The provided code simulates a very simplified model of neuronal activity in the cortex, focusing on the generation of spike trains and tonic depolarization/depolarization. Here is an overview of the biological basis: ### Objective The code aims to model cortical neuron spiking activity using two exemplar cells, and demonstrate the effect of tonic currents on neurons. It also generates spike trains which are essential for understanding neuronal firing patterns within a cortical region. ### Neuronal Model - **Population**: The model comprises a small population of two thalamocortical (TC) cells. These are often involved in relaying information between the thalamus and cortical areas and can play a role in both sensory processing and the generation of rhythmic activity. - **Equations**: The primary equation describes the change in membrane potential (`dV/dt`) influenced by applied current (`Iapp`) and synaptic currents (`@current`). This reflects the fundamental process in neuron activation, where input currents affect the neuron's voltage, potentially leading to action potentials (or spikes). ### Ion Channels and Mechanisms - **Sodium (iNa), Potassium (iK), and Leak channels**: These three types of channels are modeled using mechanisms from the Ching 2010 study. Sodium and potassium channels are critical for generating action potentials. Sodium channels contribute to depolarization (the rise of the action potential), while potassium channels are involved in repolarization (returning the membrane potential back towards resting level). - **Leak Channels**: Represent the passive current flow across the neuronal membrane, stabilizing the resting potential and affecting the neuron's responsiveness to further stimuli. - **H-channel (`gH`)**: Though used with a small conductance, these channels play roles in rhythmic activity and excitability, influencing neuronal firing patterns by mediating hyperpolarization-activated cation currents. ### Synaptic Inputs - **Uncorrelated Poisson Process**: This is used for modeling synaptic input, reflecting spontaneous and stochastic activation often observed in biological systems. It simulates the random nature of synaptic firing that cells receive in vivo. - **Parameters**: The synaptic transmission is characterized by parameters such as `g_esyn`, `rate`, and `tau_i`, which influence synaptic strength, firing rate, and synaptic time constant. These parameters help replicate real-world synaptic behavior and connectivity. ### Tonic Current - **Step Function Representation**: This function is utilized to demonstrate tonic depolarization or hyperpolarization. In the biological sense, tonic currents represent a constant level of excitation or inhibition that can modulate neuronal excitability, potentially altering the threshold for action potential generation. ### Outputs - **Spike Rasters**: Rasters are visual representations of spikes over time for different neurons, providing insights into patterns such as burst firing or regular spiking. By modeling these components, the simulation captures essential features of cortical neurons, such as their firing dynamics and response to synaptic inputs, enabling studies of complex phenomena like network oscillations and information processing in the brain.