The provided code models the neuronal dynamics of a network under the influence of perturbations. Here are the key biological elements associated with the model: ### Neuronal Network Structure - **N & NE**: The model represents a neural network with `N` neurons, of which `NE` are excitatory. This division embodies the basic structure of the brain, where neurons can be excitatory or inhibitory, reflecting their role in synaptic transmission. ### Synaptic Weights & Dynamics - **w**: The variable `w` symbolizes synaptic weights, which determine the strength of the synaptic connections between neurons. In a biological context, synaptic weight adjustments are critical for learning and plasticity. ### Temporal Dynamics - **dt**: This represents the time step of the simulation. Biologically, it reflects the temporal resolution at which neuronal dynamics and synaptic changes occur. - **tau**: Likely represents the time constant of neuronal integration, which in biology is linked to how neurons integrate incoming synaptic input over time. Different neurons have different `tau` values depending on their types and roles. ### Perturbations - **pert_ids, pert_size, N_pert**: These parameters are related to the perturbations applied to specific neurons. Perturbations mimic external stimuli or inputs, allowing exploration of the network's response to changes, akin to sensory input or experimental manipulations in neuroscience. ### Inhibitory Input - **I_inh**: This aspect includes an inhibitory input into the network which is crucial in maintaining stability and processing within neural circuits, resembling the natural role of inhibitory interneurons in the brain. ### Simulation of Neuronal Dynamics - **simulate_dynamics**: This suggests the function uses differential equations to mimic neuronal activities, potentially incorporating various factors like membrane potentials and synaptic inputs, common in computational neuroscience models to simulate realistic neuronal behavior. ### Plasticity - **dw**: This variable encapsulates synaptic plasticity, reflecting how connections between neurons are strengthened or weakened over time due to activity. The calculation of `dw` suggests a mechanism similar to spike-timing-dependent plasticity (STDP), where changes in synaptic strength depend on the timing of spikes between pre- and post-synaptic neurons. ### Perturbation and Baseline Activity - The code attempts to measure changes in neuronal activities in response to perturbations compared to baseline activities (before perturbations), a method that is critical in understanding how neurons and networks adapt to new information or changes in their environment. In essence, this code models a simplified neural network, capturing key biological features such as excitatory and inhibitory neurons, synaptic plasticity, and network response to external stimuli. This forms the basis for understanding complex neural dynamics and adaptive behaviors observed in actual neural circuits.