# Biological Basis of the Computational Model The given code is a computational model representing synaptic interactions and dynamics between different types of neurons commonly found in cortical circuits, specifically focusing on pyramidal neurons and two types of inhibitory interneurons. ## Neuronal Types - **Pyramidal Neurons (E-cells)**: These are the primary excitatory neurons in the cortex, represented as `E-cells` in the code. Their numbers are set to 80, highlighting their abundance in the cortical layers relative to inhibitory interneurons. - **Parvalbumin-expressing Interneurons (PV, I-cells)**: These fast-spiking interneurons are characterized by their rapid inhibitory signals and are modeled with 10 cells in this code. They play a crucial role in regulating the timing and synchronization of neural networks. - **Cholecystokinin-expressing Interneurons (CCK, I2-cells)**: Known for slower inhibitory post-synaptic currents, these interneurons contribute to the modulation of cortical activity over longer time scales. They are represented with 20 cells. ## Synaptic Dynamics - **Synaptic Connections and Densities**: The code sets a high density of synaptic connections between the neuronal populations, implying a high probability of synaptic interactions. - **Synaptic Time Constants**: The rise and decay times for excitatory and inhibitory synapses reflect biologically relevant properties, with fast excitation (`tau_r_e` and `tau_d_e`) and both fast (`tau_r_i`, `tau_d_i`) and slow inhibition (`tau_r_i2`, `tau_d_i2`), derived from known data sources. - **Synaptic Strength**: The conductance parameters (`g_hat`) define the strength of synaptic coupling between different neuron types. These values are drawn from empirical studies, suggesting the model's aim to reproduce realistic synaptic interactions observed in biological experiments. ## Membrane Properties - **Membrane Capacitance**: The code calculates membrane capacitance based on recorded surface area and other electrical properties, providing a measure of the neuron’s ability to store and release charge, important for determining the timing of neuronal firing. ## Reversal Potentials - **Synaptic Reversal Potentials**: `v_rev_e` and `v_rev_i` determine the direction of ion flow during synaptic events, crucial for modeling excitatory and inhibitory postsynaptic potentials. ## External Inputs - **Deterministic Drives**: A constant external input is provided to E-cells (`I_e(t)`) to simulate a baseline level of activity, whereas inhibitory interneurons receive no deterministic drive, reflecting their primarily modulatory role. - **Poisson-like Stochastic Inputs**: Although set to zero in this configuration, the code allows for stochastic synaptic inputs via a Poisson process, which can mimic random synaptic events typical in neural circuits. ## Biological Context The model aims to capture the synaptic interactions and membrane dynamics of a cortical neural network, with a focus on areas like the anterior cingulate cortex (ACC) and the amygdala (AMY). By setting parameters based on empirical research, the model strives to simulate the interplay between excitatory and inhibitory influences in the cortex, helping to understand neural processing underlying cognitive and emotional functions.