Linearization of F-1 curves by adaptation
Neural Computation
Permitted and forbidden sets in symmetric threshold-linear networks
Neural Computation
Rate models for conductance-based cortical neuronal networks
Neural Computation
Hybrid integrate-and-fire model of a bursting neuron
Neural Computation
Neural Modeling of an Internal Clock
Neural Computation
Patterns of Synchrony in Neural Networks with Spike Adaptation
Neural Computation
Dynamics of Strongly Coupled Spiking Neurons
Neural Computation
Response characteristics of a low-dimensional model neuron
Neural Computation
A systematic method for configuring vlsi networks of spiking neurons
Neural Computation
| Hi-index | 0.00 |
The method of averaging and a detailed bifurcation calculationare used to reduce a system of synaptically coupled neurons to aHopfield type continuous time neural network. Due to some specialproperties of the bifurcation, explicit averaging is not requiredand the reduction becomes a simple algebraic problem. The resultantcalculations show one how to derive a new type of "squashingfunction" whose properties are directly related to the detailedionic mechanisms of the membrane. Frequency encoding as opposed toamplitude encoding emerges in a natural fashion from the theory.The full system and the reduced system are numericallycompared.