Graph theory is now becoming a standard tool in system-level neuroscience. However,
endowing observed brain anatomy and dynamics with a complex network structure does not …

Intrinsic timescales characterize dynamics of endogenous fluctuations in neural activity.
Variation of intrinsic timescales across the neocortex reflects functional specialization of …

The cost of information processing in physical systems calls for a trade-off between
performance and energetic expenditure. Here we formulate and study a computation …

Recurrent neural networks (RNNs) in the brain and in silico excel at solving tasks with
intricate temporal dependencies. Long timescales required for solving such tasks can arise …

Modern neuroscience has evolved into a frontier field that draws on numerous disciplines,
resulting in the flourishing of novel conceptual frames primarily inspired by physics and …

Recent advancements in measurement techniques have resulted in an increasing amount of
data on neural activities recorded in parallel, revealing largely heterogeneous correlation …

Most external stimuli, including sound, temperature, and illumination, exhibit spatially
heterogeneous, and different amplitudes of the same signal are received by neurons at …

Brain–computer interfaces have seen extraordinary surges in developments in recent years,
and a significant discrepancy now exists between the abundance of available data and the …

Deep learning tools applied to high-resolution neurophysiological data have significantly
progressed, offering enhanced decoding, real-time processing, and readability for practical …

Structural modularity is a pervasive feature of biological neural networks, which have been
linked to several functional and computational advantages. Yet, the use of modular …