The code provided is a computational model that simulates certain aspects of neuronal networks, focusing on synaptic dynamics and neuronal activity. Here's a breakdown of the biological basis represented by the code: ### Neuronal Network Structure - **Layer Initialization**: The model initializes a grid-based layer of neurons, where each neuron can be either excitatory or inhibitory. The positioning of excitatory and inhibitory neurons follows a pattern, suggesting a structured neural architecture. ### Neuronal Dynamics - **Excitatory and Inhibitory Neurons**: The code differentiates between excitatory and inhibitory neurons. Excitatory neurons release glutamate, a primary excitatory neurotransmitter, whereas inhibitory neurons presumably influence the network through mechanisms such as GABA release, although this is not explicitly detailed in the provided code. This distinction affects synaptic connections and neuronal dynamics. - **Synaptic Connections**: The function `addSynapse` models the connectivity between neurons based on a probabilistic function linked to the Euclidean distance between neurons. This likely reflects biologically plausible connectivity patterns seen in cortical networks, where neighboring neurons have a higher probability of forming synapses. ### Synaptic Dynamics - **Glutamate Release and Dynamics**: Each excitatory neuron is associated with several parameters for neurotransmitter dynamics, such as `TauRelease`, `TauReplenish`, and `SpontRelease`, which model the release, replenishment, and spontaneous release of glutamate, respectively. This aspect of the code simulates the complex kinetics of synaptic vesicle dynamics and neurotransmitter release. - **Probability Distribution**: The variable `glutamate_pr` represents a normally distributed random variable, suggesting variance in the release probability of glutamate among neurons. This models biological variability in synaptic strength and vesicle release probability. ### Computational Model of Action Potentials - **Neuron Parameters**: The neurons in the model are characterized by various parameters (`Tgk`, `Tth`, `B`, etc.) that likely influence their electrical properties, akin to gating variables in Hodgkin-Huxley type models. These parameters would affect how neurons integrate inputs and spike. - **Membrane Potential**: The function `computePotential` suggests the calculation of membrane potential changes, possibly integrating synaptic inputs and determining firing threshold crossing. The membrane potential dynamics are crucial in determining neuronal firing and network activity. ### Simulation Dynamics - **Iterative Simulation Loop**: The model runs an iterative simulation loop, simulating synaptic activity, glutamate dynamics, and neuronal firing over time. The tracking of excitatory and inhibitory spikes during iterations reflects an effort to quantify network activity. - **Potential Recording**: The code includes a function to record membrane potentials, applying a Gaussian filter centered on the layer. This might model localized field potential measurements, capturing network activity's spatial and temporal dynamics. ### Conclusion Overall, the code aims to simulate a network of neurons with a focus on synaptic interactions, specifically glutamate dynamics and spike generation behavior, capturing the essence of communication within neural circuits. This reflects a simplified yet biologically informed model of neuronal network behavior used to understand neural computations and plasticity.