Introduction to the theory of neural computation
Introduction to the theory of neural computation
Acetylcholine and learning in a cortical associative memory
Neural Computation
Computational differences between asymmetrical and symmetrical networks
Proceedings of the 1998 conference on Advances in neural information processing systems II
Spike-Timing-Dependent Hebbian Plasticity as Temporal Difference Learning
Neural Computation
Dynamic Brain - from Neural Spikes to Behaviors
Proceedings of the 2009 conference on Neural Nets WIRN09: Proceedings of the 19th Italian Workshop on Neural Nets, Vietri sul Mare, Salerno, Italy, May 28--30 2009
Dynamics and storage capacity of neural networks with small-world topology
Proceedings of the 2011 conference on Neural Nets WIRN10: Proceedings of the 20th Italian Workshop on Neural Nets
Frequency selectivity emerging from spike-timing-dependent plasticity
Neural Computation
Associative memory of phase-coded spatiotemporal patterns in leaky Integrate and Fire networks
Journal of Computational Neuroscience
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We introduce a model of generalized Hebbian learning and retrieval in oscillatory neural networks modeling cortical areas such as hippocampus and olfactory cortex. Recent experiments have shown that synaptic plasticity depends on spike timing, especially on synapses from excitatory pyramidal cells, in hippocampus, and in sensory and cerebellar cortex. Here we study how such plasticity can be used to form memories and input representations when the neural dynamics are oscillatory, as is common in the brain (particularly in the hippocampus and olfactory cortex). Learning is assumed to occur in a phase of neural plasticity, in which the network is clamped to external teaching signals. By suitable manipulation of the nonlinearity of the neurons or the oscillation frequencies during learning, the model can be made, in a retrieval phase, either to categorize new inputs or to map them, in a continuous fashion, onto the space spanned by the imprinted patterns. We identify the first of these possibilities with the function of olfactory cortex and the second with the observed response characteristics of place cells in hippocampus. We investigate both kinds of networks analytically and by computer simulations, and we link the models with experimental findings, exploring, in particular, how the spike timing dependence of the synaptic plasticity constrains the computational function of the network and vice versa.