# Biological Basis of the Computational Model The provided code is part of a computational neuroscience model focusing on synaptic responses in neurons. It aims to simulate and visualize the electrical activity of neurons in response to specific inputs, with the following biological components and principles: ## Neuronal Network ### Neurons - **Three Neurons**: The code models the activity of three neurons, which may have distinct properties. These neurons are likely to be part of a simplified network used to study synaptic transmission and neuronal communication. ### Inputs - **Identical Inputs**: All neurons receive identical inputs, which allows for the observation of how each neuron responds to the same external conditions. This uniformity can be used to study intrinsic properties of neurons, such as firing patterns and membrane potential dynamics. ## Synaptic and Membrane Dynamics ### Membrane Potential - **Membrane Voltage (`vm`)**: The core focus of the model is on the membrane potential of neurons. In biological neurons, the membrane potential is crucial for action potential generation and synapse transmission. The code visualizes the changes in membrane potential over time for each neuron, reflecting their electrophysiological responses. ### Activity Threshold - **Activity Threshold (`sim.activity_thr`)**: The model employs a predefined activity threshold to detect when a neuron is firing. This mirrors the biological concept where action potentials are initiated when the membrane potential exceeds a certain threshold. ### Inter-Spike Interval (ISI) - **Inter-Spike Interval (`m_isi`, `s_isi`)**: Calculation of the mean and standard deviation of ISI offers insights into the firing regularity of neurons. In biological systems, ISI dynamics can indicate how neurons encode information and regulate synaptic transmission. ## Visualization ### Axes and Scale - **Voltage and Current Units**: The plots use standardized units (e.g., millivolt for voltage and microampere per square centimeter for current density). This standardization simulates real biological scales, providing a relatable framework for interpreting neuronal activity. ### Spike Raster Plots - **Spike Plotting**: The code uses spike raster plots to represent the times at which neurons fire. These plots are a common method in neuroscience to visualize neuron firing patterns and can reveal timing relationships and synchronized activity across the neurons modeled. ## External Modulation and Offsets - **Offset and External Inputs**: External inputs (e.g., synaptic currents) can modulate neuronal firing patterns. The `off` parameter possibly represents a temporal offset used to focus on specific segments of the neuronal response, simulating delayed synaptic input or the effect of external stimuli commencing at different times. In summary, this code attempts to model the fundamental dynamics of neuronal membrane potentials and their response to synaptic inputs. It simulates key electrophysiological processes of neurons, providing insights into their intrinsic firing properties and potential roles in neural computation.