# Biological Basis of the Computational Model The code provided is a computational neuroscience model designed to simulate the connectivity and physiological properties of a slice of the striatum, a critical region of the brain involved in motor control and cognition. The model uses a "fully-wired" approximation to simulate how neurons within the striatum connect and interact with each other. Below are the key biological aspects that the code is capturing: ## Neuronal Types 1. **Medium Spiny Neurons (MSNs):** - MSNs are the principal neurons of the striatum and are GABAergic (inhibitory) neurons. They play a significant role in the output of the basal ganglia circuit. - The model specifies a high density of MSNs, based on empirical data, set at 84,900 MSNs per mm³, reflecting the dense packing seen in rodent striatal tissue. 2. **Fast-Spiking Interneurons (FSIs):** - FSIs are another important population of neurons in the striatum, characterized by their fast firing rates. They regulate the activity of MSNs. - The model uses a specific percentage to represent the proportion of FSIs relative to the total neuronal population. ## Network Connectivity - **Connection Methods:** - The model supports two types of connection methodologies: 'physical' and 'random'. The example focuses on the 'random' wiring, which is indicative of a network where connectivity is not entirely constrained by spatial proximity. - **Targeted Connections:** - The numbers of synaptic connections and gap junctions are based on empirical data. - Each neuron type has specific target numbers for connections: MSNs connecting to other MSNs, FSIs connecting to MSNs, FSIs connecting to other FSIs, and FSIs forming gap junctions with other FSIs. These numbers are derived from anatomical studies to reflect the realistic connectivity in a striatal slice. ## Physiological Parameters - **Synaptic Weights and Delays:** - The model incorporates physiological parameters such as synaptic max delays and base synaptic weights for various inter-neuronal connections. - These parameters are crucial for capturing the dynamics of neuronal communication and signal propagation within the striatum. - **Gap Junctions:** - FSIs can also establish electrical synapses (gap junctions) which allow direct ionic currents between neurons. - The parameters for gap junctions, such as weights, are set to mimic the electrical coupling strength observed in biological tissues. ## Simulation Settings - **Temporal Dynamics:** - The model includes simulation time parameters (`tstart`, `tfinal`, `dt`) that define the temporal resolution and duration of the simulation, allowing one to examine the network activity over time. - **Random Seed:** - The use of a random seed ensures replicability of the simulation, important for stochastic processes inherent in synaptic connection formation. Overall, the model aims to reproduce the microcircuitry of the striatum, capturing key aspects of its anatomical and physiological connectivity. This allows for the study of striatal dynamics and how various factors might influence its role in motor and cognitive processes.