The provided code snippet from "Make_figs2.py" is designed to produce figures that represent the computational analysis of place cells, silent cells, and inhibitory cells in the context of a neural network model. The biological basis of this computational code revolves around several key neural components and phenomena in the hippocampus, which has significant roles in spatial navigation and memory formation. ### Biological Basis 1. **Place Cells**: - Place cells are neurons in the hippocampus that become active when an animal is in a specific location in its environment. They contribute to the animal's cognitive map of space. The code focuses on extracting firing rates for both the soma and dendrites of such cells, a distinction that likely illustrates differential input integration and processing along the cell. 2. **Silent Cells**: - Silent cells are neurons that exhibit low baseline firing rates and may become active under certain conditions or inputs. The code seems to account for them by tracking both soma and dendritic activity, possibly to infer under what conditions these cells participate in network dynamics. 3. **Inhibitory Cells**: - The model also incorporates inhibitory neurons that target either the soma or dendrites of excitatory neurons. These are critical for regulating the timing and synchronization of network activity. The code may simulate the effects of these inhibitory cells on the overall network activity by modulating synaptic weights. 4. **Synaptic Integration**: - Dendrites and somas process inputs differently. Dendritic processing can involve nonlinear integration and local synaptic modifications, affecting the overall output of the neuron. By separately analyzing the firing rates of dendrites and somas, the model can provide insights into how spatially mapped inputs are integrated within hippocampal circuits. 5. **Synaptic Weights and Plasticity**: - The code tracks synaptic weights between presynaptic inputs and dendrites (`Wpre_to_dend`). Changes in these weights are indicative of synaptic plasticity, a cellular mechanism for learning and memory, including long-term potentiation (LTP) and long-term depression (LTD). 6. **Extracellular Currents**: - The inclusion of `ExtraCurr` indicates the influence of external modulatory inputs on neuronal activity, possibly simulating neuromodulatory effects that impact memory encoding and retrieval processes. ### Output and Visualization The figures generated by this code, such as mean activity versus laps, are likely modeling how neural firing patterns and synaptic changes evolve over repeated exposures to the environment (n_laps). This models how the hippocampus and the network of neurons within adapt over time as an organism navigates and learns about its surroundings. Overall, this modeling code captures the dynamic interplay of excitatory and inhibitory neurons within the hippocampus, exploring the underpinnings of spatial navigation, learning, and memory formation at a cellular and network level.