Causal connectivity of evolved neural networks during behavior

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Understanding neural dynamics requires analyzing causal interactions without damaging neural mechanisms.
• Behavior significantly modulates causal interactions within neural systems.

Purpose of the Study:

• To propose and illustrate a novel method for characterizing causal interactions in intact neural mechanisms.
• To investigate how neural network structures and dynamics relate to behavior, particularly sensorimotor coordination.

Main Methods:

• Developed a graph-theoretic extension of vector autoregressive modeling and Granger causality.
• Introduced 'causal connectivity analysis' for intact neural systems.
• Applied the method to model neural networks controlling target fixation in a simulated head-eye system with varied environments.

Main Results:

• Causal connectivity analysis provided insights into sensorimotor coordination mechanisms.
• Networks supporting complex adaptive behavior exhibited higher causal interaction density and stronger sensory-to-motor flow.
• Distinct arrangements of 'causal sources' and 'causal sinks' were observed in networks supporting richer behaviors.
• The analysis successfully predicted the functional outcomes of simulated neural network lesions.

Conclusions:

• Causal connectivity analysis is a valuable tool for studying neural dynamics in intact systems.
• The method offers a non-invasive approach to understanding how behavior influences neural causal interactions.
• Findings suggest potential applications in analyzing neural dynamics for various cognitive and motor functions.