# Biological Basis of Fluctuating Conductance Model The provided code simulates synaptic bombardment in neurons by modeling fluctuating synaptic conductances. This simulation is part of a broader effort to recreate in-vivo-like neuronal activity using computational models. Here are the key biological aspects represented in the code: ## Synaptic Conductances 1. **Excitatory and Inhibitory Conductances**: - The code models two types of synaptic inputs to neurons: excitatory (`g_e`) and inhibitory (`g_i`) conductances. - Excitatory conductances are typically mediated by neurotransmitters like glutamate, which depolarize the neuron, potentially leading to action potentials. - Inhibitory conductances are often mediated by neurotransmitters such as GABA, which hyperpolarize the neuron, reducing the likelihood of firing. 2. **Reversal Potentials**: - The reversal potential for excitatory conductance is set to 0 mV, which is typical for excitatory synapses as they allow positive ions to enter, making the inside more positive. - The reversal potential for inhibitory conductance is set to -75 mV, aligning with chloride-mediated inhibition that makes the internal environment more negative. ## Stochastic Nature of Synaptic Inputs 1. **Fluctuations Representing Synaptic Bombardment**: - The model includes stochastic fluctuations in synaptic conductances to simulate the random and noisy nature of synaptic inputs that neurons receive under natural conditions. 2. **Ornstein-Uhlenbeck Process**: - The fluctuating conductances are modeled using an Ornstein-Uhlenbeck (OU) process, which introduces noise in a way that reflects real-world synaptic activity (i.e., temporally correlated noise). - This process is defined by parameters such as time constants (`tau_e`, `tau_i`) and diffusion coefficients (`D_e`, `D_i`) to capture the temporal dynamics of conductance changes. ## Homeostasis and Constraints 1. **Baseline Conductances**: - `g_e0` and `g_i0` represent the baseline levels of excitatory and inhibitory conductances, providing a foundation on which fluctuations are superimposed. 2. **Physical Constraints**: - The model ensures that conductances cannot become negative, reflecting the biological constraint that ion channel conductances cannot exhibit negative values. By integrating these biological principles into the model, the code aims to accurately reflect the synaptic dynamics of neurons, contributing to a better understanding of how fluctuating conductances influence neural behavior under varying synaptic input conditions.