Computational Neuroscience Series
Computational neuroscience is an approach to understanding the development and function of nervous systems at many different structural scales, including the biophysical, the circuit, and the systems levels. Methods include theoretical analysis and modeling of neurons, networks, and brain systems and are complementary to empirical techniques in neuroscience. Areas and topics of particular interest to this book series include computational mechanisms in neurons, analysis of signal processing in neural circuits, representation of sensory information, systems models of sensorimotor integration, computational approaches to biological motor control, and models of learning and memory. Further topics of interest include the intersection of computational neuroscience with engineering, from representation and dynamics, to observation and control.
Series editor: Terrence J. Sejnowski and Tomaso Poggio
Search Results
Modeling Neural Circuits Made Simple with Python
Pub Date: Mar 19, 2024
Principles of Brain Dynamics
Pub Date: Dec 05, 2023
Biological Learning and Control
Pub Date: Oct 31, 2023
Neural Control Engineering
Pub Date: Nov 01, 2022
An Introductory Course in Computational Neuroscience
Pub Date: Oct 02, 2018
From Neuron to Cognition via Computational Neuroscience
Pub Date: Nov 11, 2016
The Computational Brain
Pub Date: Nov 04, 2016
Case Studies in Neural Data Analysis
Pub Date: Nov 04, 2016
Visual Cortex and Deep Networks
Pub Date: Sep 23, 2016
Brain Computation as Hierarchical Abstraction
Pub Date: Feb 20, 2015
Visual Population Codes
Pub Date: Oct 28, 2011
Bayesian Brain
Pub Date: Jan 21, 2011
Dynamical Systems in Neuroscience
Pub Date: Jan 22, 2010
Computational Modeling Methods for Neuroscientists
Pub Date: Sep 04, 2009
Theoretical Neuroscience
Pub Date: Aug 12, 2005