Biological Basis of the Code

The provided code snippet is part of a computational neuroscience model that is simulating synaptic connectivity patterns and interactions between specific types of neuronal cells: the P23RSb (Layer 2/3 regular spiking pyramidal cells) and the P6RSb (Layer 6 regular spiking pyramidal cells). The aim is to replicate the synaptic communication and dynamics observed in the cortical column.

Key Biological Concepts

1. Neuronal Layers and Cell Types:

   • P23RSb cells: Regular spiking pyramidal neurons located in layer 2/3 of the cortex. These cells typically form intracortical circuits and are known for their role in associative processing and integration of cortical information.
   • P6RSb cells: Regular spiking pyramidal neurons found in layer 6. They often project to both cortical and subcortical targets and play a role in modulating cortical outputs and feedback processing.

2. Synaptic Connections:

   • The model simulates excitatory synaptic connections from P23RSb to P6RSb neurons. It involves:
     • AMPA Receptors: Fast excitatory synaptic transmission is mediated by AMPA-type glutamate receptors.
     • NMDA Receptors: Both AMPA and NMDA receptors are incorporated. NMDA receptors are slower and voltage-dependent, contributing to synaptic plasticity and learning.

3. Spatial Organization and Connection Probability:

   • The code uses volume connections to simulate spatially-organized synaptic interactions. The sourcemask and destmask parameters define regions from which connections are sourced and targeted, which mimics the spatial constraints within cortical layers.
   • A connection probability factor (P23RSb_P6RSb_prob) regulates how likely it is for a synaptic connection to form between these cells, reflecting the sparse but highly structured connectivity of the cortex.

4. Propagation Delays:

   • Axonal Propagation Velocity: This factor influences how quickly action potentials travel along axons between the P23RSb and P6RSb neurons, incorporating realistic synaptic delays.
   • Synaptic Delays: These delays account for the time it takes for neurotransmitter release, binding, and reactivation of the postsynaptic neuron.

5. Weight Setting and Synaptic Plasticity:

   • The code implements synaptic weight setting, which determines the strength of each synapse based on distance (volumeweight). This reflects the biological principle that synaptic efficacy can diminish with longer axonal paths, contributing to the phenomenon of synaptic scaling.

6. Stochastic Elements:

   • Randomization with Gaussian distributions for delay and weight (volumedelay and volumeweight) captures the variability observed in synaptic properties across different connections, analogous to biological diversity in synaptic strengths and timings.

In summary, this model script is a detailed attempt to replicate the biological mechanisms governing synaptic connections and neurotransmission between specific cortical layers, emphasizing the spatial organization, transmission time delays, and variability inherent in neuronal networks.