# Biological Basis of the Computational Neuroscience Model The provided code is implementing a computational model of a neural network using principles from computational neuroscience. This model seeks to simulate neuronal dynamics with a focus on understanding the behavior of excitatory and inhibitory neurons and the effects of network coupling mediated by gap junctions. The key biological concepts in the code include the following: ## 1. Neuronal Populations ### Excitatory and Inhibitory Neurons - **Excitatory Neurons (NE1):** The code models a population of 800 excitatory neurons. These neurons release neurotransmitters that increase the likelihood of neuronal firing in connected neurons. Excitatory neurons are often associated with neurotransmitters like glutamate. - **Inhibitory Neurons (NI1):** A population of 200 inhibitory neurons is also modeled. These neurons release neurotransmitters that decrease the likelihood of neuronal firing in connected neurons. Inhibitory neurons commonly use neurotransmitters like GABA (gamma-aminobutyric acid). The balance and interaction between excitatory and inhibitory signals are crucial for the stability and functioning of neural networks. ## 2. External Drive and Gap Junctions ### Mean External Drive - **Nu (ν):** The external drive parameter is set to 120, representing the mean input or external synaptic drive to the network. This is an essential factor in maintaining network dynamics and is analogous to ongoing input from sensory systems or background synaptic activity in biological neural networks. ### Gap Junction Coupling (g1) - **Gap Junctions:** The mean coupling strength (`g1`) is varied (glist = [1,5]) to simulate different levels of direct electrical synapses between neurons. Gap junctions allow for the direct transmission of electrical currents between neurons, enabling rapid and synchronous neural communication. ## 3. Plasticity Parameters ### Stability and Plasticity - **Stability/Plasticity Timing (stabTime, stopTime):** By setting both `stabTime` and `stopTime` to infinity, the code models a static network without synaptic plasticity during the simulation. This choice might be used to analyze network dynamics without the added complexity of changing synaptic strengths over time. ## 4. Simulation Outputs ### Spiking and Voltage Monitoring - **Spike Monitoring:** The simulation saves spiking activity, allowing for the analysis of temporal patterns of action potentials, which can be depicted using raster plots. Spiking activity is critical for neural encoding and information transmission in the brain. - **Voltage Monitoring:** Individual neuron voltages and their dynamics are also recorded, which are useful for understanding the subthreshold membrane potential fluctuations and the impact of synaptic inputs. ## Visualization ### Raster Plots and Voltage Traces - **Raster Plots:** These plots visually represent the timing and pattern of spiking activity across excitatory and inhibitory populations, providing insights into synchrony, bursting behavior, and population dynamics. - **Voltage Traces:** These depict the membrane potential dynamics of specific neurons over time. The traces illustrate neuronal responses to inputs and highlight the influence of inhibitory currents, particularly noticeable in the inhibitory neuron traces. This model is a simplified representation of neural circuitry, capturing essential features such as neuronal heterogeneity, synaptic interactions, and network connectivity. Through such simulations, researchers can investigate hypotheses about neural function and dysfunction in a controlled environment.