The presented code is a computational model representing a basic neural network, which is a simplified abstraction of a biological neural system. The purpose of this model is to simulate the connectivity and interaction between neurons based on certain probabilistic rules and predefined parameters. Here are the key biological aspects the code attempts to capture: ### 1. **Cell Types and Composition** The model differentiates between two types of neurons: - **Excitatory Neurons (E-cells):** Represented by the variable `N_E`, these are the neurons that release neurotransmitters causing depolarization of the target neuron, typically leading to an action potential. - **Inhibitory Neurons (I-cells):** Denoted by `N_I`, these neurons release neurotransmitters that generally cause hyperpolarization, reducing the probability of an action potential. The ratio of inhibitory to excitatory neurons is approximately 1:4, a common ratio observed in many parts of the mammalian brain. ### 2. **Connectivity** - **Connection Probability:** The model uses a 2% (`0.02`) connection probability as a parameter for connecting neurons. This indicates the likelihood that a given neuron will connect to another neuron. This low probability reflects the sparse connectivity found in biological neural networks. - **Synapses:** The model establishes both excitatory (`C_E`) and inhibitory (`C_I`) synaptic connections for each neuron. In a biological context, these connections are sites where neurons communicate through neurotransmitter release. ### 3. **Synaptic Parameters** - **Synaptic Strength (Conductance):** - **AMPA_GMAX:** Represents the maximum conductance of AMPA receptors, which mediate fast synaptic transmission in excitatory neurons, modeled here with a peak conductance of `0.006 μS`. - **GABA_GMAX:** Represents the maximum conductance of GABA receptors, essential for inhibitory synaptic transmission, with a modeled peak conductance of `0.067 μS`. These parameters determine how strong the synaptic effect of a neurotransmitter is upon release and are crucial in setting the dynamics of the neural network. ### 4. **Temporal Dynamics** - **Delay:** Set to `0 ms` in this model, which suggests instantaneous propagation of action potentials from presynaptic to postsynaptic neurons. Although this simplification does not capture the full biological latency, it simplifies the computation while allowing study of network dynamics. ### 5. **Randomness and Stochasticity** - The random distribution of synaptic connections mimics the inherent stochastic nature of biological systems, as real neural connectivity shows variability and is influenced by developmental processes and learning. ### Summary This computational model seeks to capture the essence of neural network biology, focusing on neuron types, their roles, connectivity patterns, and synaptic dynamics. While simplified, it enables the study of fundamental neural interactions, excitatory and inhibitory balance, and network behavior, which are critical to understanding the function and computation of biological neural systems.