Dendritic computation.

Michael London; Michael Häusser

doi:10.1146/annurev.neuro.28.061604.135703

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Dendritic computation.

Michael London·Michael Häusser

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Abstract

One of the central questions in neuroscience is how particular tasks, or computations, are implemented by neural networks to generate behavior. The prevailing view has been that information processing in neural networks results primarily from the properties of synapses and the connectivity of neurons within the network, with the intrinsic excitability of single neurons playing a lesser role. As a consequence, the contribution of single neurons to computation in the brain has long been underestimated. Here we review recent work showing that neuronal dendrites exhibit a range of linear and nonlinear mechanisms that allow them to implement elementary computations. We discuss why these dendritic properties may be essential for the computations performed by the neuron and the network and provide theoretical and experimental examples to support this view.

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Animals Computer Simulation Dendrites Diagnostic Imaging Humans Membrane Potentials Models, Neurological Nerve Net Neurons Nonlinear Dynamics Synapses Journal Article Research Support, Non-U.S. Gov't Review

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Michael London, Michael Häusser. 2005. Dendritic computation.. https://doi.org/10.1146/annurev.neuro.28.061604.135703

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