title: A quantitative theory of neural computation.
abstract: We show how a general quantitative theory of neural computation can be used to explain two recent experimental findings in neuroscience. The first of these findings is that in human medial temporal lobe there exist neurons that correspond to identifiable concepts, such as a particular actress. Further, even when such concepts are preselected by the experimenter, such neurons can be found with paradoxical ease, after examining relatively few neurons. We offer a quantitative computational explanation of this phenomenon, where apparently none existed before. Second, for the locust olfactory system estimates of the four parameters of neuron numbers, synapse numbers, synapse strengths, and the numbers of neurons that represent an odor are now available. We show here that these numbers are related as predicted by the general theory. More generally, we identify two useful regimes for neural computation with distinct ranges of these quantitative parameters.
authors.affiliation: Division of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA. valiant@deas.harvard.edu
authors.forename: Leslie G
authors.initials: LG
authors.lastname: Valiant
mesh_terms: D000465:Algorithms; D001921:Brain / Q000166:cytology; D016009:Chi-Square Distribution; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000166:cytology / Q000502:physiology*; D013569:Synapses / Q000502:physiology*