โ Scribed by Gerstner W., Kistler W.M.
No coin nor oath required. For personal study only.
This introduction to spiking neurons can be used in advanced-level courses in computational neuroscience, theoretical biology, neural modeling, biophysics, or neural networks. It focuses on phenomenological approaches rather than detailed models in order to provide the reader with a conceptual framework. The authors formulate the theoretical concepts clearly without many mathematical details. While the book contains standard material for courses in computational neuroscience, neural modeling, or neural networks, it also provides an entry to current research. No prior knowledge beyond undergraduate mathematics is required.
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very well written, easy to understand, walks you through the logic of each part of each equation. builds up more and more complex models based upon the previous models. You'll learn a lot of practical neurobiology stuff other than just modeling too.
This introduction to spiking neurons can be used in advanced-level courses in computational neuroscience, theoretical biology, neural modeling, biophysics, or neural networks. It focuses on phenomenological approaches rather than detailed models in order to provide the reader with a conceptual frame
<p>1 Some basic neurophysiology 4 The neuron 1. 1 4 1. 1. 1 The axon 7 1. 1. 2 The synapse 9 12 1. 1. 3 The soma 1. 1. 4 The dendrites 13 13 1. 2 Types of neurons 2 Signals in the nervous system 14 2. 1 Action potentials as point events - point processes in the nervous system 15 18 2. 2 Spontaneous
Neural Computation 22, 1โ32 (2010).<div class="bb-sep"></div>Neurons receive thousands of presynaptic input spike trains while emitting a single output spike train. This drastic dimensionality reduction suggests considering a neuron as a bottleneck for information transmission.<br/>Extending recent
Neural Computation, 9 (1997)<div class="bb-sep"></div>We show that networks of relatively realistic mathematical models for biological neurons in principle can simulate arbitrary feedfornard sigmoidal neural neb in away that has previoudv not been considered. This new approach is based on temporal c
<p>This book describes a large number of open problems in the theory of stochastic neural systems, with the aim of enticing probabilists to work on them. This includes problems arising from stochastic models of individual neurons as well as those arising from stochastic models of the activities of s