## Biological Basis of the Model Code This script is developed for computational neuroscience modeling and analysis of different neuronal cell types. It focuses primarily on the electrophysiological properties of neurons, which are central to understanding neuronal excitability and synaptic integration. The main aspects of the biological modeling illustrated by this code are: ### Neuronal Types The code considers multiple types of neurons within the brain: - **Pyramidal Cells**: These are excitatory neurons commonly found in the cerebral cortex. They play a crucial role in cognitive function, including learning and memory. - **Axoaxonic, Basket, Bistratified, OLM, VIP_CCK, VIP_CR Cells**: These typically refer to various types of inhibitory interneurons within the brain. They are responsible for regulating excitatory neural circuit activity, contributing to functions such as rhythm generation and network synchronization. Each type denotes a specific interneuron subtype with unique anatomical and functional properties. ### Electrophysiological Properties The key electrophysiological properties modeled include: - **Sag Ratio**: This measures the hyperpolarization-activated cation current (I_h) in neurons and represents how the neuron's membrane potential "sags" in response to hyperpolarizing current. Sag ratio helps in identifying h-current presence, which is relevant to neuron modulation and rhythmic activity. - **Input Resistance (R_input)**: This fundamental property describes the resistance of a neuron to incoming current, impacting how the neuron integrates synaptic inputs. - **Resting Membrane Potential (V_rest)**: It is the baseline potential difference across the neuronal membrane when the neuron is inactive, crucial for understanding the excitability threshold. - **Membrane Time Constant (t_mem)**: This reflects how quickly a neuron can respond to stimuli, which is important for the temporal integration of synaptic inputs. - **Spike Amplitude and Threshold (spike_ampl, Vthreshold)**: These describe the neuron's action potential characteristics, indicating the excitability and ability to propagate signals along the axon. ### Neuronal Response to Stimuli Neurons are examined in response to injected currents (`I`) ranging from hyperpolarizing to depolarizing inputs, allowing assessment of: - **Voltage Traces**: These visual representations allow us to see how neuron voltage changes over time with different current injections. - **Current-Voltage (VI) Relationship**: This captures how changes in membrane potential correlate with injected current, revealing the cell's passive and active ion channel properties. - **Firing Rate (FI) Curves**: These illustrate how firing frequency of action potentials changes with varying depolarizing currents, important for understanding the neuron’s output signal and frequency coding capabilities. ### Method of Data Analysis - **Peak Detection**: Implemented using `peakdet`, the algorithm identifies peaks in voltage traces to quantify action potential frequency and other dynamic properties of spike trains. - **Visualization and Comparison**: The use of plots allows for visual comparison of electrophysiological properties across different neuron types and conditions, aiding in understanding functional differences. These analyses help in understanding fundamental neurophysiological principles that underlie synaptic transmission and integration in neural circuits, giving insights into both normal and pathological brain functions. Overall, this modeling approach forms a basis for simulating and understanding neuronal dynamics that underpin brain activity and cognitive processes.