (7), 2020 – 2031. pdf

N. Carlson, V. L. Ming, and M. R. DeWeese. (2012) Sparse codes for speech predict spectrotemporal receptive fields in the inferior colliculus. PLoS Computational Biology, 7(10):e1002250. pdf

C. Hillar, J. Sohl-Dickstein, K. Koepsell, (2012) Efficient and optimal Little-Hopfield auto-associative memory storage using minimum probability flow, NIPS Workshop on Discrete Optimization in Machine Learning (DISCML). pdf

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

B. A. Olshausen (2012) 20 years of learning about vision: Questions answered, Questions unanswered, and Questions not yet asked. In: 20 Years of Computational Neuroscience. J. Bower, Ed. pdf

S. Still, D. A. Sivak, A. J. Bell, G. E. Crooks (2012) Thermodynamics of prediction. Physical Review Letters, 109(12), 120604.

L Theis, J Sohl-Dickstein, M Bethge. (2012) Training sparse natural image models with a fast Gibbs sampler of an extended state space. Neural Information Processing Systems. pdf

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

J. Zylberberg, D. Pfau, and M. R. DeWeese. (2012) Dead leaves and the dirty ground: Low-level image statistics in transmissive and occlusive imaging environments. Physical Review E, 86:066112. pdf

P. R. Zulkowski, D. A. Sivak, G. E. Crooks, and M. R. DeWeese. (2012) The geometry of thermodynamic control. Physical Review E, 86(4 Pt 1):041148. pdf


Khosrowshahi, Amir (2011) , The laminar organization of V1 neural activity in response to dynamic natural scenes, PhD Thesis, UC Berkeley. (pdf, 68mb)

A. S. Charles, B. A. Olshausen, C. J. Rozell (2011) Learning sparse codes for hyperspectral imagery. IEEE Journal of Selected Topics in Signal Processing, 5, 963-978. pdf

B. J. Culpepper, J. Sohl-Dickstein, B. Olshausen. (2011) Building a better probabilistic model of images by factorization. International Conference on Computer Vision. pdf

Garrigues PJ, Olshausen BA (2011). Group Sparse Coding with a Laplacian Scale Mixture Prior. In: Advances in Neural Information Processing Systems, 23, J. Lafferty, C.K.I. Williams, J. Shawe-Taylor, R.S. Zemel, A. Culotta, Eds. NIPS reprint

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 23. Eds: J. Lafferty and C. K. I. Williams and J. Shawe-Taylor and R.S. Zemel and A. Culotta (2011) 910-918 pdf

J. Sohl-Dickstein, P. Battaglino, and M. R. DeWeese (2011) New method for parameter estimation in probabilistic models: Minimum probability flow. Physical Review Letters, 107(22):220601. pdf

J. Sohl-Dickstein, P. Battaglino, and M.R. DeWeese (2011) Minimum Probability Flow Learning. Proceedings of the 28th International Conference on Machine Learning (Bellevue, WA). pdf

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

I. Tosic, B. A. Olshausen and B. J. Culpepper (2011) Learning sparse representations of depth. IEEE Journal on Selected Topics in Signal Processing, Vol. 5, No 5, pp 941 - 952, 2011. pdf

C. M. Wang, J. Sohl-Dickstein, I. Tosic, B. A. Olshausen (2011) Lie Group Transformation Models for Predictive Video Coding. In: Data Compression Conference 2011 proceedings. pdf

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

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

J. Zylberberg, J. T. Murphy, and M. R. DeWeese (2011) A sparse coding model with synaptically local plasticity and spiking neurons can account for the diverse shapes of V1 simple cell receptive fields. PLOS Computational Biology, 7(10):e1002250. pdf

J. Zylberberg, and M. R. DeWeese (2011) How should prey animals respond to uncertain threats? Frontiers in Computational Neuroscience, 5:20. doi: 10.3389/fncom.2011.00020. pdf


Cadieu CF, Koepsell K (2010) Phase Coupling Estimation from Multivariate Phase Statistics. Neural Computation 22(12), pp. 3107 - 3126. pdf

Canolty RT, Ganguly K, Kennerley SW, Cadieu CF, Koepsell K, Wallis JD, Carmena JM (2010) Oscillatory phase coupling coordinates anatomically-dispersed cell assemblies. PNAS 107(40) 17356 - 17361. journal pdfsupplement

Coulter W. K., Hillar C. J., Isely G., Sommer, F. T. (2010) Adaptive compressed sensing – a new class of self-organizing coding models for neuroscience. Proceedings 2010 International Con-ference on Acoustics, Speech and Signal Processing ICASSP2010. 5494-5497

Culpepper BJ, Olshausen BA (2010) Learning transport operators for image manifolds. Advances in Neural Information Processing Systems (NIPS), 22. (2010) Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams and A. Culotta. pdf supplementary materials

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

Koepsell K, Wang X, Hirsch JA, Sommer FT (2010) Exploring the function of neural oscillations in early sensory systems. Focused review in Frontiers in Neuroscience 4 (1): 53-61. Frontiers in Neuroscience

Lauritzen TZ, Ales JM, Wade AR (2010) The effects of visuospatial attention measured across visual cortex using source-imaged, steady-state EEG. J. of Vision 10(14)39: 1 - 17. pdfsupplement

Olshausen BA, Deweese MR (2010) Applied mathematics: The Statistics of Style. Nature (News & Views), 463, 1027-1028. pdf

Olshausen BA, Anderson CH (2010) Does the brain de-jitter retinal images? Proceedings of the National Academy of Sciences, 107:46. pdf

Tsao DY, Cadieu C, and Livingstone M (2010) Object Recognition: Physiological and Computational Insights. In Primate Neuroethology. Edited by M. Platt and A. Ghazanfar. Oxford University Press. 2010 (in press)

Silver WA, Landau AN, Lauritzen TZ, Prinzmetal W, Robertson LC. Isolating human brain functional connectivity associated with a specific cognitive process. Proceedings of SPIE Volume 7527.

