# Biological Basis of the Computational Model The provided code appears to model aspects of neural connectivity and activity within a network that involves ET (external tufted) cells, PG (periglomerular) cells, and other unspecified elements, potentially within the olfactory bulb or a related neural structure. Here's a breakdown of the key biological concepts represented: ## Neural Activity and Connectivity - **ET Cells**: ET cells are likely involved in the processing of sensory input within the olfactory bulb. These cells are known for their role in integrating and relaying sensory information. - **PG Cells**: These cells are typically located in the olfactory bulb and are involved in modulating sensory information through lateral inhibition and other mechanisms, which help in sharpening olfactory signals. - **Network Connectivity**: - The code alters the connectivity of ET cells, potentially simulating conditions where these cells are selectively deactivated. This can be used to study the role of ET cells in network dynamics and how their inactivity affects the overall system. ## Model Parameters - **Breathing and Light Periods**: The model includes parameters such as `breathing_period` and `light_period`, which might simulate cyclical sensory inputs in a biological system, such as the regular airflow through the nasal passages during breathing and light exposure, respectively. - **Peak Rates**: The parameters like `breath_peak_rate`, `light1_peak_rate`, and `light2_peak_rate` represent maximum firing rates or stimulation levels, reflecting the intensity of sensory inputs or neural responses. - **Half-Width Parameters**: `breath_half_width` and `light_half_width` might represent the temporal spread of these inputs/activities, suggesting that the model considers the duration or temporal profile of these signals. ## Synaptic Interactions - **Synaptic Weights and Actions**: The code includes manipulations of synaptic weights (e.g., through the `nc` objects), indicating that it is examining the influence of synaptic strength and connectivity on neural network dynamics. The dynamic adjustments of connections between PG cells and other neurons (e.g., M1 tuft and M2) illustrate synaptic plasticity's role in response to varying activation conditions. ## Recordings of Neural Events - **Event Vectors**: The model uses vectors (`pg1_to_m1tuft_events`, `pg1_axon_to_m2_events`, and `pg2_axon_to_m1priden_events`) to record specific neural firing or transmission events. This allows for the analysis of how different elements within the network respond to the set conditions, providing insights into information flow and network states under different scenarios. ## Summary Overall, the code models the interactions and dynamics of a neural network involving ET and PG cells, likely within the context of sensory processing in the olfactory system. This involves simulating different conditions such as varying sensory stimuli and selectively altering neural connections to study their role in network dynamics and function.