In simulating realistic neuronal circuitry composed of diverse types of neurons, we need an elemental spiking neuron model that is capable of not only quantitatively reproducing spike times of biological neurons given in vivo-like fluctuating inputs, but also qualitatively representing a variety of firing responses to transient current inputs. Simplistic models based on leaky integrate-and-fire...

A design for a spiking neuron with dynamical synapses is presented. Both the neuron and synapse are intended for a large scale neural network connected through an AER bus. The neuron design is a standard integrate-and-fire model with the appropriate interfacing circuits. The synapse, however, models several features from biology including a conductance, between the membrane voltage and synaptic...

Sensors are an essential element in a wide range of applications. As the number sensors increases, so does amount data collected with them. This raises challenge efficiently processing this data. Spiking Neural Networks (SNNs) represents promising approach to solve problem through event-based, parallelized processing. For SNNs be genuinely efficient, some fundamental challenges arise, like conv...

Spike-timing-dependent plasticity (STDP) has been observed in many brain areas such as sensory cortices, where it is hypothesized to structure synaptic connections between neurons. Previous studies have demonstrated how STDP can capture spiking information at short timescales using specific input configurations, such as coincident spiking, spike patterns and oscillatory spike trains. However, t...

The Hindmarsh-Rose (HR) model could describe different discharge property of an excitatory or inhibitory neuron by changing the parameter r. In this paper, HR model is used to be the dynamical equations of the spiking model neurons, and different neurons in one neuronal population are connected with WS small-world network. A neurons spiking model in the hippocampus CA3 based on small-world netw...

We propose a digital neuron model suitable for evolving and growing heterogeneous spiking neural networks on FPGAs using a piecewise linear approximation of the Quadratic Integrate and Fire (QIF) model. A network of 161 neurons and 1610 synapses with 4210 times realtime neuron simulation speed was simulated and synthesized for a Virtex-5 chip.

Spiking Neuron Networks (SNNs) overcome the computational power of neural networks made of thresholds or sigmoidal units. Indeed, SNNs add a new dimension, the temporal axis, to the representation capacity and the processing abilities of neural networks. In this paper, we present how SNN can be applied with efficacy for cell microscopic image segmentation. Results obtained confirm the validity ...

Neurons receive thousands of presynaptic input spike trains while emitting a single output spike train. This drastic dimensionality reduction suggests considering a neuron as a bottleneck for information transmission. Extending recent results, we propose a simple learning rule for the weights of spiking neurons derived from the information bottleneck (IB) framework that minimizes the loss of re...

A biologically inspired neuronal network that stores and recognizes temporal sequences of symbols is described. Each symbol is represented by excitatory input to distinct groups of neurons (symbol pools). Unambiguous storage of multiple sequences with common subsequences is ensured by partitioning each symbol pool into subpools that respond only when the current symbol has been preceded by a pa...