# Biological Basis of the Code The provided code simulates synaptic bombardment on neurons by fluctuating synaptic conductances, representing the stochastic nature of synaptic inputs that neurons receive. Here's a breakdown of the biological basis being modeled: ## Synaptic Bombardment Neurons in the brain constantly receive inputs from other neurons through synapses. These inputs can be excitatory or inhibitory, contributing to the neuron's membrane potential fluctuations. The code models this synaptic bombardment using two fluctuating conductances: excitatory (`g_e(t)`) and inhibitory (`g_i(t)`). ## Reversal Potentials - **Excitatory Reversal Potential (`E_e`)**: Typically closer to 0 mV, representing the effect of cations like Na⁺ ions flowing into the neuron, making it more likely to fire an action potential. - **Inhibitory Reversal Potential (`E_i`)**: Set negatively (e.g., -75 mV) to mimic the effect of anions (like Cl⁻) entering the neuron, making it less likely to fire. ## Conductances and Noise - **Average Conductances (`g_e0`, `g_i0`)**: Represent baseline synaptic input levels. - **Stochastic Processes (OU Process)**: The code employs Ornstein-Uhlenbeck (OU) processes to describe the random fluctuations around these average conductance levels. - **Time Constants (`tau_e`, `tau_i`)**: Represent how quickly the conductance returns to its baseline. - **Diffusion Coefficients (`D_e`, `D_i`)**: Determine the magnitude of conductance fluctuations based on synaptic noise characteristics. ## Implementation Details - The model is implemented as a point process, a common approach in neuronal simulations to add localized conductances at specific points (e.g., on dendrites or soma). - **Gaussian White Noise (`Ft`)**: Applied to represent the random fluctuations inherent in synaptic transmission. ## Biological Significance By incorporating these elements, the model captures two crucial aspects: 1. **Synaptic Variability**: Reflects the natural variability and randomness in synaptic transmission seen in vivo, including spontaneous neurotransmitter release and stochastic receptor opening. 2. **Circuit Dynamics**: Helps simulate how large-scale interactions between excitatory and inhibitory inputs influence neuronal firing patterns, essential for understanding network behaviors like oscillations, synchronization, and information processing in neural circuits. ## Reference to In Vivo Conditions The model aims to recreate in-vivo-like activity, focusing on reproducing realistic neuronal behaviors observed in experiments, such as variability in synaptic inputs, which are pivotal for producing realistic neuronal dynamics and responses.