Generalized Linear ModelsHodgkin-Huxley equationsHopfield modelcomputational neurosciencetheoretical neuroscience
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Abstract (AI)
What happens in our brain when we make a decision? What triggers a neuron to send out a signal? What is the neural code? This textbook for advanced undergraduate and beginning graduate students provides a thorough and up-to-date introduction to the fields of computational and theoretical neuroscience. It covers classical topics, including the Hodgkin-Huxley equations and Hopfield model, as well as modern developments in the field such as Generalized Linear Models and decision theory. Concepts are introduced using clear step-by-step explanations suitable for readers with only a basic knowledge of differential equations and probabilities, and are richly illustrated by figures and worked-out examples. End-of-chapter summaries and classroom-tested exercises make the book ideal for courses or for self-study. The authors also give pointers to the literature and an extensive bibliography, which will prove invaluable to readers interested in further study.
Key Findings
1
Covers classical models including the Hodgkin-Huxley equations and Hopfield model alongside modern methods such as Generalized Linear Models and decision theory.
2
Explains neural decision-making, signal generation, and neural coding through step-by-step presentations requiring only basic differential equations and probability knowledge.
3
Includes figures, worked examples, chapter summaries, classroom-tested exercises, literature pointers, and an extensive bibliography to support coursework and self-study.
4
Provides an up-to-date introduction to computational and theoretical neuroscience for advanced undergraduates and beginning graduate students.
Research Object
Neuronal and brain dynamics
Research Subject
Computational and theoretical mechanisms of neural signaling, neural coding, network dynamics, and decision-making
Publication Details
Publication Date
2014-07-24
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