Our ability to use deep learning approaches to decipher neural activity would likely benefit
from greater scale, in terms of both the model size and the datasets. However, the …

Modelling the spatiotemporal dynamics in the activity of neural populations while also
enabling their flexible inference is hindered by the complexity and noisiness of neural …

As datasets in neuroscience increase in size and complexity, interpreting these high-
dimensional data is becoming more critical. However, develo** an intuition for patterns or …

In the field of behavior-related brain computation, it is necessary to align raw neural signals
against the drastic domain shift among them. A foundational framework within neuroscience …

Loss of speech after paralysis is devastating, but circumventing motor-pathway injury by
directly decoding speech from intact cortical activity has the potential to restore natural …

Cell type is hypothesized to be a key determinant of a neuron's role within a circuit. Here, we
examine whether a neuron's transcriptomic type influences the timing of its activity. We …

Complex time-varying systems are often studied by abstracting away from the dynamics of
individual components to build a model of the population-level dynamics from the start …

People with paralysis express unmet needs for peer support, leisure activities and sporting
activities. Many within the general population rely on social media and massively multiplayer …

Understanding the neural implementation of complex human behaviors is one of the major
goals in neuroscience. To this end, it is crucial to find a true representation of the neural …

In recent years, deep learning approaches have gained significant attention in predicting
brain disorders using neuroimaging data. However, conventional methods often rely on …