Statistical model selection for neuronal responses to stimulus pairs in visual cortex

Statistical model selection for neuronal responses to stimulus pairs in visual cortex

A fundamental question concerning the way the visual world is represented in our brain is how a cortical cell responds when its classical receptive field contains more than a single stimulus object. It is a statistically challenging problem how to infer such behavior and distinguish between different explanatory models from neurobiological data. Particular challenges are that data are partially observed, highly noisy and autocorrelated. A standard way to deal with noisy data is to average over trials. In this talk I will argue that this might blur or entirely remove essential characteristics and mechanisms, which are fundamental for understanding brain function.

Two opposing models have been proposed in the literature. In the response-averaging model [1], the firing rate of the cell to a pair of stimulus objects is a weighted average of the firing rates to the individual objects. By contrast, in the probability-mixing model [2], the cell responds to the pair of objects as if only one of the objects was present in any given trial. Here we compare the abilities of the two models to account for spike trains recorded from single cells in the middle temporal visual area (MT) of rhesus monkeys, using point process techniques. The results support the probability-mixing model [3].

References:

[1] Reynolds, J. H., Chelazzi, L. & Desimone, R. Competitive mechanisms subserve attention in macaque areas V2 and V4. Journal of Neuroscience 19, 1736–1753 (1999).

[2] Bundesen, C., Habekost, T. & Kyllingsbæk, S. A neural theory of visual attention: bridging cognition and neurophysiology. Psychological Review 112, 291 (2005).

[3] Li, K. , Kozyrev, V., Kyllingsbæk, S., Treue, S., Ditlevsen, S. and Bundesen, C.: Neurons in primate visual cortex alternate between responses to multiple stimuli in their receptive field. Submitted.