TCN
Topics in Computational Neuroscience
For ideas about some interesting papers to discuss have look here TCN Paper Ideas
Spring 2016
[May 18] Karl Zipser
[May 11] Eric Dodds
[May 04] Chris Warner
[Apr 27] Mr. (Alex) Anderson
[Apr 20] Dylan Paiton
[Apr 13] Kohta Ishikawa
[Apr 06] Alejandro Bujan
[Mar 30] Guy Isley - R Chaudhuri, A Bernacchia, XJ Wang (2014). A Diversity of Localized Timescales in Network Activity [1]
[Mar 23] Brian Cheung - DJ Rezende, S Mohamed, I Danihelka, K Gregor, D Wierstra (2016). One-Shot Generalization in Deep Generative Models [2]
[Mar 16] Yubei Chen - A Beck, M Teboulle (2009). A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems [3]
[Mar 09] Sean Mackesey - P Fries (2015). Rhythms for Cognition: Communication Through Coherence [4]
[Mar 02] Charles Garfinkle - BB Ujfalussy, JK Makara, T Branco, M Lengyel (2015). Dendritic Nonlinearities are Tuned for Efficient Spike-Based Computations in Cortical Circuits [5]
[Feb 24] Eric Weiss - TS Lee, D Mumford (2003). Hierarchical Bayesian Inference in the Visual Cortex [6]
[Feb 17] Paxon Frady - C Eliasmith et al. (2012). A Large-Scale Model of the Functioning Brain [7]
[Feb 10] Cancelled - EECS Colloquium
[Feb 03] Charles Frye - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [8]
[Jan 21] Jesse Livezy and Andrew Berger - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [9]
[Jan 14] Daniel Toker - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [10]
Fall 2015
[Dec 17] Charles Frye - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [11]
[Dec 03] Omer Hazon - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [12]
[Nov 26] Thanksgiving break
[Nov 19] Eric Dodds - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [13]
Nov 12] Vasha Dutell - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [14]
Summer 2014
- [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [15]
- [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [16]
- [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [17]
- [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [18]
- [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [19]
Spring 2014
- [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [20]
Fall 2013
- [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [21]
Summer 2013
- [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [22]
Spring 2013
- [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [23] [24]
- [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [25]
- [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [26]
- [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [27]
- [Mar 6] Grady 2006 - Random Walks for Image Segmentation [28]
- [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [29]
- [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [30]
- [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [31][32]
- [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [33]
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [34]
- [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [35] [36]
- [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [37]
Fall 2012
- [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [40]
- [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [41]
- [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [42]
- [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [43]
- [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [44]
- [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [45]
Spring 2011
- [Feb 10] Anastassiou et al. Ephaptic coupling of cortical neurons [46]
- [Feb 10] Jin et al. Population receptive fields of ON and OFF thalamic inputs to an orientation column in visual cortex [47]
- [Jan 20] A review of NIPS 2010 papers. [48]
Fall 2010
- [Dec 9] Welling. Herding algorithms. [49]
- [Dec 2] Neal. MCMC using Hamiltonian dynamics. [50]
- [Nov 18] Mairal et al. Task-driven dictionary learning. [51]
- [Oct 21] Hamed. Self-referential dynamical systems for the self-organization of behavior in robotic systems. Ch 2-3 of [52]
- [Oct 14] Hammond, Vandergheynst, and Gribonval. Wavelets on graphs via spectral graph theory. [53]
- [Oct 7] Neal. Annealed importance sampling. [54]
- [Sep 30] Martius, Herrmann. Taming the beast: Guided self-organization of behavior in autonomous robots. [55]
- [Sep 23] Bullier, Jean. "What is Fed Back?" in 23 Problems in Systems Neuroscience. [56]
Past TCN Papers
Time and Location
12:00-1:00pm every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.
Overview
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.
If you have questions, please email the club organizer James Arnemann.
E-mail List
To subscribe to the journal club email list, visit link. You will receive emails twice a week about papers that will be covered in the next meeting.
Guidelines for Presenting Papers
Each person that selects a paper should present, in about 15-30 minutes:
- an executive summary
- an outline of the key points, ideas, or contributions
- relevant background information
- a description of the key figures
- what you took away from the paper
- some potential questions for discussion
- you are encouraged to use whatever method to present (slides, puppets, etc.)
