introduction: (s)- 3,5-dihydroxyphenylglycine (dhpg) is an agonist for group i metabotropic glutamate receptors. dhpg-induced synaptic depression of excitatory synapses on hippocampal pyramidal neurons is well known model for synaptic plasticity studies. the aim of the present study was to examine the effects of dhpg superfusion on excitatory synapses on pyramidal and fast-spiking gabaergic cel...

Abstract Artificial spiking neural networks have found applications in areas where the temporal nature of activation offers an advantage, such as time series prediction and signal processing. To improve their efficiency, architectures often run on custom-designed neuromorphic hardware, but, despite attractive properties, these implementations been limited to digital systems. We describe artific...

BACKGROUND Biological neuron models mainly analyze the behavior of neural networks. Neurons are described in terms of firing rates viz an analog signal. PURPOSE The Izhikevich neuron model is an efficient, powerful model of spiking neuron. This model is a reduction of Hodgkin-Huxley model to a two variable system and is capable of producing rich firing patterns for many biological neurons. ...

Spiking neuron models can accurately predict the response of neurons to somatically injected currents if the model parameters are carefully tuned. Predicting the response of in-vivo neurons responding to natural stimuli presents a far more challenging modeling problem. In this study, an algorithm is presented for parameter estimation of spiking neuron models. The algorithm is a hybrid evolution...

In a previous work (Mohemmed et al., Method for training a spiking neuron to associate input–output spike trains) [1] we have proposed a supervised learning algorithm based on temporal coding to train a spiking neuron to associate input spatiotemporal spike patterns to desired output spike patterns. The algorithm is based on the conversion of spike trains into analogue signals and the applicati...

In this paper we review some of our recent results on discrete-state spiking neuron models. The discrete-state spiking neuron model is a wired system of shift registers and can generate various spike-trains by adjusting the pattern of the wirings. In this paper we show basic relations between the wiring pattern and characteristics of the spike-train. We also show a learning algorithm which util...

In this paper we review some of our recent results on discrete-state spiking neuron models. The discrete-state spiking neuron model is a wired system of shift registers and can generate various spike-trains by adjusting the pattern of the wirings. In this paper we show basic relations between the wiring pattern and characteristics of the spike-train. We also show a learning algorithm which util...