The provided computational model simulates a network of neurons, specifically focusing on their interactions mediated by synapses and their intrinsic properties. This aligns with a core concept in computational neuroscience, modeling how networks of neurons, both excitatory and inhibitory, interact to produce emergent behaviors, such as oscillations. The model aims to replicate the dynamics observed in real neural tissues, emphasizing the interplay between excitatory and inhibitory neurons through specific parameters that reflect biologically plausible mechanisms. ### Biological Basis 1. **Neuron Types:** - **Excitatory Neurons:** These neurons increase the likelihood of firing in the neurons they connect to, represented by the excitatory threshold parameter (`vtE`). They form the majority of the network, with 80% of the neurons modeled as excitatory. - **Inhibitory Neurons:** These neurons decrease neuronal firing probability, modeled by a different threshold parameter (`vtI`). They account for 20% of the network. 2. **Neural Dynamics:** - **Membrane Capacitance (`tau_vE`, `tau_vI`):** Represents how quickly a neuron's potential can change, akin to a neuron's response speed to inputs. Excitatory and inhibitory neurons have different membrane dynamics, reflecting their distinct physiological roles. - **Adaptation Mechanisms:** Only inhibitory neurons have an adaptation mechanism (`beta_adI`, `alpha_adI`), introducing a dynamic component to the neuron's response, likely reflecting phenomena like spike-frequency adaptation observed in real neurons. 3. **Synaptic Interactions:** - **Synaptic Inputs (`GammaEE`, `GammaEI`, `GammaIE`, `GammaII`):** These parameters define the connection strengths between neuron types (Excitatory-Inhibitory and vice versa). They simulate how neural signals are relayed and transformed across the network. - **Gap Junctions (`gamma_c`):** These model electrical synapses that allow direct current flow between neurons, providing instantaneous signal transmission, which can influence synchronization within the network. The code models two conditions reflecting asynchronous and oscillatory behavior by varying the gap-junction parameter. 4. **Noise and Resonance:** - **Current Variance (`TsigE`, `TsigI`):** Represents background synaptic input variability, essential for mimicking the stochastic nature of real synaptic inputs, influencing the network's susceptibility to producing oscillations. - **Subthreshold Resonance:** By adjusting parameters like `gamma_c`, the model explores how even small fluctuations in subthreshold activity can lead to an emergent oscillatory state, a phenomenon closely tied to rhythmic brain activities. 5. **Simulation Objectives:** - The code simulates how networks can spontaneously transition between asynchronous and oscillatory states. This reflects a critical property of biological neural networks, where the collective excitation and inhibition interplay results in complex dynamics, including oscillations observed in brain regions during various cognitive and behavioral states. In summary, this model is focused on capturing essential biological features of neural networks, involving interactions between excitatory and inhibitory neurons, their adaptation behaviors, and synaptic mechanisms, with a broader goal to understand oscillatory mechanisms in neural systems.