The following explanation has been generated automatically by AI and may contain errors.
# Biological Basis of the Computational Model
The provided code snippet represents a section of a computational model that simulates the dynamics of a network of neurons. This type of model is used to understand how different biological properties of neurons and their connections influence overall network behavior, particularly in the context of cortical circuits that contain both excitatory and inhibitory neurons.
## Neuronal Types in the Model
1. **Excitatory Neurons (E):**
- The model includes a population of 4,000 excitatory neurons. These neurons primarily use glutamate as a neurotransmitter, which typically results in depolarization of post-synaptic targets, thereby promoting action potentials.
2. **Inhibitory Neurons (I):**
- There is a smaller population of 1,000 inhibitory neurons. These neurons typically use gamma-aminobutyric acid (GABA) as a neurotransmitter, leading to hyperpolarization of post-synaptic targets, thereby reducing the likelihood of action potentials.
## Network Properties
- **Connectivity (p):** The network has a connection probability of 0.2, indicative of sparse connectivity typically observed in cortical circuits, where each neuron does not connect to every other neuron due to anatomical and functional constraints.
- **Membrane Properties:**
- **Membrane Time Constant (τm):** Excitatory neurons have a longer membrane time constant (20 ms) compared to inhibitory neurons (10 ms), reflecting their ability to integrate inputs over a longer period before potentially firing an action potential.
- **Membrane Potential Dynamics:**
- **Leak Potential (V_leaky):** -70 mV is a common resting membrane potential for neurons.
- **Firing Threshold (Vthr):** -52 mV is the potential at which neurons will fire an action potential.
- **Reset Potential (Vres):** After firing, neurons reset to -59 mV and enter a refractory period, preventing immediate re-firing.
## Synaptic Transmission
- **Synaptic Dynamics:**
- The rise (Tr) and decay (Td) times for synaptic currents are modeled to reflect realistic dynamics of excitatory (AMPA) and inhibitory (GABA) synapses, capturing how quickly a synapse can act and how long its effects last.
- **Synaptic Reversal Potentials:**
- **Excitatory (AMPA):** 0 mV, consistent with excitatory post-synaptic currents.
- **Inhibitory (GABA):** -80 mV, consistent with hyperpolarizing inhibitory currents.
- **Synaptic Conductances (g):** Represent the strength of connections and impact the intensity of post-synaptic potentials.
## Summary
This model captures key biological features of cortical neural networks, including different neuron types and their respective properties like membrane time constants, thresholds, synaptic dynamics, and connectivity. These characteristics collectively allow it to emulate realistic neuronal behavior observed in biological systems, aiding investigations into how such properties contribute to overall network dynamics and cognitive functions.