## Biological Basis of the Code The provided code is simulating a computational model of a rate-based neural network with the following biological basis: ### 1. **Inhibitory Stabilized Networks (ISNs)** The model aims to simulate Inhibitory Stabilized Networks (ISNs). These are networks where inhibitory neurons play a crucial role in maintaining the balance of excitation and inhibition. The model explores how patterned inhibitory perturbations affect network dynamics based on feature similarity. ISNs are known to stabilize network activity and enable complex computations in cortical circuits. ### 2. **Rate-Based Neural Model** This is a rate-based model, meaning it simplistically represents the activity of neurons based on their firing rates rather than precise spike timings. Rate-based models are often used in studies of visual processing because they can efficiently capture the population dynamics of visual neurons. ### 3. **Visual Receptive Fields** The model employs two-dimensional visual receptive fields (RFs), employing Gabor functions to simulate the orientation and spatial frequency selectivity of visual neurons, particularly in the primary visual cortex (V1). Each neuron's RF is described by parameters like sigma (width), gamma (aspect ratio), psi (phase), and spatial frequency (sfs), which are critical for processing visual inputs. ### 4. **Excitatory and Inhibitory Neurons** - **Number of Neurons**: The network consists of 400 excitatory (E) and 400 inhibitory (I) neurons, mimicking the balance between excitation and inhibition in the cortex. - **Connection Weights**: The connectivity within and between neuron types is defined by matrices (`wEE`, `wEI`, `wIE`, `wII`) that determine the influence between excitatory and inhibitory neurons. The weights are set such that inhibitory connections (`JIE`, `JII`) are relatively stronger, reflecting the importance of inhibition in ISNs. ### 5. **Feature Similarity-Based Perturbations** Perturbations are applied to the model network based on feature similarity, suggesting a focus on how visual features are processed and disrupted in cortex-like networks: - **1D Orientation Perturbation**: Perturbations are delivered along a one-dimensional orientation axis, manipulating inhibitory neurons according to their preferred orientation. - **2D RF Perturbation**: Perturbations based on the two-dimensional receptive fields probe how disruptions affect the connectivity pattern inferred from RF correlations. ### 6. **Time Dynamics and Perturbation Intervals** The model operates over a simulated time window with specifics of perturbation onset and duration (`t0`, `tf`, `t_pert`) to study transient dynamics in response to perturbations. This reflects an interest in network state changes over time, an essential aspect of dynamic neural processing. ### 7. **Rectification and Neural Activation** Neurons in the model are subject to rectification, ensuring their firing rates remain non-negative, mimicking the non-linear nature of neuron firing. This constrains neuron outputs to be physiologically plausible, similar to how actual neuron activation is never negative. Overall, the code models the dynamic behavior of a simplified cortical network under various perturbations to understand how feature similarity impacts neural processing within an ISN framework. This can provide insights into the robustness and flexibility of cortical computation, particularly in the visual system.