Synchronization of optically coupled neural-like oscillators

Stochastic Memristive Interface for Neural Signal Processing

“…In order to simulate neural dynamics, we explored two FHN neuron generators with cubic nonlinearity constructed using diodes [ 7 , 22 ]. The dynamics of the presynaptic FHN neuron was modeled by the normalized equations obtained with the Kirchhoff law [ 21 ] as follows: where u 1 is the membrane potential of the presynaptic neuron, ν 1 is the “recovery” variable related to the ion current, f ( u 1 ) = u 1 u 1 3 /3 is the cubic nonlinearity, I 1 is the depolarization parameter characterizing the excitation threshold, and ε is a small coefficient.…”

“…The design of compact neuromorphic systems, including micro- and nanochips, capable of reproducing information and computational functions of brain cells is a great challenge of modern science and technology. Such systems are of interest for both fundamental research in the field of nonlinear dynamics and the synchronization of complex systems [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ], as well as medical applications in the devices for monitoring and stimulating brain activity in the framework of neuroprosthetic tasks [ 8 , 9 , 10 ]. Due to their importance, memristive devices have recently become the subject of intense research, especially in the area of neuromorphic and neurohybrid applications [ 11 , 12 , 13 , 14 , 15 , 16 , 17 ].…”

“…Such artificial synapses were implemented as electronic circuits that convert pulses of presynaptic voltage into postsynaptic currents with some synaptic amplification. Different strategies were used for the hardware implementation of synaptic circuits, e.g., an optical interface between electronic neurons [ 4 , 5 , 7 ].…”

Optoelectronic system for brain neuronal network stimulation

“…This neurointerface operates as follows ( Fig 1 ). The electronic neuron based on the FHN circuit [ 19 , 28 ] ( Fig 2 ) modulates the intensity of LED emission. It should be noted that the dynamics of the FHN neuron qualitatively represents the main features of a living neuron, namely, the presence of an excitability threshold, and resting and spiking behaviors.…”

“…They are distinguished by their biological relevance, a number of simulated ion channels and a number of represented dynamic modes [ 11 – 18 ]. Collective dynamics of artificial neurons were also extensively studied [ 19 – 22 ] with concentrated efforts in modelling and implementing synaptic connections in hardware. It should be noted that synaptic plasticity is one of the most sophisticated and challenging features of neuronal network dynamics to be implemented in hardware.…”