Spatiotemporal learning in analog neural networks using spike-timing-dependent synaptic plasticity.

Incorporating the spike-timing-dependent synaptic plasticity (STDP) into a learning rule, we study spatiotemporal learning in analog neural networks. First, we study learning of a finite number of periodic spatiotemporal patterns by deriving the dynamics of the order parameters. When a pattern is retrieved successfully, the order parameters exhibit periodic oscillation.

Learning of oscillatory correlated patterns in a cortical network by a STDP-based learning rule.

In this paper, we propose an iterative learning rule that allows the imprinting of correlated oscillatory patterns in a model of the hippocampus able to work as an associative memory for oscillatory spatio-temporal patterns. We analyze the dynamics in the Fourier domain, showing how the network selectively amplify or distort the Fourier components of the input, in a manner which depends on the imprinted patterns. We also prove that the proposed iterative local rule converges to the pseudo-inverse rule generalized to oscillatory patterns.

A learning rule for place fields in a cortical model: theta phase precession as a network effect.

We show that a model of the hippocampus introduced recently by Scarpetta et al. (2002, Neural Computation 14(10):2371-2396) explains the theta phase precession phenomena. In our model, the theta phase precession comes out as a consequence of the associative-memory-like network dynamics, i.

Effects of noise in a cortical neural model.

Recently Phys. Rev. E 64, 011920 (2001)]; Phys.