Systematic mapping of neural function to morphology

The dendrites of neurons in animal brains display a wide variety of shapes and branching patterns both across dif-ferent animal species as well across different brain struc-tures and cell types within each species. This diversity of dendritic shapes presumably parallels the diversity of den-dritic computational functions. Although some function – structure relationships are understood, there is no gen-eral insight in how dendritic functions, such as the inte-gration of synaptic signals, are fulfilled differently by different dendritic morphologies. We have previously developed two methods for finding dendritic structures optimized for a given computational function ([1] and [2]). Both methods implement a recursive algorithm that represents dendritic morphology in a compact manner, by an L-System as in [3]. Then, Genetic Algortihms (GAs) are

Abstract licence: CC BY-NC-ND

5 more references, whose wording the paper’s licence keeps private.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.