Analog VLSI and neural systems
Analog VLSI and neural systems
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Retinomorphic vision systems: reverse engineering the vertebrate retina
Retinomorphic vision systems: reverse engineering the vertebrate retina
Pulse-based computation in VLSI neural networks
Pulsed neural networks
Building silicon nervous systems with dendritic tree neuromorphs
Pulsed neural networks
Communicating neuronal ensembles between neuromorphic chips
Neuromorphic systems engineering
A VLSI-Based Model of Azimuthal Echolocation in the Big Brown Bat
Autonomous Robots
Analog VLSI: Circuits and Principles
Analog VLSI: Circuits and Principles
Modeling short-term synaptic depression in Silicon
Neural Computation
Modeling Selective Attention Using a Neuromorphic Analog VLSI Device
Neural Computation
Spike-Driven Synaptic Plasticity: Theory, Simulation, VLSI Implementation
Neural Computation
Biophysics of Computation: Information Processing in Single Neurons (Computational Neuroscience Series)
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A systematic method for configuring vlsi networks of spiking neurons
Neural Computation
Proceedings of the great lakes symposium on VLSI
Dynamic state and parameter estimation applied to neuromorphic systems
Neural Computation
Spatio-temporal spike pattern classification in neuromorphic systems
Living Machines'13 Proceedings of the Second international conference on Biomimetic and Biohybrid Systems
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Synapses are crucial elements for computation and information transfer in both real and artificial neural systems. Recent experimental findings and theoretical models of pulse-based neural networks suggest that synaptic dynamics can play a crucial role for learning neural codes and encoding spatiotemporal spike patterns. Within the context of hardware implementations of pulse-based neural networks, several analog VLSI circuits modeling synaptic functionality have been proposed. We present an overview of previously proposed circuits and describe a novel analog VLSI synaptic circuit suitable for integration in large VLSI spike-based neural systems. The circuit proposed is based on a computational model that fits the real postsynaptic currents with exponentials. We present experimental data showing how the circuit exhibits realistic dynamics and show how it can be connected to additional modules for implementing a wide range of synaptic properties.