The code provided is a simulation of a ring network model, commonly used in computational neuroscience to study neural dynamics and network behavior. The biological basis of this code lies in its attempt to model interactions within a neural network constructed as a circular arrangement of neurons. Here’s a breakdown of the biological aspects the code aims to simulate: ### Neuronal Types **1. NEURONAL MODELS (`NRN1`, `NRN2`):** - **RS-cell (Regular Spiking):** These are likely excitatory neurons that exhibit regular spiking patterns. These neurons might correspond to pyramidal cells in the cortex, which are known for their regular firing rates. - **FS-cell (Fast Spiking):** These neurons are likely inhibitory and exhibit fast-spiking behavior typical of interneurons, such as parvalbumin-expressing GABAergic neurons in the cortex. ### Network Configuration **2. NETWORK MODEL (`NTWK`):** - The network model (`CONFIG1`) suggests a predefined configuration of connections within the neural network, potentially specifying parameters like connectivity arity and synaptic strengths. ### Ring Model **3. RING MODEL (`RING`):** - The ring model, possibly referred to as `RING1`, involves neurons arranged in a circular topology, allowing interactions via excitatory and inhibitory connections. This type of model is useful for studying phenomena like pattern formation and wave propagation in the cortex where neurons are functionally organized. ### Stimulation **4. STIMULATION (`STIM`):** - The parameter `CENTER` indicates a localized input stimulation typically applied in the center of the network. This mimics stimulus-induced activation in a specific cortical region and is used to study the spatial and temporal spread of activation. ### Parameters and Dynamics **5. Connectivity (`exc_connect_extent`, `inh_connect_extent`):** - These parameters define the spatial extent of excitatory and inhibitory connections in millimeters, which correlate to how synaptic interactions spread across cortical space. **6. Conduction Velocity (`conduction_velocity_mm_s`):** - The velocity of signal propagation through the network is specified, reflecting axonal conduction velocities observed in biological systems, thereby affecting the timing and pattern of neuronal activation. **7. Synaptic Dynamics (`Tau1`, `Tau2`):** - These time constants influence the temporal dynamics of synaptic currents, with typical values suggesting dynamics of excitatory post-synaptic potentials (EPSP) and inhibitory post-synaptic potentials (IPSP). ### Output and Analysis **8. Activity Variables (`Fe_aff`, `Fe`, `Fi`, `muVn`):** - **Fe_aff:** Represents activities of neurons driven by afferent input. - **Fe and Fi:** Correspond to excitatory and inhibitory neural firing rates, analogous to the balance between excitation and inhibition critical for cortical function. - **muVn:** Depicts deviations in membrane potential reflecting neural activity changes, which can be related to local field potentials. ### Visualization **9. Visualization:** - The code includes sophisticated plotting mechanisms to visualize space-time dynamics of neuronal activity, reflecting how specific stimuli can propagate across the network, akin to optical imaging or voltage-sensitive dye imaging in experimental neuroscience. This simulation creates a dynamic representation of how neurons in a network can interact under various stimuli, capturing essential features of cortical processing. It allows researchers to study how different types of neurons and synaptic configurations contribute to the emergent behavior observed in neural circuits.