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Abstract:
Recent biological experimental findings have shown that the
synaptic plasticity depends on the relative timing of the pre-
and post-synaptic spikes which determines whether Long Term
Potentiation (LTP) occurs or Long Term Depression (LTD) does. The
synaptic plasticity has been called ``Temporally Asymmetric
Hebbian plasticity (TAH)''. Many authors have numerically shown
that spatiotemporal patterns can be stored in neural networks.
However, the mathematical mechanism for storage of the
spatio-temporal patterns is still unknown, especially the effects
of LTD. In this paper, we employ a simple neural network model
and show that interference of LTP and LTD disappears in a sparse
coding scheme. On the other hand, it is known that the covariance
learning is indispensable for storing sparse patterns. We also
show that TAH qualitatively has the same effect as the covariance
learning when spatio-temporal patterns are embedded in the
network.
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