Formal and Attribute-Specific Information

Formal and attribute-specific information in primary visual cortex

Daniel S. Reich, Ferenc Mechler, and Jonathan D. Victor

J. Neurophysiol. 85, 305-318 (2001)

Abstract

We measure the rates at which neurons in the primay visual cortex (V1) of anesthetized macaque monkeys transmit stimulu-related information in response to three types of visual stimulus. The stimuli-randomly modulated checkerboard patterns, stationary sinusoidal gratings, and drifting sinusoidal gratings-have very different spatiotemporal structures.We obtain the overall rate of information transmission, which we call "formal" information, by the "direct method" (de Ruyter van Steveninck et al. 1997).We find the highest information rates in the responses of simple cells to drifting gratings (median:10.3bits/sec,0.92bits/spike); responses to randomly modulated stimuli and stationary gratings transmit information at significantly lower rates. In general, simple cells transmit information at higher rates, and over a larger range, than complex cells. Thus, in the responses of V1 neurons, stimuli that are rapidly modulated do not necessarily evoke higher information rates, as might be the case with motion-sensitive neurons in area MT (Buracus et al. 1998). By extension of the direct method, we parse the formal information into "attribute-specific" components, which provide a measure of the information transmitted about contrast and spatiotemporal pattern. We find that contrast-specific information varies across neurons-about 0.3 to 2.1 bits/sec or 0.005 to 0.22 bits/spike-but depends little on the stimulus type. Spatiotemporal pattern-specific information, however, depends strongly on the type of stimulus and neuron (simple or complex). The remaining information rate, typically between 10% and 32% of the formal information rate for each neuron, cannot be unambiguously assigned to either contrast or spatiotemporal pattern. This indicates that some information concerning these two stimulus attributes is confounded in the responses of single neurons in V1. A model that considers a simple cell to consist of a linear spatiotemporal filter followed by a static rectifier predicts higher information rates than are found in real neurons and completely fails to account for the confounded information.


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