The Ghost Inside the Machine: Intrinsic Excitability Modulates Neuronal Function and Synaptic Integration




Pahlavan, Bahram

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Since the cerebellum is involved in many forms of movement and non-movement learnings and diseases, it is of utmost importance to develop and utilize a computer model of cerebellar neurons and circuitry to complete experimental efforts such as electrophysiology and calcium imaging. I worked on improving established biophysically detailed computer model of the sole output neurons of cerebellar cortex named Purkinje cells. I first modified the model to replicate basic experimental data including simple and complex spiking of these neurons and some aspects of their intracellular calcium concentration more reliably. In spinocerebellar ataxia 13,19/22, cerebellar dependent learning is disrupted and A-type current (IA), caused by voltage-gated potassium channels KV3.3 and Kv4.3, is affected. We took advantage of our model and showed that knockout of IA, specifically by Kv4.3, removes the nonlinearity of dendritic calcium response which is used as a proxy to investigate the long-term depression of parallel fiber inputs, a form of plasticity important for cerebellar learning. To make our model physically more realistic, I also studied the thermodynamics governing neuronal conductances (sodium, potassium, and calcium) kinetics and showed that macromolecular rate theory (MMRT) can explain temperature dependence of ratio of reaction rates at different temperatures. MMRT can better predict the neuronal conductance kinetics required to simulate neuronal activity at different temperatures. Furthermore, MMRT also predicts an optimal temperature for the enzymatic reactions that we suggest that could be a thermodynamical barrier to avoid overexcitation of neurons by increasing temperatures.


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biophysical modeling, Computational neuroscience, Excitability, Neuronal membrane conductance, Purkinje cell, Thermodynamics



Neuroscience, Developmental and Regenerative Biology