The Journal of Neuroscience, June 1, 2002, 22(11):4746-4755
Energy-Efficient Neuronal Computation via Quantal Synaptic
Failures
William B
Levy1 and
Robert A.
Baxter1, 2
1 University of Virginia Health System, Department of
Neurosurgery, Charlottesville, Virginia 22908, and
2 Baxter Research Company, Bedford, Massachusetts
01730
Organisms evolve as compromises, and many of these compromises can
be expressed in terms of energy efficiency. For example, a compromise
between rate of information processing and the energy consumed might
explain certain neurophysiological and neuroanatomical observations
(e.g., average firing frequency and number of neurons). Using this
perspective reveals that the randomness injected into neural processing
by the statistical uncertainty of synaptic transmission optimizes one
kind of information processing relative to energy use. A critical
hypothesis and insight is that neuronal information processing is
appropriately measured, first, by considering dendrosomatic summation
as a Shannon-type channel (1948) and, second, by considering such
uncertain synaptic transmission as part of the dendrosomatic computation rather than as part of axonal information transmission. Using such a model of neural computation and matching the information gathered by dendritic summation to the axonal information transmitted, H(p*), conditions are defined that guarantee
synaptic failures can improve the energetic efficiency of neurons.
Further development provides a general expression relating optimal
failure rate, f, to average firing rate, p*, and
is consistent with physiologically observed values. The expression
providing this relationship, f
4
H(p*), generalizes across activity levels
and is independent of the number of inputs to a neuron.
Key words:
computation; efficiency; energy; entropy; information
theory; mutual information; optimization; quantal failures; Shannon
Copyright © 2002 Society for Neuroscience 0270-6474/02/22114746-10$05.00/0