2021, Articolo in rivista, ENG
Calandra Sebastianella G.; Di Lauro M.; Murgia M.; Bianchi M.; Carli S.; Zoli M.; Fadiga L.; Biscarini F.
Organic neuromorphic devices mimic signal processing features of biological synapses, with short-term plasticity, STP, modulated by the frequency of the input voltage pulses. Here, an artificial synapse, made of intracortical microelectrodes, is demonstrated that exhibits either depressive or facilitative STP. The crossover between the two STP regimes is controlled by the frequency of the input voltage. STP features are described with an equivalent circuit where an inductance component is introduced in parallel with the RC circuit associated with poly(3,4-ethylenedioxythiophene)/polystyrene sulfonate (PEDOT/PSS)||electrolyte interface. The proposed RLC circuit explains the physical origin of the observed STP and its two timescales in terms of charge build up in PEDOT/PSS.
2019, Articolo in rivista, ENG
Juzekaeva E.; Nasretdinov A.; Battistoni S.; Berzina T.; Iannotta S.; Khazipov R.; Erokhin V.; Mukhtarov M.
Functional coupling live neurons through artificial synapses is the primary requirement for their implementation as prosthetic devices or in building hybrid networks. Here, the first evidence of unidirectional, activity dependent, coupling of two live neurons in brain slices via organic memristive devices (OMD) is demonstrated. ODM is a polymeric electrochemical element, which has two terminals for the connection in electrical circuits and which displays hysteresis and rectifying features. OMD coupling is characterized by nonlinear relationships determined by the instantaneous values of OMD resistance that can be controlled by the neuronal activity, and the excitation threshold in the postsynaptic neuron. OMD coupling also has the spike-timing features similar to that of the natural excitatory synapses. Also, OMD-synapses support synchronized delta-oscillations in the two-neuron network. It is proposed that OMD-synapses may enable realization of prosthetic synapses and building hybrid neuronal networks endowed with a capacity of learning, memory, and computation.