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Literature DB >> 22023194

Mathematical equivalence of two common forms of firing rate models of neural networks.

Abstract

We demonstrate the mathematical equivalence of two commonly used forms of firing rate model equations for neural networks. In addition, we show that what is commonly interpreted as the firing rate in one form of model may be better interpreted as a low-pass-filtered firing rate, and we point out a conductance-based firing rate model.

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Year: 2011 PMID： 22023194 PMCID： PMC3237837 DOI： 10.1162/NECO_a_00221

Source DB: PubMed Journal: Neural Comput ISSN： 0899-7667 Impact factor: 2.026

10 in total

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2. Rate models for conductance-based cortical neuronal networks.

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3. How noise contributes to contrast invariance of orientation tuning in cat visual cortex.

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4. Population dynamics of interacting spiking neurons.

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5. Minimal models of adapted neuronal response to in vivo-like input currents.

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6. The contribution of spike threshold to the dichotomy of cortical simple and complex cells.

Authors: Nicholas J Priebe; Ferenc Mechler; Matteo Carandini; David Ferster
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7. From spiking neurons to rate models: a cascade model as an approximation to spiking neuron models with refractoriness.

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8. Parameter space structure of continuous-time recurrent neural networks.

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9. Excitatory and inhibitory interactions in localized populations of model neurons.

Authors: H R Wilson; J D Cowan
Journal: Biophys J Date: 1972-01 Impact factor: 4.033

10. From spiking neuron models to linear-nonlinear models.

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10 in total

12 in total

Mathematical equivalence of two common forms of firing rate models of neural networks.

1. Neural noise can explain expansive, power-law nonlinearities in neural response functions.

2. Rate models for conductance-based cortical neuronal networks.

3. How noise contributes to contrast invariance of orientation tuning in cat visual cortex.

4. Population dynamics of interacting spiking neurons.

5. Minimal models of adapted neuronal response to in vivo-like input currents.

6. The contribution of spike threshold to the dichotomy of cortical simple and complex cells.

7. From spiking neurons to rate models: a cascade model as an approximation to spiking neuron models with refractoriness.

8. Parameter space structure of continuous-time recurrent neural networks.

9. Excitatory and inhibitory interactions in localized populations of model neurons.

10. From spiking neuron models to linear-nonlinear models.

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