August 2010
Volume 10, Issue 7
Free
Vision Sciences Society Annual Meeting Abstract  |   August 2010
How STS recognizes actions: Predicting single-neuron responses in higher visual cortex
Author Affiliations
  • Cheston Tan
    Department of Brain & Cognitive Sciences, MIT
    McGovern Institute for Brain Research at MIT
  • Jedediah Singer
    Children's Hospital Boston, Harvard Medical School
  • Thomas Serre
    Department of Brain & Cognitive Sciences, MIT
    McGovern Institute for Brain Research at MIT
  • David Sheinberg
    Department of Neuroscience, Brown University
  • Tomaso Poggio
    Department of Brain & Cognitive Sciences, MIT
    McGovern Institute for Brain Research at MIT
Journal of Vision August 2010, Vol.10, 935. doi:10.1167/10.7.935
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to Subscribers Only
      Sign In or Create an Account ×
    • Get Citation

      Cheston Tan, Jedediah Singer, Thomas Serre, David Sheinberg, Tomaso Poggio; How STS recognizes actions: Predicting single-neuron responses in higher visual cortex. Journal of Vision 2010;10(7):935. doi: 10.1167/10.7.935.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

Computational models of visual processing make quantitative, testable predictions, and the accuracy of these predictions can be used to objectively gauge model goodness. Such quantitative validation is common at the behavioral and neural population levels. However, this is less common at the single-neuron level, partly because of difficulty in data collection, but more importantly because the large variability among neurons requires models to be specific enough to predict the response of a single neuron, yet flexible enough to be fitted to all individual neurons within the recorded population. Such validation has been done for V1 (see Olshausen & Field, 2005), V4 (Cadieu et al, 2007; David et al, 2006) and MT (Rust et al, 2006). Here, we test a model for spatio-temporal processing of action sequences in the primate superior temporal sulcus (STS). The relatively higher position of STS in the visual processing hierarchy (Felleman & Van Essen, 1991) makes this a harder task. Using computer-generated humanoid action sequences, we trained monkeys to recognize multiple actions and recorded from the temporal lobe (Singer & Sheinberg, in press). We then used computational models of the ventral (Serre et al, 2005) and dorsal (Jhuang et al, 2007) streams of visual cortex, coupled with a simple parameter-search procedure to fit either model to 100+ individual neurons, predicting the firing rate of each neuron in response to 64 action sequences, for 90 bins of duration 10ms each. We performed leave-one-out cross-validation, and both models achieved good test-set performance comparable to previous work in V4 and MT, while controls performed significantly worse and close to chance. To the best of our knowledge, this is not only the first instance of quantitative modeling for single neurons this far downstream, but also the first instance of time-series prediction for neurons beyond V1.

Tan, C. Singer, J. Serre, T. Sheinberg, D. Poggio, T. (2010). How STS recognizes actions: Predicting single-neuron responses in higher visual cortex [Abstract]. Journal of Vision, 10(7):935, 935a, http://www.journalofvision.org/content/10/7/935, doi:10.1167/10.7.935. [CrossRef]
×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×