|
Abstract:
Neocortical circuits are dominated by massive excitatory
feedback: more than eighty percent of the synapses made by
excitatory cortical neurons are onto other excitatory cortical
neurons. Why is there such massive recurrent excitation in the
neocortex and what is its role in cortical computation? Recent
neurophysiological experiments have shown that the plasticity of
recurrent neocortical synapses is governed by a temporally
asymmetric Hebbian learning rule. We describe how such a rule may
allow the cortex to modify recurrent synapses for prediction of
input sequences. The goal is to predict the next cortical input
from the recent past based on previous experience of similar input
sequences. We show that a temporal difference learning rule for
prediction used in conjunction with dendritic back-propagating
action potentials reproduces the temporally asymmetric Hebbian
plasticity observed physiologically. Biophysical simulations
demonstrate that a network of cortical neurons can learn to predict
moving stimuli and develop direction selective responses as a
consequence of learning. The space-time response properties of
model neurons are shown to be similar to those of direction
selective cells in alert monkey V1.
|
