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Abstract
Memristive systems have gained considerable attention in the field of neuromorphic engineering, because they allow the emulation of synaptic functionality in solid state nano-physical systems. In this study, we show that memristive behavior provides a broad working framework for the phenomenological modelling of cellular synaptic mechanisms. In particular, we seek to understand how close a memristive system can account for the biological realism. The basic characteristics of memristive systems, i.e. voltage and memory behavior, are used to derive a voltage-based plasticity rule. We show that this model is suitable to account for a variety of electrophysiology plasticity data. Furthermore, we incorporate the plasticity model into an all-to-all connecting network scheme. Motivated by the auto-associative CA3 network of the hippocampus, we show that the implemented network allows the discrimination and processing of mnemonic pattern information, i.e. the formation of functional bidirectional connections resulting in the formation of local receptive fields. Since the presented plasticity model can be applied to real memristive devices as well, the presented theoretical framework can support both, the design of appropriate memristive devices for neuromorphic computing and the development of complex neuromorphic networks, which account for the specific advantage of memristive devices.
Highlights
Synaptic plasticity in the excitability between neurons results from an increase or reduction of the strength of synaptic connections and contributes to neuroplasticity
We focus on synaptic plasticity measurements and extent our former model to a more neurobiologically relevant framework: we would like to address the question of how far the plasticity model accounts for experimental electrophysiological data
Experimental investigation on cells from the hippocampal CA1 slices found that the induction of long-term potentiation (LTP) and long-term depression (LTD) strongly depends on the variation of the level of post-synaptic polarization[28,29]
Summary
Synaptic plasticity in the excitability between neurons results from an increase or reduction of the strength of synaptic connections and contributes to neuroplasticity. A further advantage is that voltage-based STDP models belong to the class of phenomenological models which aim to provide a minimal description of the principal phenomena observed in electrophysiological investigations Such an approach has the advantage of reducing the complexity of the system to a few key parameters and allows testing the compatibility of model parameters with experimental synaptic plasticity data (an overview of model classification can find in ref.[9]). Using models of neuronal all-to-all connecting networks and neurobiological hippocampal circuits, we extend our findings of synaptic plasticity from the cellular to the network level At this respect, principles of the discrimination and processing of mnemonic pattern information in the auto-associative CA3 network of the hippocampus are emulated and discussed. In detail, based on biological data we show evidence that our network model is capable of learning by the discrimination and completion of similar input patterns (pattern separation or completion) and by the formation of functional bidirectional connections resulting in the formation of local receptive fields in auto-associative CA3 networks
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