The code provided models a network of pyramidal neurons connected through gap junctions, also known as electrical synapses. This setup is designed to simulate reentrant oscillations within a loop of connected neurons. Below are the key biological components and concepts represented in the code: ### **Biological Basis of the Model** 1. **Pyramidal Neurons:** - **Cellular Target:** The model uses pyramidal neurons, a type of excitatory neuron predominantly found in cortical areas of the brain, such as the hippocampus and prefrontal cortex. These neurons play a crucial role in cognitive functions like learning and memory due to their synaptic connectivity and plasticity. - **Neuronal Sections:** The axonal segment chosen is `section 21`, a parameter that likely represents a location along the axon important for conduction reliability in the model. 2. **Gap Junctions:** - **Electrical Synapses:** Gap junctions in this model allow direct electrical signaling between neurons via the axonal sections of the cells. This setup mimics real-world synchronization phenomena observed in neural tissue, particularly in fast-spiking networks. - **Conductance Values:** The code sets varying conductances (`gj_conductance` and `weakgj_conductance`), introducing asymmetry to simulate biological conditions where not all synaptic connections are equally strong, potentially initiating reentrant oscillations (a type of feedback loop that can cause repetitive neural circuit activation typical in some oscillatory brain rhythms). 3. **Currents and Conductances:** - **Ion Channels:** The code implements Hodgkin-Huxley (HH) type fast sodium (Na\(^+\)) and delayed rectifier potassium (K\(^+\)) channels, essential for action potential generation and propagation. By isolating these channels, the model examines their primary roles in sustaining rapid signal transmission across the network. - **Current Biasing:** A hyperpolarizing current (`setcurrentbias`) is applied to the soma of neurons to manipulate baseline excitability, an important aspect of neuromodulation affecting firing rates and patterning in biological neurons. 4. **Reentrant Oscillations:** - **Break in Symmetry:** Setting one gap junction conductance weaker than others (`gaps.object(0).setg(weakgj_conductance)`) aims to break the symmetrical conduction loop. This is critical for studying phenomena like neuronal avalanches and pathological conditions such as epilepsy, where abnormal reentrant signaling may occur. 5. **Stimulation:** - **Pulse Injection:** A pulse of current (`Ipulse1`) injected into the first neuron of the loop (cell `object(0)`) acts as an initiating stimulus that can potentially trigger and sustain the simulated oscillatory activity across the network. ### **Conclusion** This computational model provides insights into mechanisms governing neuronal synchronization and oscillation in interconnected networks of pyramidal neurons. By incorporating realistic synaptic and cellular dynamics, it can enhance our understanding of fundamental and pathological neural processes, including cognitive functionality and disorders associated with aberrant synchronization, such as epilepsy.