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 3/25/2013: Demixing PCA (dPCA)

If you had lots of spike trains over 4 seconds for 800 neurons, 6 stimulus conditions, and 2 behavioral choices, how would you visualize your data? Unsupervised dimensionality reduction techniques, such as principal component analysis (PCA) finds orthonormal basis vectors that captures the most variance of the data, but the results are not necessarily interpretable. What one wants is to say is something like:

“Along this direction, the population dynamics seems to encode stimulus, and along this other orthogonal dimension, neurons are modulated by the motor behavior…”

Continue reading