Synaptic Function in Memristor Devices for Neuromorphic Circuit Applications

The realization of artificial neural circuits requires synaptic materials and devices that show adaptation at different time scales to modulate signal transmission between neurons according to the desired applications. Sensory-motor and intelligence operation in the brain relies on the dynamical pro...

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Detalles Bibliográficos
Autores: Bisquert, Juan, Shim, Wooyoung, Kim, So Yeon, Linares-Barranco, Bernabé
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/402251
Acceso en línea:http://hdl.handle.net/10261/402251
https://api.elsevier.com/content/abstract/scopus_id/105008442460
Access Level:acceso abierto
Palabra clave:Memristors
Neuromorphic computing
Synapses
Descripción
Sumario:The realization of artificial neural circuits requires synaptic materials and devices that show adaptation at different time scales to modulate signal transmission between neurons according to the desired applications. Sensory-motor and intelligence operation in the brain relies on the dynamical properties of synapses that adapt to the frequency and synchronization of voltage spikes. The properties of potentiation and depression of the synapse conductivity control the plasticity and adaptation of synapses. Here, the general dynamical properties of ionic or electronic current conduction that form the main rules of synaptic activity are discussed. The basic model requirements of a memristor or chemical inductor to produce an adaptation of conductance to incoming stimuli are established. The synaptic response can be described by the combination of three factors: A conduction process that depends on an internal state variable x; this variable causes rectification at an onset voltage; it also causes a memory, characterized by a delay in response to the stimulus. Diverse diagnosis methods are described that connect the nonlinear time response, the nonlinear cycling of current–voltage curves, and the linear frequency response of impedance spectroscopy, to assess the adaptation properties.