Contribution of task-irrelevant stimuli to drift of neural representations
Farhad Pashakhanloo
Despite being observed in biological and artificial neural networks, mechanisms of representational drift are still under investigation. Under one set of hypotheses, drift is a result of noisy continual learning, which over long times leads to changes in the representations while the performance is fixed. However, the exact contributions of various sources of noise remain elusive. Of particular interest is the data-dependency of the drift, and its relation to the task within a context.Here, using theory and simulations we show that the presence of task-irrelevant stimuli, which the agent learns to ignore in a context, could be sufficient to create long-term drift in the representation of task-relevant input. By studying different architectures and learning rules, we demonstrate this phenomenon in both Hebbian-based learning (Oja and Similarity Matching), as well as stochastic gradient descent (autoencoder and two-layer networks). The results show that in all cases the drift rate increases as a function of the variance and the dimension of the data in the task-irrelevant subspace. We further demonstrate this phenomenon in a real dataset, and show that the noise induced by task-irrelevant data yields different qualitative predictions for drift than those arising from Gaussian synaptic noise. Overall, our study links the structure of stimuli, task and learning to the phenomenon of representational drift and could pave the way for using drift as a signal for uncovering underlying computation in the brain.