### Biological Basis of the Code The provided code is simulating and visualizing the synaptic response of a small neuronal network consisting of three neurons receiving identical input signals. Below are the key biological elements that the code is modeling: #### Neuronal Activity - **Membrane Potential (Vm):** Each neuron has a membrane potential that is tracked throughout the simulation (`vm`), which is crucial for understanding the excitability of neurons. The plot displays the changes in membrane potential over time for each neuron. - **Action Potentials and Spike Activity:** The code identifies spikes or action potentials when the membrane potential exceeds a certain threshold (`sim.activity_thr`). The generation of action potentials is indicative of neural activity and communication between neurons. #### Synaptic Input - **Input Current:** The synaptic input is represented as an injected current (`nn_inputs`). This mimics excitatory or inhibitory postsynaptic potentials in biological neurons. Such inputs drive the membrane potential changes and can potentially trigger spikes. #### Inter-Spike Interval (ISI) - The inter-spike interval (ISI) is calculated (`calc_isi`) and displayed, providing information on the timing between consecutive spikes. The ISI is a critical parameter for understanding neural coding and rhythmic activity in neural networks. #### Visualization Characteristics - **Plots and Labels:** The visualization uses colors (blue, red, green) to differentiate between the neurons and indicates biological values such as voltage (mV) and current density (µA/cm²) using plotted lines and annotations. - **Time Scale:** A timeline is plotted to represent the temporal dynamics of the simulation in milliseconds, an essential unit for studying neuronal dynamics. ### Biological Context This simulation captures the fundamental dynamics of action potentials, synaptic input, and inter-spike intervals, which are key aspects of neuronal communication and network function. The model's goal is to understand how neurons respond to synaptic inputs and how they encode information via spiking activity. The simulation provides insights into the treatment of identical inputs by different neurons and how their synaptic responses can vary, potentially due to various intrinsic properties or connectivity. This type of modeling is pivotal for understanding brain function, neural coding, and the mechanisms underlying various neural computations.