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Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution
Published on: September 5, 2012
Energy-efficient encoding by shifting spikes in neocortical neurons
Aleksey Malyshev1, Tatjana Tchumatchenko, Stanislav Volgushev
1Department 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.
The European Journal of Neuroscience
|August 15, 2013
Summary
Neocortical neurons achieve rapid computation by quickly responding to input changes. They balance high sensitivity with low noise and energy use by shifting action potentials or generating new ones, depending on ongoing activity.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Systems Neuroscience
Background:
- Neocortical network computation speed relies on neurons rapidly detecting input changes.
- High sensitivity to perturbations risks noise and energy inefficiency.
- Reconciling sensitivity, low noise, and energy efficiency is a key challenge.
Purpose of the Study:
- Investigate if neuronal networks can be both highly sensitive and energy-efficient.
- Examine how neurons respond to small perturbations in noisy environments.
- Determine the mechanisms underlying rapid neuronal responses.
Main Methods:
- Intracellular recordings from rat visual cortex layer 2/3 pyramidal neurons.
- Application of artificial excitatory postsynaptic currents (aEPSCs) to simulate input perturbations.
- Analysis of neuronal firing rate changes and action potential (AP) generation in response to stimuli.
Main Results:
- Layer 2/3 pyramidal neurons exhibit high sensitivity, responding to small aEPSCs within 2-2.5 ms.
- Rapid responses are achieved by generating additional APs or shifting existing spikes.
- Energy-efficient responses occur when spikes are shifted, balancing increases and decreases in spike count.
- Response strategy (shifting vs. generating spikes) depends on aEPSCs timing relative to ongoing depolarization waves.
Conclusions:
- Neuronal networks can achieve high sensitivity and low-noise operation simultaneously.
- Ongoing activity patterns can facilitate energy-efficient computations.
- The timing of inputs relative to network state dictates response strategy and energy cost.
