### Abstract

The activity of neurons is correlated, and this correlation affects how the brain processes information. We study the neural circuit mechanisms of correlations by analyzing a network model characterized by strong and heterogeneous interactions: excitatory input drives the fluctuations of neural activity,which are counterbalanced by inhibitory feedback. In particular, excitatory input tends to correlate neurons, while inhibitory feedback reduces correlations.We demonstrate that heterogeneity of synaptic connections is necessary for this inhibition of correlations. We calculate statistical averages over the disordered synaptic interactions and apply our findings to both a simple linear model and a more realistic spiking network model.We find that correlations at zero time lag are positive and of magnitude K^{-1/2} , where K is the number of connections to a neuron. Correlations at longer timescales are of smaller magnitude, of order K^{-1}, implying that inhibition of correlations occurs quickly, on a timescale of K^{-1/2} . The small magnitude of correlations agrees qualitativelywith physiological measurements in the cerebral cortex and basal ganglia. The model could be used to study correlations in brain regions dominated by recurrent inhibition, such as the striatum and globus pallidus.

Original language | English (US) |
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Pages (from-to) | 1732-1767 |

Number of pages | 36 |

Journal | Neural Computation |

Volume | 25 |

Issue number | 7 |

DOIs | |

State | Published - 2013 |

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### ASJC Scopus subject areas

- Cognitive Neuroscience
- Arts and Humanities (miscellaneous)

### Cite this

*Neural Computation*,

*25*(7), 1732-1767. https://doi.org/10.1162/NECO_a_00451

**Decorrelation by recurrent inhibition in heterogeneous neural circuits.** / Bernacchia, Alberto; Wang, Xiao-Jing.

Research output: Contribution to journal › Article

*Neural Computation*, vol. 25, no. 7, pp. 1732-1767. https://doi.org/10.1162/NECO_a_00451

}

TY - JOUR

T1 - Decorrelation by recurrent inhibition in heterogeneous neural circuits

AU - Bernacchia, Alberto

AU - Wang, Xiao-Jing

PY - 2013

Y1 - 2013

N2 - The activity of neurons is correlated, and this correlation affects how the brain processes information. We study the neural circuit mechanisms of correlations by analyzing a network model characterized by strong and heterogeneous interactions: excitatory input drives the fluctuations of neural activity,which are counterbalanced by inhibitory feedback. In particular, excitatory input tends to correlate neurons, while inhibitory feedback reduces correlations.We demonstrate that heterogeneity of synaptic connections is necessary for this inhibition of correlations. We calculate statistical averages over the disordered synaptic interactions and apply our findings to both a simple linear model and a more realistic spiking network model.We find that correlations at zero time lag are positive and of magnitude K-1/2 , where K is the number of connections to a neuron. Correlations at longer timescales are of smaller magnitude, of order K-1, implying that inhibition of correlations occurs quickly, on a timescale of K-1/2 . The small magnitude of correlations agrees qualitativelywith physiological measurements in the cerebral cortex and basal ganglia. The model could be used to study correlations in brain regions dominated by recurrent inhibition, such as the striatum and globus pallidus.

AB - The activity of neurons is correlated, and this correlation affects how the brain processes information. We study the neural circuit mechanisms of correlations by analyzing a network model characterized by strong and heterogeneous interactions: excitatory input drives the fluctuations of neural activity,which are counterbalanced by inhibitory feedback. In particular, excitatory input tends to correlate neurons, while inhibitory feedback reduces correlations.We demonstrate that heterogeneity of synaptic connections is necessary for this inhibition of correlations. We calculate statistical averages over the disordered synaptic interactions and apply our findings to both a simple linear model and a more realistic spiking network model.We find that correlations at zero time lag are positive and of magnitude K-1/2 , where K is the number of connections to a neuron. Correlations at longer timescales are of smaller magnitude, of order K-1, implying that inhibition of correlations occurs quickly, on a timescale of K-1/2 . The small magnitude of correlations agrees qualitativelywith physiological measurements in the cerebral cortex and basal ganglia. The model could be used to study correlations in brain regions dominated by recurrent inhibition, such as the striatum and globus pallidus.

UR - http://www.scopus.com/inward/record.url?scp=84880983456&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84880983456&partnerID=8YFLogxK

U2 - 10.1162/NECO_a_00451

DO - 10.1162/NECO_a_00451

M3 - Article

C2 - 23607559

AN - SCOPUS:84880983456

VL - 25

SP - 1732

EP - 1767

JO - Neural Computation

JF - Neural Computation

SN - 0899-7667

IS - 7

ER -