Spiking neuron models : single neurons, populations, plasticity / Wulfram Gerstner, Werner M. Kistler.
Gerstner, Wulfram| Call Number | 573.8/536 |
| Author | Gerstner, Wulfram, author. |
| Title | Spiking neuron models : single neurons, populations, plasticity / Wulfram Gerstner, Werner M. Kistler. |
| Physical Description | 1 online resource (xiv, 480 pages) : digital, PDF file(s). |
| Notes | Title from publisher's bibliographic system (viewed on 05 Oct 2015). |
| Summary | Neurons in the brain communicate by short electrical pulses, the so-called action potentials or spikes. How can we understand the process of spike generation? How can we understand information transmission by neurons? What happens if thousands of neurons are coupled together in a seemingly random network? How does the network connectivity determine the activity patterns? And, vice versa, how does the spike activity influence the connectivity pattern? These questions are addressed in this 2002 introduction to spiking neurons aimed at those taking courses in computational neuroscience, theoretical biology, biophysics, or neural networks. The approach will suit students of physics, mathematics, or computer science; it will also be useful for biologists who are interested in mathematical modelling. The text is enhanced by many worked examples and illustrations. There are no mathematical prerequisites beyond what the audience would meet as undergraduates: more advanced techniques are introduced in an elementary, concrete fashion when needed. |
| Added Author | Kistler, Werner M., 1969- author. |
| Subject | NEURONS. Neural networks (Neurobiology) NEUROPLASTICITY. Computational neuroscience. |
| Multimedia |
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| Summary | Neurons in the brain communicate by short electrical pulses, the so-called action potentials or spikes. How can we understand the process of spike generation? How can we understand information transmission by neurons? What happens if thousands of neurons are coupled together in a seemingly random network? How does the network connectivity determine the activity patterns? And, vice versa, how does the spike activity influence the connectivity pattern? These questions are addressed in this 2002 introduction to spiking neurons aimed at those taking courses in computational neuroscience, theoretical biology, biophysics, or neural networks. The approach will suit students of physics, mathematics, or computer science; it will also be useful for biologists who are interested in mathematical modelling. The text is enhanced by many worked examples and illustrations. There are no mathematical prerequisites beyond what the audience would meet as undergraduates: more advanced techniques are introduced in an elementary, concrete fashion when needed. |
| Notes | Title from publisher's bibliographic system (viewed on 05 Oct 2015). |
| Subject | NEURONS. Neural networks (Neurobiology) NEUROPLASTICITY. Computational neuroscience. |
| Multimedia |