Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
The Design Warrior's Guide to FPGAs
The Design Warrior's Guide to FPGAs
Fast recursive filters for simulating nonlinear dynamic systems
Neural Computation
A spiking neural network model of an actor-critic learning agent
Neural Computation
Simplicity and efficiency of integrate-and-fire neuron models
Neural Computation
Robust ratiometric infochemical communication in a neuromorphic "synthetic moth"
Living Machines'13 Proceedings of the Second international conference on Biomimetic and Biohybrid Systems
| Hi-index | 0.00 |
Programmable logic designs are presented that achieve exact integration of leaky integrate-and-fire soma and dynamical synapse neuronal models and incorporate spike-time dependent plasticity and axonal delays. Highly accurate numerical performance has been achieved by modifying simpler forward-Euler-based circuitry requiring minimal circuit allocation, which, as we show, behaves equivalently to exact integration. These designs have been implemented and simulated at the behavioral and physical device levels, demonstrating close agreement with both numerical and analytical results. By exploiting finely grained parallelism and single clock cycle numerical iteration, these designs achieve simulation speeds at least five orders of magnitude faster than the nervous system, termed here hyper-real-time operation, when deployed on commercially available field-programmable gate array (FPGA) devices. Taken together, our designs form a programmable logic construction kit of commonly used neuronal model elements that supports the building of large and complex architectures of spiking neuron networks for real-time neuromorphic implementation, neurophysiological interfacing, or efficient parameter space investigations.