# Biological Basis of the Code The provided code is a computational neuroscience model that investigates the stability of predictive coding models for synthetic tasks of varying sizes. In computational neuroscience, predictive coding is a theoretical framework that describes how the brain processes information. It posits that the brain constantly generates predictions about incoming sensory input and updates these predictions based on the errors between the predicted and actual inputs. ## Key Concepts 1. **Predictive Coding:** - Predictive coding suggests that the brain minimizes prediction error through recursive signals across hierarchical levels of processing. - In the model, neural units are implemented as “prediction neurons” which attempt to predict incoming stimuli. - The code tests different scales of tasks and examines neural responses, particularly oscillations, which may impact stability. 2. **Rao and Ballard's Algorithm:** - The model specifically tests stability using an algorithm from Rao and Ballard, which is well-known in predictive coding frameworks for minimizing prediction errors through a bidirectional exchange of signals between hierarchical layers. - The model explores different values of the parameter `zeta`, which likely regulates learning rate or sensitivity of neurons to error signals, reflecting the adaptability of the neural network in prediction error minimization. 3. **Stability and Oscillations:** - The code measures oscillations within the neural response, a critical phenomenon in brain dynamics that may indicate instability. Excessive neural oscillations can be detrimental, mimicking pathological brain states such as epilepsy, whereas moderate oscillations are crucial for functions like attention and synchronization. - The results capture whether under certain conditions (e.g., task scale, parameter values) the network becomes unstable, marked by oscillations, or achieves stability, marked by minimized prediction error. 4. **Synaptic Weights (W):** - In the model, weights define the strength of connections between neurons, simulating synaptic efficacy which can be modified through experiences as per Hebbian learning rules in biological networks. 5. **Neural Representation:** - The neural response `y` represents the output of neurons, akin to spikes or firing rates that encode information in biological neurons. - The storage of response differences and ratios allows for understanding how one neuron’s prediction compares to collective network activity, reflecting hierarchical processing within the brain. In summary, this code models the mechanistic underpinnings of predictive coding in the brain, particularly focusing on how hierarchical neural networks process errors in predictions and how they achieve stability across varying task complexities. By modeling operations like prediction generation and error correction, it aims to reflect the dynamic adaptation of biological neural systems.