Dimensions of variability in circuit models of cortex

Presented at: Neuroscience 2021
C.E. Credits: P.A.C.E. CE Florida CE


Cortical circuits often receive multiple inputs from upstream populations with non-overlapping stimulus tuning. Both the feedforward and recurrent architectures of the receiving cortical layer reflect input tuning diversity. We study how population-wide neuronal variability propagates through a hierarchical cortical network receiving multiple independent tuned inputs. Our new analysis of in vivo neural data from the primate visual system shows that the number of latent variables (dimensions) needed to describe population shared variability is smaller in V4 than in downstream visual area PFC.  We successfully reproduce the dimensionality expansion observed across brain areas using a multi-layer spiking network with structured feedforward projections and recurrent assemblies of multiple, tuned neuron populations. We show that tuning-structured connectivity generates attractor dynamics within the model PFC activity, where attractor competition expands the dimensions of shared variance. Restricting dimensionality analysis to activity from one attractor state recovers the low-dimensional structure inherited from each of our tuned inputs. Our model thus introduces a framework where high-dimensional cortical variability is understood as alternating states of low-dimensional, tuning-specific circuit dynamics.


Learning Objectives:

1. Measure the space in which population-wide neuronal fluctuations inhabit in both visual area V4 and the prefrontal cortex (PFC).

2. Build a circuit based model that captures how neuronal variability is transferred from V4 to PFC.

3. Using our model, build a theoretical framework that predicts how the recurrent circuitry within PFC supports an expansion of the dimensions of variability compared to V4.

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