# Biological Basis of the Code in FNS Framework The provided code snippet is part of the FNS (Firnet NeuroScience) framework, which is focused on modeling spiking neural networks (SNNs). This framework uses a specific neuron model, named the Leaky Integrate-and-Fire with Latency (LIFL) model, to simulate neurons and their interactions. Here's a description of the biological concepts represented in the code: ## Neuron Models - **Leaky Integrate-and-Fire (LIF) with Latency Model**: The LIF model is a simplified representation of a neuron. It is designed to capture the essential properties of neuronal membrane dynamics: - **Membrane Potential**: The LIF model integrates incoming electrical currents over time, similar to accumulating charge on a capacitor. When this buildup reaches a certain threshold, the neuron "fires" or produces a spike, which is analogous to an action potential in biological neurons. - **Leakage**: The model accounts for the passive decay of the membrane potential over time, mimicking the conductive properties of neuronal membranes which leak ions. - **Latency**: The LIFL model includes a latency aspect, which introduces a delay between the integration reaching the threshold and the firing event. This feature aims to capture the propagation delays that occur along axons and synaptic connections in biological neurons. ## Neural Network Dynamics - **Spiking Events**: The framework records and manages spiking events, mirroring the firing (action potentials) of real neurons. Spikes are central to how neurons communicate in the brain, transmitting information by these discrete electric signals. - **Excitatory and Inhibitory Neurons**: The `collectFireSpike` method includes a parameter called `isExcitatory`, indicating whether a neuron increases (excitatory) or decreases (inhibitory) the activity of connected neurons. This dynamic is crucial in balancing neural activity and stabilizing network function. ## Synaptic Connections - **Burning Spikes**: The term "burning" might relate to synaptic processing where a spike results in neurotransmitter release, influencing post-synaptic neuron activity. This refines the computation to account for the neurotransmitter dynamics at synapses, crucial for synaptic transmission in biological networks. - **Time Delays and Compression**: The code also attempts to simulate time delays in neural signal propagation (axon delays) and compression of neuronal signals, enabling complex temporal dynamics similar to those seen in biological brains. ## Network Statistics - **Activity Monitoring**: The code snippets related to `collectActive`, `collectPassive`, `collectPassive2active`, and `collectFireSpike` relate to monitoring neuron activities and state changes. This reflects attempts to track key dynamics in neural network simulations, akin to how neuroscientists measure neural activity in vivo using techniques like electrophysiology. - **Missed Fires and Delays**: It keeps track of missed spikes and minimum axonal delays, emphasizing the importance of temporal precision and signal propagation in brain microcircuitry. The broader aim of such a framework is to provide a computationally efficient way to simulate large-scale neural networks, drawing from and mirroring biological principles of nervous system operation. This allows exploration of various hypotheses about neural function, processing, and behavior based on established biological insights.