A transition to self-sustained current oscillations was investigated in a circuit with NbOx thin film device, acting as an artificial neuron. Above threshold of the applied voltage, begins demonstrate persistent but rather random spiking, which becomes more regular increasing voltage. Experimental measurements reveal two scaling regions interspike interval dependence on source voltage: initial ...

Multistable dynamical systems have important applications as pattern recognition and memory storage devices. Conditions under which time-delayed recurrent loops of spiking neurons exhibit multistability are presented. Our results are illustrated on both a simple integrate-and-fire neuron and a HodgkinHuxley-type neuron, whose recurrent inputs are delayed versions of their output spike trains. T...

Objective Purkinje neuron dysfunction is associated with cerebellar ataxia. In a mouse model of spinocerebellar ataxia type 1 (SCA1), reduced potassium channel function contributes to altered membrane excitability resulting in impaired Purkinje neuron spiking. We sought to determine the relationship between altered membrane excitability and motor dysfunction in SCA1 mice. Methods Patch-clamp ...

Spiking neural network is a type of artificial neural network in which neurons communicate between each other with spikes. Spikes are identical Boolean events characterized by the time of their arrival. A spiking neuron has internal dynamics and responds to the history of inputs as opposed to the current inputs only. Because of such properties a spiking neural network has rich intrinsic capabil...

A silicon neuron circuit that produces spiking and bursting firing patterns, with biologically plausible spike shape, is presented. The circuit mimics the behaviour of known classes of cortical neurons: regular spiking (RS), fast spiking (FS), chattering (CH) and intrinsic bursting (IB). The paper describes the operation of the circuit, provides simulation results, a simplified analytical model...

In this work, we propose a variation of a direct reinforcement learning algorithm, suitable for usage with spiking neurons based on the spike response model (SRM). The SRM is a biologically inspired, flexible model of spiking neuron based on kernel functions that describe the effect of spike reception and emission on the membrane potential of the neuron. In our experiments, the spikes emitted b...