Inferring synaptic plasticity rules from spike counts

In last week’s computational & theoretical neuroscience journal club I presented the following paper from Nicolas Brunel’s group:

Inferring learning rules from distributions of firing rates in cortical neurons.
Lim, McKee, Woloszyn, Amit, Freedman, Sheinberg, & Brunel.
Nature Neuroscience (2015).

The paper seeks to explain experience-dependent changes in IT cortical responses in terms of an underlying synaptic plasticity rule. Continue reading

Every Neuron is Special

A couple of weeks ago I presented

A category-free neural population supports evolving demands during decision-making

by David Raposo, Matthew Kaufman and Anne Churchland.  By “categories” they are referring to some population of cells whose responses during an experiment seem to be dominated by one or two of the experimental variables. The authors refer to these types of categories as functional categories.

Continue reading

Attractors, chaos, and network dynamics for short term memory

In a recent lab meeting, I presented the following paper from Larry Abbott’s group:

From fixed points to chaos: Three models of delayed discrimination.
Barak, Sussillo, Romo, Tsodyks, & Abbott,
Progress. in Neurobiology 103:214-22 2013.

The paper seeks to connect the statistical properties of neurons in pre-frontal cortex (PFC) during short-term memory with those exhibited by several dynamical models for neural population responses. In a sense, it can be considered a follow-up to Christian Machens’ beautiful 2005 Science paper [2], which showed how a simple attractor model could support behavior in a two-interval discrimination task. The problem with the Machens/Brody/Romo account (which relied on mutual inhibition between two competing populations) is that it predicts extremely stereotyped response profiles, with all neurons in each population exhibiting the same profile. Continue reading

Lab Meeting 10/26/2011

This week, Ozan presented a recent paper from Matthias Bethge’s group:

A. S. Ecker, P. Berens, A. S. Tolias, and M. Bethge
The effect of noise correlations in populations of diversely tuned neurons
The Journal of Neuroscience, 2011

The paper describes an analysis of the effects of correlations on the coding properties of a neural population, analyzed using Fisher information. The setup is that of a 1D circular stimulus variable (e.g., orientation) encoded by a population of N neurons defined by a bank of tuning curves (specifying the mean of each neuron’s response), and a covariance matrix describing the correlation structure of additive “proportional” Gaussian noise.

The authors find that when the tuning curves are heterogeneous (i.e., not shifted copies of a single Gaussian bump), then noise correlations do not reduce Fisher information. So correlated noise is not necessarily harmful. This seems surprising in light of a bevy  of recent papers showing that the primary neural correlate of perceptual improvement (due to learning, attention, etc.) is a reduction in noise correlations. (So Matthias, what is going on??).

It’s a very well written paper, very thorough, with gorgeous figures. And I think it sets a new record for “most equations in the main body of a J. Neuroscience paper”, at least as far as I’ve ever seen. Nice job, guys!