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


Abbey C, Sohl-Dickstein J, Olshausen BA. (2009) Higher-order scene statistics of breast images. Proceedings of SPIE. pdf

Cadieu C.F., Olshausen B.A., (2009) Learning Transformational Invariants from Natural Movies. Advances in Neural Information Processing Systems (NIPS), 21:209-216, 2009. D. Koller and D. Schuurmans and Y. Bengio and L. Bottou, MIT Press, Cambridge, MA. pdf [Movies: Figure 2 wmv / mov , 4a avi / mov , 4b avi / mov , 4c avi / mov , 4d avi / mov ]

Charles CF, Koepsell K (2009) A multivariate phase distribution and its estimation, Poster presentation, Computational and Systems Neuroscience. doi: 10.3389/conf.neuro.06.2009.03.260 abstract

Huth A, Cadieu CF, Dale CL, Weber D, Pantazis D, Darvas F, Leahy R, Simpson GV, Koepsell K (2009) Detecting functional connectivity in networks of phase-coupled neural oscillators, Poster presentation, Computational and Systems Neuroscience. doi: 10.3389/conf.neuro.06.2009.03.258 abstract

Kanerva P (2009). Hyperdimensional Computing: An introduction to computing in distributed representation with high-dimensional random vectors. Cognitive Computation 1(2): 139-159 link pdf

Koepsell K, Wang X, Vaingankar V, Wei Y, Wang Q, Rathbun DL, Usrey W, Hirsch J and Sommer FT (2009) Retinal oscillations carry visual information to cortex. Front Syst Neurosci 3:4 pdf

Lauritzen TZ, D’Esposito M, Heeger D and Silver MA. (2009) Top-down flow of visual spatial attention signals from parietal to occipital cortex. Journal of Vision, 9(13):18, 1-14. link

Ming, V. & Holt, L. (2009) Efficient coding in human auditory perception. J. Acoust. Soc. Am. 126.

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

Olshausen, B., C. Cadieu, and D.K. Warland. (2009) Learning Real and Complex Overcomplete Representations from the Statistics of Natural Images, Proc. SPIE 7446, 74460S, 2009. link

Parra LC, Beck JM, Bell AJ (2009) On the maximization of information flow between spiking neurons. Neural Computation, 21. link


Garrigues P.J., El Ghaoui L., An Homotopy Algorithm for the Lasso with Online Observations. Advances in Neural Information Processing Systems 21 (NIPS 2008). pdf

Koepsell K, Sommer FT (2008) Information transmission in oscillatory neural activity. Biological Cybernetics 99:403–416 abstract pdf

Koepsell K, Spoerhase C (2008) Neuroscience and the Study of Literature. Some Thoughts on the Possibility of Transferring Knowledge. JLT 2:2, pp. 363 – 374. link

Hromadka T, DeWeese MR, and Zador AM. Sparse representation of sounds in the unanesthetized auditory cortex. PLoS Biol. 6, 124-137 (2008). pdf

Monaci G., Sommer F. T. and Vandergheynst P., Learning Sparse Generative Models of Audiovisual Signals, Proc. of European Conf. on Signal Processing (EUSIPCO08), 2008 pdf

Rozell CJ, Johnson DH, Baraniuk RG, Olshausen BA (2008) Sparse Coding via Thresholding and Local Competition in Neural Circuits. Neural Computation, 20:2526-2563 pdf

Teeters JL, Harris KD, Millman KJ, Olshausen BA, Sommer FT (2008) Data sharing for computational neuroscience. Neuroinformatics 6:47-55 pdf

Yang Y, DeWeese MR, Otazu G, and Zador AM. Millisecond-scale differences in neural activity in auditory cortex can drive decisions. Nature Neuroscience 11, 1262-1263 (2008). pdf


Garrigues P.J. Olshausen B.A. (2007) Learning Horizontal Connections in a Sparse Coding Model of Natural Images. To appear in Advances in Neural Information Processing Systems 20 (NIPS 2007) pdf

Olshausen, B., C. Cadieu, J. Culpepper, and D.K. Warland. (2007) Bilinear Models of Natural Images, Proc. SPIE Int. Soc. Opt. Eng. 6492, 649206, February 2007. pdf

Rehn M, Sommer FT (2007) 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. pdf

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

Wang X, Wei Y, Vaingankar V, Wang Q, Koepsell K, Sommer FT, Hirsch JA (2007) 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: 339-341


Bethge M (2006) Factorial coding of natural images: how effective are linear models in removing higher-order dependencies? J. Opt. Soc. Am. A, 23(6): 1253-1268.

DeWeese MR, Zador AM (2006) Non-Gaussian membrane potential dynamics imply sparse, synchronous activity in auditory cortex. J. Neuroscience 26(47), 12206-12218 (2006). pdf

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

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


Bell A.J., Parra L.C. (2005) Maximising Sensitivity in a Spiking Network, Advances in Neural Information Processing Systems 17, Saul L.K. and Weiss Y. and Bottou L., MIT Press, Cambridge, MA pdf

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

Martinez LM, Wang Q, Reid RC, Pillai C, Alonso J-M, Sommer FT, Hirsch JA (2005) Receptive field structure varies with layer in the primary visual cortex. Nature Neuroscience 8 , 372 - 379 pdf

Olshausen BA, Field DJ (2005) How close are we to understanding V1? Neural Computation, 17, 1665-1699. pdf

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

Redwood Neuroscience Institute

An incomplete list of publications from the Redwood Neuroscience Institute (2002-2004) is available here.