The provided code is part of a computational model designed to simulate synaptic interactions within specific neural pathways in the cerebellum and related structures, focusing particularly on the pathway interactions involving Purkinje cells (PCs), deep cerebellar nuclei (DCN), negative osteoblast (NO) cells, and the pontine nucleus (PN) via mossy fibers. The key biological pathways modeled in this code are outlined below: ### Biological Basis of the Modeled Pathways: 1. **PC-DCN Pathway**: - **Biological Description**: Purkinje cells (PCs) in the cerebellum transmit inhibitory signals to the deep cerebellar nucleus (DCN). This inhibition is critical for cerebellar processing, influencing motor coordination and learning. - **Key Aspects in Code**: The code uses a tanhSyn2 mechanism to model fast inhibitory synaptic transmission from PCs to the DCN. Parameters like conductance (`g`), synaptic reversal potential (`e`), and delay are configured according to empirical data, such as the delay from Hoebeek et al., 2008, which influences how these synapses dynamically affect DCN activity. 2. **PC-NO Pathway**: - **Biological Description**: This pathway involves inhibition from PCs to certain other inhibitory neurons, referred to here as NO cells. Like in the PC-DCN pathway, this interaction contributes to the precise regulation of cerebellar output and motor control. - **Key Aspects in Code**: Similar to the PC-DCN pathway, tanhSyn2 is used but with different parameters for synaptic strength and dynamics, particularly focusing on an increased `tau` value, indicating slower synaptic decay, reflecting longer-lasting synaptic effects. 3. **PYcell-PN-DCN Pathway**: - **Biological Description**: This models the mossy fiber pathway whereby excitatory input from the pontine nucleus (through the pontocerebellar path) to the DCN is mediated and modified, contributing excitatory drive to the cerebellar nuclei. These synapses typically involve fast excitatory neurotransmission mediated by glutamate receptors, like AMPA and NMDA receptors. - **Key Aspects in Code**: The code provides separate synaptic objects for AMPA and NMDA receptor-mediated transmission using `NoisyExpSyn` and `NoisyExp2Syn`, respectively. These synapses are characterized by their specific rise and decay times (`tau`) and reversal potential for excitatory postsynaptic currents (`e`), with each synapse subjected to synaptic noise, reflecting natural variability in synaptic transmission. ### Synaptic Features: - **Synaptic Noise**: Throughout this model, noise is introduced into the synaptic transmission, which is a common approach to incorporate the stochastic nature of neurotransmitter release and receptor interaction observed in biological synapses. - **Gating Variables and Dynamics**: Parameters such as `tau`, `rparam`, `alpha`, and synaptic delays play crucial roles in defining the temporal dynamics of synaptic interactions, reflecting biological processes like neurotransmitter binding, channel opening, and postsynaptic potential propagation. Overall, this computational model captures the essential synaptic interactions and dynamics between PCs, DCN, NO, and other involved neurons in the cerebellum and its associated structures, providing a framework to explore the integrative role of these cells in motor control and coordination.