## Biological Basis of the Code The provided code simulates a network of neurons, modeling the dynamics and interactions within a neural network in a computational setting. It encapsulates various biological processes fundamental to neuronal activity and synaptic transmission. Here's a breakdown of the key biological aspects represented in the code: ### Neuron Simulation The simulation initializes a set of neurons with specific parameters. Each neuron in a biological context seeks to replicate the characteristics of actual biological neurons, which includes: - **Membrane Potential (V_m):** The key variable `nn_params(:,1)` represents the membrane potential of each neuron, capturing the essential electrical state necessary for excitation and the generation of action potentials. - **Synaptic Inputs:** Synaptic inputs influence the membrane potential. In biology, synaptic inputs are received through synapses, where neurotransmitters mediate communication between neurons, affecting membrane potential and, consequently, neuronal firing. ### Synaptic Transmission The code models synaptic transmission through three primary neurotransmitter systems: - **AMPA Receptors:** These receptors mediate fast synaptic transmission in the central nervous system. The `net_AMPA` array reflects the influence of AMPA-mediated currents on the neurons, which often lead to excitatory post-synaptic potentials. - **GABA Receptors:** GABA neurotransmission leads to inhibitory effects in neurons, as represented by the `net_GABA` array. Delays and strengths for GABA are used to mimic its synaptic dynamics that typically result in hyperpolarization of the membrane. - **NMDA Receptors:** Represented by `net_NMDA`, NMDA receptors contribute to synaptic plasticity and memory function. They are unique due to their voltage-dependent properties and their role in long-term potentiation. ### Synaptic Inputs and Delays The model incorporates synaptic delay for each neurotransmitter. Delays (`sim.net.delay_GABA`, `sim.net.delay_AMPA`, etc.) are critical in capturing the temporal dynamics of synaptic transmission which in turn affects the timing and integration of neuronal inputs. ### External Inputs Inputs external to the network are modeled to affect synaptic currents. This mimics real-world scenarios where neurons receive input from other brain areas or sensory stimuli, critically affecting neuronal excitability and synchronization. ### Activity and Instrumentation Activity within the network is tracked by measuring recent activation (`recent_act`) relative to a threshold (`sim.activity_thr`). In biological neurons, action potentials occur upon reaching a threshold, which is necessary for signal transduction and neural code. ### Overall Network Dynamics The main loop of the code tracks the development of the neural network over time. It processes synaptic connects through pre-synaptic terms, reflecting how populations of neurons would interact and process collective input, mimicking real neural network dynamics in the brain. This script is representative of how computational models strive to translate the complex interactions and dynamics observed in biological neurons and synapses into mathematical frameworks to study behavior, learning, and network function in silico.