The code provided is a computational simulation representing neuronal dynamics, specifically modeling the membrane potential dynamics of axonal and somatic compartments of neurons. The primary focus seems to be on understanding how various forms of synaptic inputs and intrinsic membrane properties influence neuronal excitability, particularly in response to inhibitory and excitatory stimuli. Here's the biological basis broken down into key components: ### Neuronal Compartments - **Axon Compartment:** The code models an axonal region with factors such as membrane capacitance (`c_m`), leak conductance (`g_l`), resting membrane potential (`E_l_axon`), and a threshold potential (`V_thresh_axon`). These parameters define the axonal membrane's passive properties, influencing how it generates and propagates action potentials. - **Soma Compartment:** The soma is modeled with similar parameters (`E_l_soma`), and the input from the axon can influence the somatic potential. A high resistance, modeled by `radi_by_length` and `g_comp`, indicates the degree of attenuation of the axonal input. ### Synaptic Inputs - **Inhibitory Input:** Governed by conductance `g_inh` and reversal potential `E_inh`, representing synaptic inputs that hyperpolarize the neuron by allowing Cl⁻ ions to flow into the cell, moving the membrane potential towards an inhibitory reversal potential (in this case, the same as `E_l_axon`). - **Excitatory Input:** Modeled by conductance `g_exc` and reversal potential `E_exc`. These inputs depolarize the membrane toward a more positive value, driving the membrane potential closer to the action potential threshold. The presence of such excitatory conductance is modeled differently across conditions specified in the loop. ### Action Potential Generation - The model evaluates the generation of action potentials (APs) using a predefined waveform (`AP`), which models the spike shape characterized by a rapid depolarization followed by repolarization, defined by different tau values (`tau_in` and `tau_out`). The code conducts spike detection using threshold criteria (`V_thresh_axon`). ### Synaptic Noise - The input current, `I_ext_orig`, which consists of noisy synaptic input with an added random component (`randn` function), simulates synaptic bombardment experienced by neurons in vivo. This captures the variability of neuronal firing in response to currents that mimic synaptic inputs in a realistic brain environment. ### Analysis Metrics - **Spike Detection and Analysis:** The model identifies action potentials based on membrane potential peaks detected using `findpeaks`, and tracks the number and characteristics of spikes, potentially corresponding to different experimental conditions. - **Statistical Analysis:** Post-simulation, data regarding spike rate, amplitude, and other spike characteristics are analyzed and plotted to understand the effects of different synaptic inputs and electrophysiological properties on neuronal firing. ### Biological Context This model could be representative of cortical pyramidal neurons or other types prominently featuring axonal and somatic compartments with distinct roles in action potential initiation and propagation. The inclusion of both inhibitory and excitatory inputs simulates a network model where the balance of excitation and inhibition is crucial for various processes such as synaptic plasticity, information processing, and neurological disorders. Overall, the code models fundamental aspects of neuronal excitability, focusing on action potential properties and how different synaptic conditions can modulate neuronal firing patterns, which are critical to understanding how neurons encode and process information.