Biological Basis of the Computational Model Code

The provided code is a part of a computational neuroscience model focusing on synaptic connections and signal propagation between two distinct types of neurons labeled as P5IBb and I23LTS. The model uses these connections to explore the dynamics of neural circuits, simulating synaptic transmission and its effects on network activity. Here, we interpret the biological relevance of the components in the code:

Neuronal Types and Synapses

1. P5IBb Neurons:

   • Likely represent a certain type of excitatory neuron possibly located in layer 5 of the cortex (indicated by "P5"). These neurons are involved in shaping output signals towards other neurons.

2. I23LTS Neurons:

   • Represent a type of inhibitory neuron, indicated by the "LTS" label, which stands for "Low-Threshold Spiking". These neurons are typically inhibitory interneurons, often found in cortical layer 2/3. They are known to regulate local circuits' excitability and are involved in feedforward and feedback inhibition.

Synaptic Mechanisms

1. AMPA and NMDA Receptors:
   • The model simulates synaptic connections between P5IBb neurons and I23LTS neurons through AMPA and NMDA receptors.
   • AMPA Receptors: Mediate fast excitatory synaptic transmission.
   • NMDA Receptors: Contribute to slower excitatory transmission and are crucial for synaptic plasticity due to their voltage-dependent magnesium block and calcium permeability.

Connectivity and Synaptic Propagation

1. Volume Connectivity:

   • The volumeconnect functions establish spatially constrained synaptic connections, reflecting the spatial distribution and target specificity of synaptic projections in the brain.
   • Connectivity patterns are defined using spatial masks indicating restricted regions, which biologically mirrors targeted synaptic branching.

2. Axonal Propagation:

   • The propagation velocity of action potentials along the axon is defined, crucial for timing-dependent plasticity and overall network synchrony.
   • Stochasticity in synaptic delays is introduced using Gaussian distributions, representing biological variability in synaptic signal transmission.

3. Synaptic Delays and Weights:

   • Synaptic delays are adjusted to capture the variability in neurotransmitter release mechanisms and postsynaptic response times.
   • Synaptic weights settings involve factors for decay, maximal, and minimal weights, reflecting synaptic strength modulation essential for processes like learning and memory.

Summary

In modeling, the interactions between P5IBb and I23LTS neurons through AMPA and NMDA receptors, the code mimics key aspects of cortical microcircuits. These components are fundamental in understanding information processing, synaptic plasticity, and dynamic behavior of neuronal networks observed in biological systems. The architecture and parameters set in the simulation are tailored to reflect biological connectivity, statistical variability in synaptic signaling, and adaptability crucial for realistic neuronal modeling.