# Biological Basis of the Computational Model The provided code is a computational representation of neuronal models, specifically aiming to capture the dynamics of certain types of neurons present in a biological neural network. This is achieved by using the Adaptive Exponential Integrate-and-Fire (AdEx) neuron model, which is known for its ability to mimic a wide variety of neuronal firing patterns through a simple set of parameters. Here's an overview of the biological aspects modeled by the code: ## Neuronal Model - **AdEx Model**: The `neuronModel` is specified as `'adex'` (Adaptive Exponential Integrate-and-Fire model), a biologically inspired approach that captures the spiking behavior of neurons with an adaptive mechanism. This model accounts for the spike generation via exponential terms and includes adaptation, which is crucial for replicating the firing frequency adaptation observed in biological neurons. ## Compartments and Morphology - **Compartmental Structure**: The neurons are modeled with multiple compartments (`numCompartments`), which reflects the dendritic tree structure of real neurons. Each compartment represents either a section of the soma, dendrite, or axon, allowing the model to simulate spatial aspects of neuronal signaling. - **Morphometrics**: Properties like `compartmentLengthArr` and `compartmentDiameterArr` define the size and shape of each compartment, mimicking the morphology of neurons which influences electrical properties and signal propagation. - **Compartment Relationships**: `compartmentParentArr` specifies the hierarchical structure, replicating how dendritic branches stem from the soma or other branches. ## Electrical Properties - **Membrane Properties**: The code includes parameters like `C` (membrane capacitance), `R_M` (membrane resistance), and `R_A` (axial resistance), which are crucial for defining how signals propagate through each neuron. These values directly relate to how biological membranes control ion flow and electrical signal propagation. - **Leak and Threshold Potentials**: `E_leak`, `V_t`, `v_reset`, and `v_cutoff` are intrinsic properties that help to define the resting state and firing threshold, similar to the biological resting potential and action potential thresholds of neurons. ## Dynamics of Firing - **Adaptation**: Parameters such as `a`, `tau_w`, and `b` control the adaptation behavior of neuronal firing. This reflects real biological processes where neurons adjust their firing rates in response to sustained inputs. - **Spike Threshold Dynamics**: Parameters like `delta_t` articulate the dynamics of spike initiation, akin to the sharp threshold at which a biological neuron produces an action potential. ## Synaptic Input - **Input Type**: The code models input currents using `i_ou` (Ornstein-Uhlenbeck process), which generates noise with specific `meanInput` and `stdInput`, emulating synaptic bombardment from other neurons in a stochastic manner. This mirrors the temporal variability of synaptic inputs that neurons receive in vivo. - **Synaptic Dynamics**: `tau` represents synaptic time constants, dictating how quickly inputs change. This is crucial for modeling temporal aspects of synaptic transmission. ## Functional Hierarchy - **Soma and Dendritic Segregation**: Different compartment types (e.g., `somaID`, `apicalID`, `basalID`) indicate the specialization of dendritic branches, which in biology, are involved in distinct computational roles like spatial integration and input segregation. - **Layer Specification**: `somaLayer` indicates the cortical layer in which each neuron resides, reflecting the laminar organization of the cortex where neurons in different layers have differing roles in cortical processing. This model, therefore, encapsulates various essential aspects of neuron structure and function, providing a simplified yet biologically relevant framework for understanding how neuronal dynamics emerge from underlying biophysical properties.