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Inferring neural circuit structure from datasets of heterogeneous tuning curves

Fig 2

Structure of the feedforward and recurrent generative models used in our computational experiments.

(A) The feedforward network model of primary motor cortex (M1) is borrowed from Ref. [4] and produces heterogeneous hand-location tuning curves. This heterogeneity is rooted in the random network structure, including the variability in the input layer (modeling a premotor or parietal area) receptive field widths, σ_i, feedforward weights to the M1 layer, , and the threshold, ϕ, of M1 output neurons which are rectified linear units. (B) The structure of the Stabilized Supralinear Network (SSN) with one-dimensional topography (retinotopy) as a model of the primary visual cortex (V1). The SSN is a recurrent network of excitatory (E) and inhibitory (I) neurons. The visual stimulus (bottom) models the input to V1 due to a grating of diameter b_s in condition s. Heterogeneity in model output (size tuning curves) originates in the heterogeneity of feedforward and recurrent horizontal connections. The mean and variance of the horizontal connections between SSN neurons depend on the pre- and postsynaptic cell-types and their retinotopic distance, and for different connection-types falloff over different characteristic length scales. For a full description of models and their parameters see Materials and methods.

doi: https://doi.org/10.1371/journal.pcbi.1006816.g002