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Computational Modeling of Spiking in the Layer 5 Projection Neurons of the Mouse Motor Cortex

Publication Type : Conference Proceedings

Publisher : Springer Nature Singapore

Source : Lecture Notes in Electrical Engineering

Url : https://doi.org/10.1007/978-981-97-4711-5_2

Campus : Amritapuri

Center : Amrita Mind Brain Center

Year : 2025

Abstract : Mathematical models have allowed the reconstruction of realistic behavior of large and complex neurons under various physiological functions. Understanding the electrophysiological behavior regarding neuronal gain, process, and connectivity of such complex pyramidal projection neurons in the motor cortex is essential for unraveling the neural basis of motor control and developing therapies for conditions related to motor dysfunctions. This modeling study analyzed the input/output properties of L5 cortico-striatal projection neurons and their sensitivity to abnormal firing behavior. The study reconstructs the excitability of layer 5 cortico-striatal neurons and suggests how they integrate the inputs temporally matching experimental observations. The study also showed variability in the output patterns in layer 5 cortico-striatal neurons with varying current stimuli. This model predicted that the firing rate is proportional to the delayed input currents, which may affect the neuron’s feature integration property. The modeling methodology showed several single-neuron excitability characteristics relevant to current disease mechanism theories and recognized Parkinson’s disease-associated symptoms. These results help to uncover the sensory integration at the single neuron level and pathophysiology of motor deficit along with other interconnected motor systems in the context of motor control and information processing.

Cite this Research Publication : Arathi Rajendran, Navya Ajith, Aiswarya Chandrabhanu Nambiar, Giovanni Naldi, Shyam Diwakar, Computational Modeling of Spiking in the Layer 5 Projection Neurons of the Mouse Motor Cortex, Lecture Notes in Electrical Engineering, Springer Nature Singapore, 2025, https://doi.org/10.1007/978-981-97-4711-5_2

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