N. Matsumoto, M. Okada
Recent biological experimental (cid:12)ndings have shown that the synap- tic 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 plas- ticity (TAH)". Many authors have numerically shown that spatio- temporal patterns can be stored in neural networks. However, the mathematical mechanism for storage of the spatio-temporal pat- terns is still unknown, especially the e(cid:11)ects of LTD. In this paper, we employ a simple neural network model and show that inter- ference of LTP and LTD disappears in a sparse coding scheme. On the other hand, it is known that the covariance learning is in- dispensable for storing sparse patterns. We also show that TAH qualitatively has the same e(cid:11)ect as the covariance learning when spatio-temporal patterns are embedded in the network.