The code simulates neural activity in a computational model, aiming to capture key dynamics of a network of excitatory and inhibitory neurons. Below, the biological relevance of different components in the code is elucidated: ### Neuron Types and Populations - **Excitatory and Inhibitory Neurons:** The network consists of excitatory (Ne) and inhibitory (Ni) neurons where 75% are excitatory and 25% are inhibitory. This distribution is characteristic of many regions in the mammalian cortex, reflecting their natural abundance and role in processing information. ### Membrane and Ion Channels - **Membrane Dynamics:** The code models membrane potential dynamics (voltage changes) using a series of differential equations that incorporate various ion channels. - **Ion Channels:** The neurons are characterized by channels for sodium (\( Na^+ \)) and potassium (\( K^+ \)), which play critical roles in generating action potentials. These are represented by variables such as `gne`, `gki`, `gnae`, and `gna`, which symbolize conductances for respective channels. They help the neuron reach excitation thresholds (v_the and v_thi), leading to action potentials. ### Gating Variables - **Gating Dynamics:** The gating variables (`minf`, `winf`) represent the probability of a channel being open at a given membrane potential. The variables are involved in the regulation of ionic currents that maintain the resting potential and facilitate action potential initiation. - **Biophysics of Gating:** These variables are updated according to voltage-dependent models (`tanh` functions) that reflect biological measurements of voltage-dependent activation and inactivation. ### Reversal Potentials - **Reversal Potentials (Ek):** The excitatory and inhibitory neurons have different potassium reversal potentials (`vke`, `vki`), initially set to a pathological value and transition to physiological values. This reflects the impact of ion concentration changes on neural excitability and firing rate. - **Dynamic Changes:** The gradual changes from a pathological (\(-60mV\)) to physiological range (\(-70mV\) for excitatory and \(-80mV\) for inhibitory neurons) indicate attention to disease or altered state modeling, which affects how ions such as potassium govern neural excitability. ### Synaptic Interactions - **Synapse Model:** Synaptic interactions incorporate excitatory (\( ve \)) and inhibitory (\( vi \)) reversal potentials. This represents the post-synaptic potential changes due to neurotransmitter binding affecting ion channel states. - **Synaptic Decay and Coupling:** The time constants (`taue`, `taui`) model the rate of synaptic current decay following a post-synaptic potential, while coupling strengths (`Jee`, `Jie`, etc.) represent synaptic weights or efficacy in the network, influencing how strongly neurons affect each other. ### Input and Stimulation - **External Inputs:** The model includes Poisson-based impersonation of synaptic inputs reflecting spontaneous neural activity, with the `stim` and `exc_base` variables capturing transient increases in activity, akin to external stimulation the cortex might receive. ### Network and Activity - **Network Dynamics:** The model captures population-level dynamics, measuring excitatory and inhibitory neuron firing rates, which collectively contribute to network synchronization and stability, crucial for healthy brain function. Overall, the code attempts to replicate biological neural networks by capturing aspects of membrane electrophysiology, ionic conductance, synaptic interaction, and network dynamics, mimicking living organism neural responses, possibly under altered conditions reflective of diseases affecting ion channel functionality.