Reliable neuronal systems: the importance of heterogeneity

For every engineer it goes without saying: in order to build a reliable system we need components that consistently behave precisely as they should. It is also well known that neurons, the building blocks of brains, do not satisfy this constraint. Even neurons of the same type come with huge varianc...

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Bibliographic Details
Published in:PloS one 2013-12, Vol.8 (12), p.e80694-e80694
Main Authors: Lengler, Johannes, Jug, Florian, Steger, Angelika
Format: Article
Language:English
Subjects:
alpha-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic Acid - metabolism
Animals
Brain
Brain - physiology
Computer science
Computer Simulation
Computers
Defects
Energy efficiency
Excitatory Postsynaptic Potentials - physiology
Experiments
gamma-Aminobutyric Acid - metabolism
Genetic Heterogeneity
Heterogeneity
Humans
Learning - physiology
Models, Neurological
Nerve Net - physiology
Neuronal Plasticity - genetics
Neurons
Neurons - cytology
Neurons - metabolism
Neurosciences
Physiology
Poisson Distribution
Population
Receptors, AMPA - genetics
Receptors, AMPA - metabolism
Receptors, N-Methyl-D-Aspartate - genetics
Receptors, N-Methyl-D-Aspartate - metabolism
Signal processing
Standard deviation
Synapses
Synapses - physiology
Synaptic plasticity
Synaptic Transmission - physiology
Variability
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Summary:For every engineer it goes without saying: in order to build a reliable system we need components that consistently behave precisely as they should. It is also well known that neurons, the building blocks of brains, do not satisfy this constraint. Even neurons of the same type come with huge variances in their properties and these properties also vary over time. Synapses, the connections between neurons, are highly unreliable in forwarding signals. In this paper we argue that both these fact add variance to neuronal processes, and that this variance is not a handicap of neural systems, but that instead predictable and reliable functional behavior of neural systems depends crucially on this variability. In particular, we show that higher variance allows a recurrently connected neural population to react more sensitively to incoming signals, and processes them faster and more energy efficient. This, for example, challenges the general assumption that the intrinsic variability of neurons in the brain is a defect that has to be overcome by synaptic plasticity in the process of learning.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0080694