The provided code models ephaptic interactions in the mammalian olfactory system as described by Bokil et al. (2001). Ephaptic interactions refer to the influence of one neuron's electrical activity on an adjacent neuron through the extracellular space, without direct synaptic connection. This phenomena is significant in the olfactory bulb, which has a dense packing of neurons and plays a crucial role in odor processing. Here are the key biological elements modeled in the code: ### Axonal Structures - **Passive Cables**: The code simulates two passive axons named `a` and `b`. These are modeled as cables, which are simplified representations of the biological axons. Each cable's characteristics, such as diameter, length, and axial resistivity (`Ra`), are specified to simulate the passive electrical properties of neurons. - **Extracellular Space**: The extracellular space between axons is critical in ephaptic interactions. The parameter `beta` is the ratio of the extracellular to axon cross-sectional area, relating to the effective space between the axons for current to flow. ### Electrical Properties - **Passive Conductance**: The code uses a passive conductance model (`insert pas` with parameters `e_pas` and `g_pas`) which relates to the resting potential and membrane conductance of axons. This focuses on simulating the resting, or background, electrical state of the axons. - **Extracellular Mechanism**: By inserting `extracellular` and specifying transversal and axial resistances (`xg` and `xraxial` respectively), the model simulates the resistance of the extracellular medium. These properties impact how electrical fields surround axons and interact with neighboring neurons. ### Inter-axonal Interaction - **Connection Scheme**: The axons are artificially connected through a linear mechanism (`LinearMechanism` object), which models an asymmetric conductance reflecting the presence of multiple axons (`N`) with a subset being stimulated (`Ns`). This asymmetry compensates for the influence of multiple unstimulated axons adjacent to the stimulated one, representing how local electrical fields created by activity in one neuron can influence others nearby. ### Biological Relevance - **Simulating Local Fields**: This model captures the core biophysical processes behind the generation of local field potentials in neural tissue, a key consideration in understanding sensory processing regions like the olfactory bulb. - **Implication for Olfactory Processing**: By simulating these interactions, researchers can explore how non-synaptic interaction influences sensory information processing. Understanding these interactions may provide insights into how sensory signals integrate and modulate within the olfactory system, potentially influencing overall perception. By capturing these fundamental aspects, the code helps elucidate how electrical interactions beyond synaptic connections contribute to neuronal dynamics in the olfactory system.