Analog VLSI and neural systems
Analog VLSI and neural systems
Retinomorphic vision systems: reverse engineering the vertebrate retina
Retinomorphic vision systems: reverse engineering the vertebrate retina
Neural networks with dynamic synapses
Neural Computation
Reading neuronal synchrony with depressing synapses
Neural Computation
Chaotic balanced state in a model of cortical circuits
Neural Computation
Computing and learning with dynamic synapses
Pulsed neural networks
The Retinomorphic Approach: Pixel-Parallel Adaptive Amplification,Filtering, and Quantization
Analog Integrated Circuits and Signal Processing
Modeling short-term synaptic depression in Silicon
Neural Computation
Neural Systems as Nonlinear Filters
Neural Computation
Modeling Selective Attention Using a Neuromorphic Analog VLSI Device
Neural Computation
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Short-term dynamical synapses increase the computational power of neuronal networks. These synapses act as additional filters to the inputs of a neuron before the subsequent integration of these signals at its cell body. In this work, we describe a model of depressing and facilitating synapses derived from a hardware circuit implementation. This model is equivalent to theoretical models of short-term synaptic dynamics in network simulations. These circuits have been added to a network of leaky integrate-and-fire neurons. A cortical model of direction-selectivity that uses short-term dynamic synapses has been implemented with this network.