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Neural Sequence Generation Using Spatiotemporal Patterns of Inhibition.

Jonathan Cannon^1,2, Nancy Kopell¹, Timothy Gardner³

¹Department of Mathematics, Boston University, Boston, Massachusetts, United States of America.

Plos Computational Biology

|November 5, 2015

Summary

Neural sequence generation may involve complex interactions between excitation and inhibition, not just feedforward excitation. This new model highlights how structured inhibition maintains timing and synchrony in neural activity for reproducible behaviors.

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Area of Science:

Computational Neuroscience
Systems Neuroscience
Neural Dynamics

Background:

Stereotyped neural activity sequences are fundamental to behaviors and cognition.
The synfire chain model, based on feedforward excitation, is a prominent theory for neural sequence generation.
Recent findings show complex interactions between excitatory and inhibitory neurons in neural pathways.

Purpose of the Study:

To propose a novel model for neural sequence generation inspired by experimental observations.
To investigate the role of local feedback inhibition in maintaining neural synchrony and timing.

Main Methods:

Development of a computational model simulating neural sequence generation.
The model incorporates spatially recurrent excitatory chains with local feedback inhibition zones.
Analysis of how pulse propagation interacts with spatiotemporal inhibition patterns.

Main Results:

The proposed model demonstrates robust sequence generation through the interplay of excitation and inhibition.
Synchrony and precise timing are maintained by the interaction between neural pulses and structured inhibition.
This contrasts with traditional models relying solely on redundant excitatory connections.

Conclusions:

Spatially and temporally structured inhibition plays a crucial role in neural sequence generation.
This inhibitory mechanism offers an alternative to purely feedforward excitatory models for explaining reproducible neural activity.
The findings suggest a more complex neural architecture for generating timed behavioral outputs.