Spatiotemporal elements of macaque V1 receptive fields

N C Rust, O Schwartz, J A Movshon, and E P Simoncelli

Published in Neuron, vol.46(6), pp. 945--956, Jun 2005.
© Cell Press, Elsevier Inc.
DOI: 10.1016/j.neuron.2005.05.021

Download:
  • Reprint (pdf)

  • Neurons in primary visual cortex (V1) are commonly classified as simple or complex based upon their sensitivity to the sign of stimulus contrast. The responses of both cell types can be described by a general model in which the outputs of a set of linear filters are nonlinearly combined. We estimated the model for a population of V1 neurons by analyzing the mean and covariance of the spatiotemporal distribution of random bar stimuli that were associated with spikes. This analysis reveals an unsuspected richness of neuronal computation within V1. Specifically, simple and complex cell responses are best described using more linear filters than the one or two found in standard models. Many filters revealed by the model contribute suppressive signals that appear to have a predominantly divisive influence on neuronal firing. Suppressive signals are especially potent in direction-selective cells, where they reduce responses to stimuli moving in the non-preferred direction.
    Related:
  • Early versions of this work: cns-03Spike-triggered Characterization of Excitatory and Suppressive Stimulus Dimensions in Monkey V1
    by N C Rust, O Schwartz, J A Movshon, and E P Simoncelli
    , sfn-03An analysis of spike-triggered covariance reveals suppressive mechanisms of directional selectivity in macaque V1 neurons
    by N Rust, O Schwartz, E P Simoncelli, and J A Movshon
    , cosyne-04Unexpected Spatio-temporal Structure in V1 Simple and Complex Cells Revealed by Spike-triggered Covariance
    by N C Rust, O Schwartz, E P Simoncelli, and J A Movshon
  • A more general book chapter on characterizing neural responses: Gazzaniga-03Characterization of neural responses with stochastic stimuli
    by E P Simoncelli, J Pillow, L Paninski, and O Schwartz
  • Spike-triggered covariance analysis of suppressive responses in retina: nips-01Characterizing neural gain control using spike-triggered covariance
    by O Schwartz, E J Chichilnisky, and E P Simoncelli
  • Fitting retinal ganglion cell responses with a generalized integrate-and-fire model: JN-05Prediction and decoding of retinal responses with a probabilistic spiking model
    by J W Pillow, L Paninski, V J Uzell, E P Simoncelli, and E J Chichilnisky
  • Online Publications