# Biological Basis of the Model Code The provided code snippet represents a computational neuroscience model of a neural circuit composed of multiple interconnected brain areas, aimed at simulating stimulus processing and response in the brain. This type of model often draws inspiration from biological principles of neural dynamics and synaptic interactions. Here's a breakdown of the biological basis for key components in the code: ## Biological Basis ### Network of Wong-Wang Areas - **Wong-Wang Model:** The model utilizes a variation of the Wong-Wang framework, which is a phenomenological model describing large-scale neural interactions across cortical areas. This framework is known for modeling decision-making processes and attentional dynamics by characterizing the competition between populations of excitatory and inhibitory neurons. ### Excitatory and Inhibitory Populations - **Excitatory and Inhibitory Neurons:** The code implements separate populations for excitatory and inhibitory neurons. Excitatory neurons increase the likelihood of firing in their target neurons, whereas inhibitory neurons decrease it. In biological neural circuits, these two types of neurons form the basis of signal integration and processing across brain regions. ### Conductances and Synaptic Interactions - **NMDA and GABA Conductances:** The model includes NMDA (N-methyl-D-aspartate) and GABA (gamma-aminobutyric acid) conductances to simulate synaptic interactions. NMDA conductances are associated with excitatory synaptic input and are important for plasticity and memory formation. GABA conductances correspond to inhibitory input, critical for controlling neural excitability and maintaining network stability. ### Rate-Based Neuronal Dynamics - **Firing Rates:** The neuronal population dynamics are expressed in terms of firing rates, a common simplification in neuro-computational models. Firing rates represent the average activity level of a group of neurons over time, permitting the simulation of large-scale neural activity without tracking each spike. ### Noise and External Stimuli - **Noise Simulating Variability:** Gaussian noise is added to simulate biological variability in neuronal responses, mimicking the inherent randomness and variability seen in neural systems. - **External Input (Iext):** The model includes external stimuli inputs, mimicking sensory inputs that would activate these brain regions. This drives the population dynamics, akin to how stimuli influence neuronal activity and perception in biological brains. ### Area-Specific Dynamics and Inter-Area Connections - **Network with Multiple Areas:** The code models activities across 30 cortical areas, simulating the interconnected network found in the mammalian brain. Each area has its excitatory and inhibitory populations influenced by intrinsic and extrinsic factors. - **Gradient and Long-Range Effects:** Long-range inhibitory and excitatory interactions between areas (modulated by `We` and `Wi`) mimic inter-areal corticocortical connections, and gradients (`Jnsgrad`, `Jniegrad`) capture area-specific connectivity strengths or influences. ### Lesions - **Lesion Induction:** By allowing the "lesion" of specific areas, the code can simulate the effects of stroke or focal lesions on network dynamics. Lesions are implemented by setting the firing rates to zero, a representation of neuronal inactivity due to damage. Overall, this computational model seeks to emulate the complex dynamics of brain networks influenced by both local interactions within brain regions and long-range connections. Key components reflect core biological themes: excitatory/inhibitory balance, network connectivity, synaptic dynamics, and the disruptive effects of neural lesions.