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Revealing topographic and temporal features of muscle coactivation in sensorimotor cortex of rats and bats using closed-loop data collection and machine learning analysis techniques

While classical motor maps suggest simple body-part representations, complex behaviours demand sophisticated organizational principles that have been difficult to investigate due to technical limitations in combining intracortical microstimulation (ICMS) with electromyography (EMG). This thesis addresses fundamental questions about cortical muscle organization through systematic investigation of rat and Egyptian fruit bat sensorimotor cortex using long-train ICMS combined with multi-muscle EMG recordings. We developed an integrated data collection and analysis ecosystem featuring real-time feedback control to eliminate spontaneous movement contamination, circuit optimization strategies for artifact reduction, and comprehensive automated analysis frameworks. In rats, comprehensive mapping across sensorimotor cortex revealed extensive topographic overlap of muscle representations, challenging traditional views of discrete muscle maps. Individual muscles exhibited complex spatial patterns with remarkable coactivation capacity from single cortical sites. Extensive cross-body coordination between face, forelimb, and hindlimb muscles demonstrated rich temporal coordination patterns, establishing that motor cortex is fundamentally organized around muscle synergies rather than individual muscle control. We discovered the first identified echolocation control module in bat cortex. Extensive motor representations crossing traditional cortical boundaries demonstrated sophisticated sensorimotor integration supporting flight and vocalization. Advanced computational validation through machine learning techniques provided objective confirmation of functional modularity in cortical representations for both within-muscle and across them. This work establishes functional modularity as a fundamental principle of sensorimotor organization and demonstrates that cortical circuits encode behaviourally critical timing relationships with remarkable precision, providing unprecedented insight into how evolution shapes neural circuits for motor behaviours.