Difference between revisions of "TCN"

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== Topics in Computational Neuroscience ==
 
== Topics in Computational Neuroscience ==
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===Fall 2015===
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12/17/15 - Dylan Paiton
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12/10/15 - Eric Weiss
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12/03/15 - Sean
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11/26/15 - Thanksgiving break
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11/19/15 - Eric D.
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11/12/15 - Vasha
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=== Summer 2014 ===
 
=== Summer 2014 ===

Revision as of 21:01, 12 November 2015

Topics in Computational Neuroscience

Fall 2015

12/17/15 - Dylan Paiton

12/10/15 - Eric Weiss

12/03/15 - Sean

11/26/15 - Thanksgiving break

11/19/15 - Eric D.

11/12/15 - Vasha


Summer 2014

  • [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [1]
  • [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [2]
  • [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [3]
  • [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [4]
  • [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [5]

Spring 2014

  • [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [6]

Fall 2013

  • [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [7]

Summer 2013

  • [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [8]

Spring 2013

  • [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [9] [10]
  • [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [11]
  • [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [12]
  • [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [13]
  • [Mar 6] Grady 2006 - Random Walks for Image Segmentation [14]
  • [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [15]
  • [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [16]
  • [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [17][18]
  • [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [19]

Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [20]

  • [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [21] [22]
  • [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [23]

Fall 2012

  • [Aug 22] Maass et al. - Liquid State Computing [24] [25]
  • [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [26]
  • [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [27]
  • [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [28]
  • [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [29]
  • [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [30]
  • [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [31]

Spring 2011

  • [Feb 10] Anastassiou et al. Ephaptic coupling of cortical neurons [32]
  • [Feb 10] Jin et al. Population receptive fields of ON and OFF thalamic inputs to an orientation column in visual cortex [33]
  • [Jan 20] A review of NIPS 2010 papers. [34]

Fall 2010

  • [Dec 9] Welling. Herding algorithms. [35]
  • [Dec 2] Neal. MCMC using Hamiltonian dynamics. [36]
  • [Nov 18] Mairal et al. Task-driven dictionary learning. [37]
  • [Oct 21] Hamed. Self-referential dynamical systems for the self-organization of behavior in robotic systems. Ch 2-3 of [38]
  • [Oct 14] Hammond, Vandergheynst, and Gribonval. Wavelets on graphs via spectral graph theory. [39]
  • [Oct 7] Neal. Annealed importance sampling. [40]
  • [Sep 30] Martius, Herrmann. Taming the beast: Guided self-organization of behavior in autonomous robots. [41]
  • [Sep 23] Bullier, Jean. "What is Fed Back?" in 23 Problems in Systems Neuroscience. [42]

Past TCN Papers

Time and Location

2:00-3:00pm usually every Wednesday in the Redwood Center (560 Evans). For 2013 Fall term, we are meeting every other week. 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.)

Suggestion Board