Biological Basis of the Computational Model

The computational neuroscience model described in the code simulates a network of neurons involved in the basal ganglia circuitry, a group of subcortical nuclei in the brain known for their significant role in motor control, learning, and other functions. The model focuses on simulating gamma-band oscillations in the presence of dopamine modulation through the use of a D2 agonist.

Key Biological Components Modeled

Basal Ganglia Nuclei

The code models five main nuclei of the basal ganglia:

• Striatal D1 Neurons (SD1): These neurons express dopamine D1 receptors and are part of the direct pathway, which facilitates movement.
• Striatal D2 Neurons (SD2): These neurons express dopamine D2 receptors and are part of the indirect pathway, which inhibits movement.
• Subthalamic Nucleus (STN): Plays a critical role in the regulation of movement and is involved in the indirect pathway.
• Globus Pallidus Interna (GPi): The primary output nucleus projecting to motor-related areas, responsible for inhibitory control.
• Globus Pallidus Externa (GPe): Modulates activity between the STN and GPi, acting as an intermediate relay in the indirect pathway.

Dopamine Modulation

• The model includes parameters for dopamine levels (dop1 and dop2) to simulate tonic dopamine influence. Dopamine plays a crucial role in modulating the activity of the basal ganglia, affecting motor control and reward-based learning.

Synaptic Connections

• AMPA, NMDA, and GABA: These are receptor types involved in excitatory (AMPA, NMDA) and inhibitory (GABA) synaptic transmission. The code specifies time constants and synaptic weights associated with these receptors, reflecting their varying contributions to post-synaptic potentials (PSPs).

Neuronal Parameters

• Membrane Potential Dynamics: The model specifies parameters like membrane time constants, resistance, and noise—indicating the variability in neuronal response due to synaptic input and intrinsic properties.
• Firing Thresholds and Refractory Periods: Parameters like firing thresholds and refractor periods are set to realistically simulate action potential generation and subsequent neuron resetting.

Network Properties

• Connectivity: The connection proportion (p_connect) reflects the probability of synaptic connections between neurons, representing the sparse connectivity typically seen in biological neural networks.
• Burst Currents and Spontaneous Activity: Burst-current parameters and spontaneous input currents are included to simulate intrinsic neuronal firing behavior, particularly for neurons in the STN.

Input and Output Processing

• Tonic and Phasic Inputs: The model uses tonic (regular) input patterns and allows for phasic (burst-like) changes in input, reflecting different modes of network operation seen in vivo.
• Recording of Neural Activity: The code highlights the tracing of specific neuronal activity for detailed study, indicative of simulating and analyzing biophysical properties like membrane potential and synaptic inputs.

Applied Biological Factors

• Urethane Manipulation: The code includes a mechanism for simulating the effects of urethane, an anesthetic known to modify synaptic transmission, thereby exploring the effects of altered neural dynamics under different physiological states.

In essence, the provided code simulates gamma-band oscillations in the basal ganglia network under the influence of dopamine, emphasizing the biological roles of key neural pathways, neurotransmission, and intrinsic neuronal dynamics. Such models assist in understanding diseases like Parkinson’s, where these pathways are heavily implicated.