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Dynamics of population activity in visual cortex

Matteo Carandini

UCL Institute of Ophthalmology

The primary visual cortex (V1) codes fundamental attributes of visual
stimuli, representing them in the coordinated and dynamically-changing
activity of populations of neurons. Does this representation follow
simple mathematical rules? Are these rules stable or do they change
according to stimulus properties such as a recent history, strength,
or configuration?

We addressed these questions in a series of experiments performed in
anesthetized cats, where we recorded from populations of V1 neurons
using multielectrode arrays. Stimuli were rapid sequences of flashed
gratings varying in orientation, drifting gratings of various
orientations and contrasts, and plaids obtained by summing two such

In response to sequences of single gratings, the cortex adopts a very
simple coding scheme, with a stereotyped response that is simply
applied over and over to different groups of neurons depending on the
stimulus orientation. Responses to subsequent stimuli simply sum to
each other, thus forming a representation that is easily decoded.

This basic linear representation, however, is only the scaffolding for
more complex, nonlinear operations that make the cortex extremely
adaptive. First, when stimuli are followed by blank stimuli, the
responses persist longer than expected, leaving a slightly longer
memory trace of the stimulus than could have been possible from a
purely linear operation. Second, when stimuli are varied in contrast,
the cortical responses are scaled in a nonlinear way, without
disrupting the ratios of firing rate across neurons that participate
in the response. Third, in response to sums of stimuli of different
contrast, the cortex engages in a range of behaviors spanning the
gamut from simple summation to winner-take-all competition; this range
of behaviors is simply explained by normalization operations that
profoundly affect the outcome of linear processing. Fourth, the cortex
shows a marked ability to adapt to the statistics of the stimuli: it
changes the selectivity and responsivity of neurons just as needed to
counteract any nonuniformities in the recent history of stimulation.

We have great hope and reasonable expectation that the rules that we
have uncovered are not specific to area V1 but are rather general
rules of operation of cortical populations, and therefore may act as
guide to research in the underlying mechanisms and circuits and to
research in the fundamental neural computations that lead to
perception and behavior.