# Biological Basis of the Computational Neuroscience Model The provided code is part of a computational neuroscience model that simulates synaptic interactions within a neural network. The focus of the code is on the propagation of synaptic signals and the updating of neuronal states and synaptic weights, reflecting processes in a biological neural network. Below are the key biological aspects that the code attempts to model: ## Neuronal Units and Synaptic Links ### Neurons - **Units (Neurons)**: The model consists of units representing neurons, each with an output and state that are updated over discrete time steps (`N_STEPS`). The neurons' states are likely their membrane potentials or other state variables related to neural activity. - **Neuron State and Output**: Functions like `UPDATE_NEURON` compute the neuron's input, potential (state), and output, similar to a biological neuron's processing of inputs to produce outputs based on its state. ### Synapses and Signal Propagation - **Synaptic Links**: The structure `LINK` represents synapses connecting neurons. Each link contains weights and delays, mimicking synaptic strength and transmission delay seen in biological synapses. The function `PROPAGATE_LINK_SIGNAL` models the propagation of signals across these synaptic delays, which is akin to how synaptic inputs are temporally integrated in real neurons. - **Signal and Spike Amplitudes**: Each synapse maintains signals and spike amplitudes over time delays, representing the temporal dynamics of synaptic transmission and the influence of neurotransmitter release. ## Synaptic Plasticity ### Hebbian Learning - **Hebbian Mechanisms**: The code includes references to Hebbian learning rules (e.g., `HEBB_ASS_PC`, `HEBB_MC_GC`), suggesting synaptic plasticity. These rules are biological plausible learning mechanisms where synaptic strength is adjusted based on the coincidence of pre- and post-synaptic activity, in line with the adage "cells that fire together, wire together." ### Weight Measurement and Adaptation - **Synaptic Weight Measurement**: Functions like `MEASURE_WEIGHTS` are used to assess synaptic weights, reflecting the model's capacity to track changes in synaptic strength over time, similar to tracking synaptic efficacy experimentally. - **Forgetting Mechanisms**: The code contains mechanisms (e.g., `FORGET_MC_GC`) for synaptic weight decay ("unlearning"), mimicking biological processes where synapses weaken over time without reinforcement. ## Network Dynamics ### Synaptic and Neuronal Modulation - **Parameter Modulation**: The code includes parameters like `gmax`, `tau1`, `tau2`, and `mod`, which are used in the calculation of signal propagation. These likely correspond to maximum synaptic conductance, decay times, and modulatory factors that influence synaptic efficacy, echoing how various biophysical and biochemical factors modulate synaptic strength in real neurons. ### Diverse Neuronal Types - **Neuronal Types**: The code refers to different types of neurons (e.g., `granule`, `mitral2`, `pyr`). This indicates an attempt to represent diverse neuronal populations found in biological systems, each with potentially distinct functional roles in the network. ## Summary The code models the dynamics of neuronal activity and synaptic interaction, incorporating elements of synaptic transmission, membrane potential dynamics, and synaptic plasticity closely tied to biological neural systems. Such models aim to provide insights into how neurons process information, how synaptic strengths are adjusted based on learning rules, and how this contributes to overall network function.