Energy-efficient encoding by shifting spikes in neocortical neurons.
The speed of computations in neocortical networks critically depends on the ability of populations of spiking neurons to rapidly detect subtle changes in the input and translate them into firing rate changes. However, high sensitivity to perturbations may lead to explosion of noise and increased energy consumption. Can neuronal networks reconcile the requirements for high sensitivity, operation in a low-noise regime, and constrained energy consumption? Using intracellular recordings in slices from the rat visual cortex, we show that layer 2/3 pyramidal neurons are highly sensitive to minor input perturbations. They can change their population firing rate in response to small artificial excitatory postsynaptic currents (aEPSCs) immersed in fluctuating noise very quickly, within 2-2.5 ms. These quick responses were mediated by the generation of new, additional action potentials (APs), but also by shifting spikes into the response peak. In that latter case, the spike count increase during the peak and the decrease after the peak cancelled each other, thus producing quick responses without increases in total spike count and associated energy costs. The contribution of spikes from one or the other source depended on the aEPSCs timing relative to the waves of depolarization produced by ongoing activity. Neurons responded by shifting spikes to aEPSCs arriving at the beginning of a depolarization wave, but generated additional spikes in response to aEPSCs arriving towards the end of a wave. We conclude that neuronal networks can combine high sensitivity to perturbations and operation in a low-noise regime. Moreover, certain patterns of ongoing activity favor this combination and energy-efficient computations.
Department of Psychology, University of Connecticut, 406 Babbidge Road, Unit 1020, 06269-1020, Storrs, CT, USA; Institute of Higher Nervous Activity and Neurophysiology, RAS, Moscow, Russia.
Aleksey
A
Malyshev
Tatjana
T
Tchumatchenko
Stanislav
S
Volgushev
Maxim
M
Volgushev
D000200:Action Potentials; D000818:Animals; D019706:Excitatory Postsynaptic Potentials; D008959:Models, Neurological; D019579:Neocortex / Q000166:cytology / Q000502:physiology*; D017966:Pyramidal Cells / Q000502:physiology*; D051381:Rats; D017208:Rats, Wistar; D014793:Visual Cortex / Q000166:cytology / Q000502:physiology
population coding; rat neocortex; response speed; signal detection time; slices