RT Journal Article
SR Electronic
T1 Energy-Efficient Neuronal Computation via Quantal Synaptic Failures
JF The Journal of Neuroscience
JO J. Neurosci.
FD Society for Neuroscience
SP 4746
OP 4755
DO 10.1523/JNEUROSCI.22-11-04746.2002
VO 22
IS 11
A1 Levy, William B
A1 Baxter, Robert A.
YR 2002
UL http://www.jneurosci.org/content/22/11/4746.abstract
AB 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.