Browse all publications by topic
Browse all publications by year
- L. Schwabe and
K. Obermayer. Adaptivity of Tuning Functions in a Generic Recurrent
Network Model of a Cortical Hypercolumn.
.
J. Neurosci., 25:3323-3332, 2005.
(FTP PDF, 524 kb)
The representation of orientation information in the adult visual
cortex is plastic as exemplified by phenomena such as perceptual learning or
attention. Although these phenomena operate on different time scales and give
rise to different changes in the response properties of neurons, both lead to
an improvement in visual discrimination or detection tasks. If, however,
optimal performance is indeed the goal, the question arises as to why the
changes in neuronal response properties are so different. Here, we
hypothesize that these differences arise naturally if optimal performance is
achieved by means of different mechanisms. To evaluate this hypothesis, we
set up a recurrent network model of a visual cortical hypercolumn and asked
how each of four different parameter sets (strength of afferent and recurrent
synapses, neuronal gains, and additive background inputs) must be changed to
optimally improve the encoding accuracy of a particular set of visual
stimuli.Wefind that the predicted changes in the population responses and the
tuning functions were different for each set of parameters, hence were
strongly dependent on the plasticity mechanism that was operative. An optimal
change in the strength of the recurrent connections, for example, led to
changes in the response properties that are similar to the changes observed
in perceptual learning experiments. An optimal change in the neuronal gains
led to changes mimicking neural effects of attention. Assuming the validity
of the optimal encoding hypothesis, these model predictions can be used to
disentangle the mechanisms of perceptual learning, attention, and other
adaptation phenomena.
|
