The provided code represents a segment of a computational neuroscience model simulating synaptic connectivity and dynamics between specific neuron populations in the cortex. The key biological components and principles being modeled are:
I23LTS neurons: These are inhibitory interneurons, often identified as LTS (Low Threshold Spiking) interneurons located in layer 2/3 of the cortex. LTS interneurons are known for firing at low thresholds and play critical roles in modulating cortical excitability and synchronizing network activity through GABAergic synapses.
P23RSa neurons: These represent a subtype of excitatory pyramidal neurons found in the same cortical layers. They are referred to as RS (Regular Spiking) pyramidal neurons, which are characterized by their regular, tonic firing in response to current inputs. The "a" designation may denote a specific anatomical or physiological subclass within the regular spiking pyramids.
Synaptic Connections: The model sets up GABAergic synaptic connections (inhibitory) from the I23LTS interneurons to the P23RSa pyramidal neurons. This reflects the physiological reality where interneurons modulate the activity of excitatory pyramidal cells, influencing cortical processing.
Axonal Propagation Velocity: The axonal propagation velocity (CABLE_VEL) is a critical parameter that dictates the timing and speed of action potential transmission through axons. This velocity impacts synaptic delay and is vital in shaping temporal patterns of neural activity.
Connection Probability and Spatial Constraints: The connections are set probabilistically with spatial constraints, reflecting the realistic spatial distribution and connectivity likelihood in biological neural tissues. Morphological constraints such as synaptic location (e.g., basal or apical dendrites) represent different regions of the neurons where synapses form.
Synaptic and Axonal Delays: These parameters reflect the inherent delays in synaptic transmission and axonal conduction—a fundamental aspect of neural circuitry affecting synchronization and timing of neural signals.
Synaptic Weights: The strength of synaptic connections is modulated, incorporating mechanisms like decay rates, which emulate synaptic plasticity, a biological process where the strength of synapses changes over time due to activity levels and other factors.
This model encapsulates how intricate neural network dynamics are shaped by the interactions between different types of neurons and the spatial and probabilistic nature of their connections. Interneuron-to-pyramidal cell interactions, synaptic delays, and connection probabilities are vital for the precise regulation of cortical circuits, influencing processes such as sensory processing, memory, and cognition within the brain. The code allows the simulation of these dynamics, providing insights into how these biological systems might maintain balance and function under various conditions.