Abstracting spatial prototypes through short-term suppression of Hebbian weights in
a continuously changig environment
S. Tavitian, T. Fomin and A. Lõrincz
Neural Network World
in press (1997)
Abstract
A step towards assumption-free self-organization is proposed. We address the
problem of learning a stable representation of the environment from inputs
continuously changing at an unpredictable rate. The vulnerability of
competitive Hebbian learning to low rate changes is assessed. It is shown that
anti-Hebbian suppression of the feed-forward Hebbian weights broadens the range
of rates in which learning is possible, and reduces the influence of the rate
on the emerging representation. The resulting robustness during real-time
training is demonstrated through simulations, and is compared to an alternative
non-synaptic suppression scheme. Some particular passive short-term response
properties of high-level visual areas are pointed out as the biological clues
for this form of short-term plasticity. The question is raised about a
possible stabilizing role of the proposed mechanism in the learning of
invariances.
