# Biological Basis of the Code The provided code is part of a computational neuroscience model likely aimed at simulating neural networks, including the dynamics of various types of neurons and their responses to different stimuli. Here are the key biological aspects reflected in the code: ## Neuronal Types and Parameters The code models various neuronal types, each with distinct properties that reflect real-world neuronal behavior: - **Regular Spiking (RS) Excitatory Cells**: These neurons are characterized by parameters such as resting membrane potential (RMP), threshold potential (VTH), and refractory period, which determine their firing behavior. The RS cells are typically pyramidal neurons in the cortex. - **Low-Threshold Spiking (LTS) Interneurons**: These interneurons have a low threshold for firing action potentials, indicated by a distinct VTH. They typically serve inhibitory roles and are crucial in oscillatory network dynamics. - **Fast Spiking (FS) Interneurons**: Known for their ability to fire rapidly, these interneurons have parameters set to ensure quick recovery and high-frequency firing, playing a role in network inhibition. ## Synaptic Dynamics The code includes mechanisms for simulating synaptic interactions: - **Weight Matrices (wmatex)**: This structure sets the synaptic weights of external inputs, mimicking synaptic strengths in the brain. This includes modulatory effects (`attendmod`) that reflect attention-related synaptic plasticity. - **Neurotransmitter Dynamics**: Parameters like `tauGA`, `tauAM2`, and `tauNM2` represent time constants for various receptors, such as GABAergic (GA) and glutamatergic receptors (AMPA and NMDA), influencing the synaptic conductances and kinetics, thereby affecting neuronal firing patterns. ## Neuromodulation and External Inputs - **Exogenous Inputs and Gain**: The external stimuli settings, such as `EXGain` and varying frequencies for excitatory and inhibitory inputs (e.g., `sgrhzE`, `sgrhzI`), simulate the modulatory effects occurring due to external sensory inputs and transitions between network states, like different attention levels. - **Schizophrenia Model**: The `schizon` procedure reflects how certain parameters, like the conductance of NMDA receptors, could be altered to mimic conditions present in schizophrenia, affecting excitatory-inhibitory balance and synaptic efficacy. ## Shock Simulation The model incorporates mechanisms to simulate sudden changes or "shocks": - **Shock Parameters**: Structures like `wshock` and `durshock` are used to simulate sudden bursts of activity, which could represent physiological events such as seizures or artificially induced deep brain stimulation. ## Stochasticity and Randomness - **Random Seed Usage**: The parameters `seed_stats` and `rdm.MCellRan4` introduce variability in the simulation, reflecting the inherent stochastic nature of synaptic transmission and neuronal firing. ## Cellular Dynamics Specific parameters detailed for each cell type, such as `tauahp` (afterhyperpolarization time constant) and `ahpwt` (afterhyperpolarization weight), simulate ionic channel dynamics that determine how neurons return to baseline after spikes, impacting firing patterns and rhythmicity. The code aims to capture the complexity of real neuronal networks, allowing for the study of how different types of neurons interact under various physiological and pathological conditions. This can provide insights into higher-level brain functions and their dysfunctions in disorders like schizophrenia.