The provided code is part of a computational neuroscience model simulating synaptic connections and signal propagation between specific neuronal cell types in the neocortex, namely the P23RSc and P23RSd cells. Here's a breakdown of the biological basis behind the model:
Neuronal Types: The model focuses on two types of pyramidal cells in layer 2/3 of the neocortex, denoted as P23RSc and P23RSd. These neurons are integral to cortical processing, playing roles in sensory perceptions and higher cognitive functions.
Synaptic Connections: The model simulates synaptic connections between these neurons. Synapses can be either excitatory or inhibitory, but the focus here is on excitatory synapses mediated by two main neurotransmitter receptors: AMPA and NMDA.
AMPA Receptors: These are fast-acting ionotropic glutamate receptors that mediate rapid synaptic transmission. They are critical for the initial depolarization phase and synaptic plasticity, which refers to the strengthening or weakening of synapses over time based on activity levels.
NMDA Receptors: These glutamate receptors are also ionotropic but have slower kinetics and play a crucial role in synaptic plasticity, particularly in long-term potentiation (LTP), a cellular mechanism underpinning learning and memory. They require depolarization to relieve Mg²⁺ block and allow Ca²⁺ influx.
Synaptic Locations: The model lists a variety of synapse locations, representing different anatomical sites on the dendrites and soma where synapses can form. This mirrors the complex dendritic arborization found in pyramidal neurons, where synapse location can influence neuronal response and plasticity.
Connection Probability: The probability of synapse formation between cells is parameterized, reflecting the stochastic nature of synaptic connectivity in the brain. This respects the biological observation that not all potential synaptic connections are realized.
Delays and Propagation Velocities: The model accounts for synaptic and axonal delays in signal transmission, using parameters that mimic the biophysical properties of neuronal tissues, like axonal conduction velocities. These delays are critical for the timing of synaptic integration and coordination among neurons in networks.
Synaptic Weights and Plasticity: The use of parameters for synaptic weights and decay rates reflects mechanisms of synaptic plasticity. In the brain, synaptic strength can change with activity, and this model simulates such dynamics with decay functions representing activity-dependent weakening.
This model aims to simulate the complex dynamics of excitatory synaptic connections within a specific network of cortical neurons. By adjusting parameters like synaptic weights, probabilities, and conduction velocities, the model explores how these variables influence neuronal communication and information processing in the neocortex.
In essence, this code models a simplified version of cortical microcircuitry, attempting to mimic the physiological and anatomical characteristics of real pyramidal neuron interactions, a crucial step toward understanding cortical functions and dysfunctions in both normal and pathological states.