Task-driven neural network models predict neural dynamics of proprioception
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We propose a method for adapting neural networks to distribution shifts at test-time. In contrast to training-time robustness mechanisms that attempt to anticipate and counter the shift, we create a closed-loop system and make use of test-time feedback sig ...
Bottom-up models of functionally relevant patterns of neural activity provide an explicit link between neuronal dynamics and computation. A prime example of functional activity patterns are propagating bursts of place-cell activities called hippocampal rep ...
On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an essential layer of safety assurance that could lead to more principled decision making by enabling sound risk assessment and management. The safety and reliability impr ...
Understanding how neural circuits remodel and adapt to animal behavior is a central theme in the field of Neuroscience. One strategy to reach this goal is to repeatedly record the same animal's neural circuits and observe how they adapt when facing differe ...
To characterize a physical system to behave as desired, either its underlying governing rulesmust be known a priori or the system itself be accurately measured. The complexity of fullmeasurements of the system scales with its size. When exposed to real-wor ...
The advent of comprehensive synaptic wiring diagrams of large neural circuits has created the field of connectomics and given rise to a number of open research questions. One such question is whether it is possible to reconstruct the information stored in ...
Understanding behavior from neural activity is a fundamental goal in neuroscience. It has practical applications in building robust brain-machine interfaces, human-computer interaction, and assisting patients with neurological disabilities. Despite the eve ...
Atomic simulations using machine learning interatomic potential (MLIP) have gained a lot of popularity owing to their accuracy in comparison to conventional empirical potentials. However, the transferability of MLIP to systems outside the training set pose ...
We present an approach to bridge the gap between the computational models of human vision and the clinical practice on visual impairments (VI). In a nutshell, we propose to connect advances in neuroscience and machine learning to study the impact of VI on ...
The dynamics of neuron populations during diverse tasks often evolve on low-dimensional manifolds. However, it remains challenging to discern the contributions of geometry and dynamics for encoding relevant behavioural variables. Here, we introduce an unsu ...