The provided code is part of a computational model designed to simulate neural networks using an event-driven framework known as "Firnet NeuroScience" (FNS). This model is premised on the Leaky Integrate-and-Fire (LIF) neuron model, widely utilized in computational neuroscience to mimic the behavior of real neurons. Below are the key biological aspects that the code aims to model: ## Biological Basis of the Code ### Neuron Dynamics - **Integrate-and-Fire Model**: The implementation is based on the Leaky Integrate-and-Fire (LIF) model, a simplified representation of neuron behavior where each neuron integrates incoming signals over time and "fires" (produces a spike) when the membrane potential exceeds a certain threshold. ### Synaptic Connections - **Excitatory and Inhibitory Synapses**: The parameters `mu_w_exc` and `mu_w_inh` suggest modeling of excitatory and inhibitory synapse strengths, respectively. Biological neurons communicate through these synapses, which either increase (excitatory) or decrease (inhibitory) the likelihood of the postsynaptic neuron firing. - **Synaptic Plasticity**: The variables `etap`, `etam`, `taup`, and `taum` are indicative of synaptic plasticity mechanisms, possibly related to spike-timing-dependent plasticity (STDP). This reflects the biological ability of synapses to strengthen or weaken over time based on activity. ### Network Topology - **Small-World Topology**: The boolean `small_world_topology` indicates modeling based on small-world networks. In biological neural networks, a small-world topology allows for an efficient balance between local and global connectivity, facilitating quick information transfer. - **Rewiring Probability**: The parameter `rewiring_P` suggests the inclusion of a mechanism for changing connections within the network, resonating with neuroplasticity observed in biological systems where neural connections are not static. ### External Inputs - **External Stimuli**: The parameters prefixed with `external_inputs_` (e.g., number, type, time offset, amplitude) model external inputs to the network, mimicking sensory input or other external stimuli affecting neuron firing rates, akin to real neural networks receiving sensory or environmental inputs. ### Population and Structure - **Node Population**: Parameters like `n` (number of neurons) and `Bn` (perhaps burst number) relate to the population size and structural characteristics of the network, drawing parallels to neuron populations in specific brain regions or layers. - **Inhibitory and Excitatory Ratio**: The parameter `R` reflects the ratio between excitatory and inhibitory neurons, crucial for maintaining balance in neural circuits akin to the balance observed in cortical microcircuits. ### Overall Objective The simulator's goal is to emulate spiking neural network dynamics, focusing on the timing of spikes, synaptic strength changes, and network topology. By adopting biologically inspired models and parameters, this simulation can investigate how these neural components interact to produce complex behaviors and processing capabilities characteristic of real biological systems. This high-level simulation offers insights into how biological neural networks can be modeled computationally, helping to bridge the gap between abstract mathematical models and real-world neurophysiological phenomena.