# Biological Basis of the Computational Model The code provided is part of a computational neuroscience model aimed at simulating a subset of the basal ganglia circuitry, focusing on specific neural interactions related to dopamine-modulated processes. Below are the key biological aspects that this code models: ## Circuit Components - **Nuclei Modeled:** - **Striatal D1 and D2 Neurons (SD1, SD2):** These are components of the striatum, the input nucleus of the basal ganglia, classified based on their dopamine receptor type - D1 (direct pathway) and D2 (indirect pathway). The model simulates 64 neurons per channel across 3 channels for each striatal component. - **Subthalamic Nucleus (STN):** Part of the indirect pathway, it provides glutamatergic (excitatory) projections to other nuclei. - **Globus Pallidus External and Internal Segments (GPe, GPi):** These are key players in mediating motor function, with GPe primarily providing inhibitory input to other basal ganglia structures and GPi acting as one of the main output nuclei. ## Connectivity and Synaptic Transmission - **Connection Proportion:** The connection fraction (`p_connect`) is set to 0.25, reflecting the sparsity of biological synaptic connections. - **Synaptic Weights and Dynamics:** The model includes different synaptic weights for excitatory (AMPA, NMDA) and inhibitory (GABA) synapses. Dopamine levels modulate these weights, which simulate tonic and phasic influences in a dynamic network context. - **Synaptic Delays:** Axonal delays have been specified, reflecting the time neurotransmitter takes to travel across synapses, which is critical to temporal dynamics in neural circuits. ## Input and State Dynamics - **Input Types:** The model supports different input paradigms (`slow`, `tonic`, `organo`) to simulate specific experimental conditions such as slow-wave activity under anaesthesia. - **Spontaneous and Burst Currents:** Reflects intrinsic neuronal activity and the ability to generate burst firing, which is characteristic of certain basal ganglia neurons like those in the STN. ## Dopaminergic Modulation - **Dopamine Levels:** The code sets dopamine (`dop1` and `dop2`) to zero, simulating conditions without dopaminergic influence, relevant to understanding Parkinsonian states. - **Coefficient Effects:** Different coefficients (e.g., `stnda`, `gpeda`) specify dopamine's proportional effects on AMPA, GABA inputs, and spontaneous neuron activity, adding bio-realistic modulation to the model. ## Noise and Variability - **Membrane Noise:** Noise in membrane potential replicates the biological variability in neuron firing, incorporating a realistic aspect of neuronal behavior. - **Variable Resistances and Time Constants:** By using Gaussian distribution noise on time constants and resistances, the model introduces biological non-homogeneity. ## Urethane Influence - **Urethane Simulation:** Adjustments for glutamatergic and GABAergic scale (e.g., `glut_scale`, `gaba_scale`) account for the impact of urethane (an anaesthetic) on synaptic transmission, allowing exploration of neural dynamics under anaesthesia. ## Shunting Inhibition - **Redistribution of Inhibitory Synapses:** The proportion of inhibitory synapses across neuronal compartments adds a layer of realism, mimicking inhibitory control mechanisms like shunting inhibition in neuronal processes. The model ultimately aims to simulate the complex interplay within the basal ganglia, which is crucial for understanding motor control, the effects of neuromodulators like dopamine, and conditions such as Parkinson's disease. By integrating these biological components, the simulation serves as a platform for exploring basal ganglia network dynamics and pathophysiology.