Inductive bias

…distribution itself, clarifying their inductive bias without a population-limit assumption. The second introduces reward-guided remasking for discrete generation without changing model weights. The proposed sampler corrects earlier choices and comes with robustness guarantees under noisy process verification, contrasting its quadratic complexity with potentially exponential error accumulation in common selection heuristics.

…subjects and tasks, along with lack of inductive biases for disentangling shared and dataset-specific dynamical structure. In this work, we hypothesize that similar behavioral tasks admit a corresponding family of

How do animals adapt in a dynamic world? Sometimes, our expectations about the world are drastically violated, such as when we accidentally bite into spoiled food. In these circumstances, we must adjust or face further harm. A classical experimental paradigm to study this behavior is reversal learning, where the values of stimuli are suddenly reversed, for example from positive to negative. Here we investigate how computational models of neural circuits can express the many behavioral strategies empirically observed in reversal learning. We report that simply introducing more neurons in a neu

Research in Neuroscience, as in many scientific disciplines, is undergoing a renaissance based on deep learning. Unique to Neuroscience, deep learning models can be used not only as a tool but interpreted as models of the brain. The central claims of recent deep learning-based models of brain circuits are that they shed light on fundamental functions being optimized or make novel predictions about neural phenomena. We show, through the case-study of grid cells in the entorhinal-hippocampal circuit, that one may get neither. We rigorously examine the claims of deep learning models of grid cells

…explanation, is called their inductive bias, and it is implicitly built into the operation of animals' neural circuits. This relationship between an observed sensory circuit and its inductive bias is a useful explanatory wi

Many regions of the human brain conduct highly specific functions, such as recognizing faces, understanding language, and thinking about other people’s thoughts. Why might this domain specific organization be a good design strategy for brains, and what is the origin of domain specificity in the first place? In this talk, I will present recent work testing whether the segregation of face and object perception in human brains emerges naturally from an optimization for both tasks. We trained artificial neural networks on face and object recognition, and found that networks were able to perform bo