Biological Basis of the Code

The provided code models spontaneous synaptic noise as a conductance trace in a neuron by simulating the random activity of synapses. It aims to capture the stochastic nature of synaptic inputs that neurons receive, which is an inherent characteristic of neural communication in the brain.

Key Biological Concepts

1. Spontaneous Synaptic Activity:

   • Neurons exhibit spontaneous synaptic activity, meaning that even in the absence of a specific external input, synapses can randomly release neurotransmitters. This background synaptic noise plays a crucial role in maintaining the excitability of neurons and modulating their response to other inputs.

2. Synaptic Conductance:

   • Conductance changes at the synapse level are modeled here. When neurotransmitters are released into the synaptic cleft, they bind to receptors on the postsynaptic neuron, leading to a change in ion flow across the postsynaptic membrane. This change is represented as a conductance in computational models, which affects the membrane potential and excitability of neurons.

3. Poisson Distribution for Vesicle Release:

   • Vesicle release from the presynaptic neuron at synapses often follows a Poisson distribution, reflecting a random and independent release process. The code simulates vesicle release using a Poisson probability distribution (poissrnd), mirroring the biological process by which neurotransmitter-containing vesicles release their contents in a seemingly random manner.

4. Kinetics of Synaptic Conductance:

   • The model uses an alpha function to represent the time course of synaptic conductance changes (characterized by syn_tau). In biological terms, this alpha function approximates the dynamics of postsynaptic receptor activation and deactivation after neurotransmitter binding, capturing the time delay and duration of synaptic inputs.

5. Synaptic Mapping and Architecture:

   • The concept of a synaptic map in the code represents the physical or functional distribution of synapses on the neuron's dendrites. This aligns with the biological reality where synaptic density and distribution vary across the neuron, contributing to the spatial integration of synaptic inputs.

Conclusion

This code models the spontaneous synaptic noise by simulating conductance changes due to random synaptic activity. The use of a Poisson distribution to model vesicle release, and an alpha function for conductance time courses, reflects biological processes of neurotransmitter release and receptor kinetics. By capturing these stochastic and kinetic properties, the model mimics how spontaneous activity influences neuronal excitability and information processing in the brain.