计算神经科学

Data-Driven Computational Neuroscience Pub Date : 2020-11-30 DOI:10.1017/9781108642989.003

B. Kappen

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Computational Neuroscience

2 Neural information processing is noisy 6 2.1 Poisson Processes . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1.1 Interval distribution . . . . . . . . . . . . . . . . . . . . . 10 2.1.2 Integration reduces CV . . . . . . . . . . . . . . . . . . . 12 2.1.3 Refractoriness . . . . . . . . . . . . . . . . . . . . . . . 12 2.2 ISI distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.3 First passage times . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.3.1 Diffusion . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.3.2 First passage time distribution . . . . . . . . . . . . . . . 17 2.3.3 Laplace transformation . . . . . . . . . . . . . . . . . . . 18 2.3.4 Scale invariance . . . . . . . . . . . . . . . . . . . . . . 20 2.3.5 Approximate scale invariance. . . . . . . . . . . . . . . . 20 2.4 Integrate and Fire Neuron . . . . . . . . . . . . . . . . . . . . . . 23 2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.6 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

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Data-Driven Computational Neuroscience

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