Based on the provided code snippet, the computational neuroscience model is simulating a network of neurons with the aim of exploring certain dynamical properties or behaviors of brain tissue. Here is a breakdown of the biological aspects being modeled: ### Neuronal Types The code hints at different types of neurons being modeled, specifically inhibitory (inh) and excitatory (ex) neurons. Inhibitory neurons, often GABAergic, decrease the likelihood of the firing of connected neurons, while excitatory neurons, often glutamatergic, increase firing likelihood. - **Inhibitory Neurons:** The code distinguishes between LTS (Low Threshold Spiking), FS (Fast Spiking), and RS (Regular Spiking) types. Each type has distinct characteristics relevant to action potential firing patterns and synaptic connectivity. - **Excitatory Neurons:** These include RS (Regular Spiking), IB (Intrinsically Bursting), and CH (Chattering) types, each contributing differently to the network dynamics. ### Synaptic Connectivity - **Connection Probability (`prob`):** This parameter (`prob`) reflects the likelihood of synaptic connections between any two neurons in the network, crucial for determining network structure and dynamics. - **Rewiring Probability for Excitatory Neurons (`probEx`):** The model accounts for modifications in how excitatory connections are established, possibly simulating plasticity-related rewiring mechanisms in the brain. ### Network Composition - **Percentage of Inhibitory Neurons (`perc_inh`):** This parameter reflects the fraction of inhibitory neurons within the network. The balance between inhibition and excitation is a critical factor in the brain’s stability and function. ### External Inputs and Stimuli - **External Stimulus Amplitude (`set_InpAmp`, `set_InpAmpAfterChange`):** This parameter models external stimuli applied to the network, potentially simulating sensory input or experimental manipulation. - **Number and Timing of Stimuli:** Parameters like `set_n_stim`, `set_tStim`, and `set_tStimAfterChange` help model the temporal dynamics of stimuli administration to the network. ### Plasticity and Dynamics - **Noise Amplitude (`noiseAmp`):** Biological neural systems are inherently noisy. This parameter captures the stochastic fluctuations present in neuronal systems. - **Network Size (`setNNeuron`) and Adjustments (`set_changeNneurons`):** The simulation likely explores size-dependent properties of neural network dynamics by modifying the number of neurons. ### Temporal Dynamics - **Time Parameters (`setTmax`, `set_dtTrajectory`):** These control the duration and resolution of the simulation, influencing how the temporal dynamics of neuronal activity are captured. ### Functionality and Output The model generates output indicative of network activity, likely producing data that can mimic biological measures such as raster plots (indicating the timing of neuronal spikes) and lifetime distributions of neuronal firing patterns. ### Overall Aim While the code specifics are computational, they simulate critical biological principles such as synaptic interactions, network dynamics, neuronal excitability, and the balance of excitation/inhibition in the brain. The aim is likely to uncover insights into how networks of neurons process information, respond to inputs, and which mechanisms contribute to their stability and adaptability in a biologically plausible manner.