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Neural Circuits

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
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Updated: Jun 16, 2026

Multi-electrode Array Recordings of Neuronal Avalanches in Organotypic Cultures

Multi-electrode Array Recordings of Neuronal Avalanches in Organotypic Cultures

Published on: August 1, 2011

The asynchronous state in cortical circuits.

Alfonso Renart¹, Jaime de la Rocha, Peter Bartho

¹Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA. arenart@andromeda.rutgers.edu

Science (New York, N.Y.)

|January 30, 2010

Summary

This summary is machine-generated.

Recurrent neural networks can generate low spiking correlations despite shared inputs. This asynchronous state, observed in rodent neocortex, suggests rethinking the role of neural correlations in information processing.

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Area of Science:

Computational neuroscience
Neural circuits

Background:

Correlated neural spiking is common in cortical circuits.
The functional role of these correlations is debated, with concerns they limit information decoding.

Purpose of the Study:

To investigate if recurrent neural networks can achieve low spiking correlations despite shared inputs.
To explore the mechanisms generating such an asynchronous state.

Main Methods:

Theoretical modeling of recurrent neural networks.
Analysis of spontaneous fluctuations in excitatory and inhibitory neuronal populations.
Experimental validation using in vivo recordings from rodent neocortex.

Main Results:

Recurrent networks can generate an asynchronous state with low mean spiking correlations.
Shared inputs are compensated by negative correlations in synaptic currents.
Near-zero mean correlations were experimentally observed in rodent neocortex.

Conclusions:

Neural networks can maintain low correlations even with significant shared input.
The asynchronous state offers a potential mechanism for mitigating the impact of correlations.
Findings prompt a reevaluation of the origins and functional impact of neural correlations.