We have examined a role of dynamic synapses in the stochastic Hop eldlike network behavior. Our results demonstrate an appearance of a novel phase characterized by quick transitions from one memory state to another. The network is able to retrieve memorized patterns corresponding to classical ferromagnetic states but switches between memorized patterns with an intermittent type of behavior. This phenomeno n might reect the exibility of real neural systems and their readiness to receive and respond to novel and changing external stimuli.

We compute the capacity of a binary neural network with dynamic depressing synapses to store and retrieve an infinite number of patterns. We use a biologically motivated model of synaptic depression and a standard mean-field approach. We find that at T=0 the critical storage capacity decreases with the degree of the depression. We confirm the validity of our main mean-field results with numerical simulations.

Recent experimental findings show that the efficacy of transmission in cortical synapses depends on presynaptic activity. In most neural models, however, the synapses are regarded as static entities where this dependence is not included. We study the role of activity-dependent (dynamic) synapses in neuronal responses to temporal patterns of afferent activity. Our results demonstrate that, for suitably chosen threshold values, dynamic synapses are capable of coincidence detection (CD) over a much larger range of frequencies than static synapses. The phenomenon appears to be valid for an integrate-and-fire as well as a Hodgkin-Huxley neuron and various types of CD tasks.

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Abstract.Using a biologically motivated model of synaptic depression and within a mean-field approach, we examined the role of synaptic depression in the capacity of a binary neural network with N units to store and retrieve P patterns. In the limit of α ≡ P/N → 0, our results demonstrate the appearance of a novel phase characterized by quick transitions from one memory state to another. This phenomenon might reflect the flexibility of real neural systems to receive and respond to novel and changing external stimuli. In addition, we have computed the maximum storage capacity of such a network in the limit of α = 0 and T = 0. Supported by mean-field results and Monte Carlo simulations, we concluded that the critical storage capacity for effective retrieval of stable memory patterns decreases with the degree of the depression. Nevertheless, the storage of memories as oscillatory states will require a different definition of storage capacity. How such a new storage capacity depends on the synaptic depression is still an open question.