How critical is brain criticality?
Criticality is the singular state of complex systems poised at the brink of a phase transition
between order and randomness. Such systems display remarkable information-processing …
between order and randomness. Such systems display remarkable information-processing …
Statistical mechanics of deep learning
The recent striking success of deep neural networks in machine learning raises profound
questions about the theoretical principles underlying their success. For example, what can …
questions about the theoretical principles underlying their success. For example, what can …
Cortical layer–specific critical dynamics triggering perception
INTRODUCTION Perceptual experiences in mammals may arise from patterns of neural
circuit activity in cerebral cortex. For example, primary visual cortex (V1) is causally capable …
circuit activity in cerebral cortex. For example, primary visual cortex (V1) is causally capable …
Scaling limits of wide neural networks with weight sharing: Gaussian process behavior, gradient independence, and neural tangent kernel derivation
G Yang - arXiv preprint arXiv:1902.04760, 2019 - arxiv.org
Several recent trends in machine learning theory and practice, from the design of state-of-
the-art Gaussian Process to the convergence analysis of deep neural nets (DNNs) under …
the-art Gaussian Process to the convergence analysis of deep neural nets (DNNs) under …
Linking connectivity, dynamics, and computations in low-rank recurrent neural networks
F Mastrogiuseppe, S Ostojic - Neuron, 2018 - cell.com
Large-scale neural recordings have established that the transformation of sensory stimuli
into motor outputs relies on low-dimensional dynamics at the population level, while …
into motor outputs relies on low-dimensional dynamics at the population level, while …
How to start training: The effect of initialization and architecture
We identify and study two common failure modes for early training in deep ReLU nets. For
each, we give a rigorous proof of when it occurs and how to avoid it, for fully connected …
each, we give a rigorous proof of when it occurs and how to avoid it, for fully connected …
Recurrent neural networks as versatile tools of neuroscience research
O Barak - Current opinion in neurobiology, 2017 - Elsevier
Highlights•Recurrent neural networks (RNNs) are powerful models of neural systems.•RNNs
can be either designed or trained to perform a task.•In both cases, low dimensional …
can be either designed or trained to perform a task.•In both cases, low dimensional …
Shared cortex-cerebellum dynamics in the execution and learning of a motor task
Throughout mammalian neocortex, layer 5 pyramidal (L5) cells project via the pons to a vast
number of cerebellar granule cells (GrCs), forming a fundamental pathway. Yet, it is …
number of cerebellar granule cells (GrCs), forming a fundamental pathway. Yet, it is …
Circuit models of low-dimensional shared variability in cortical networks
Trial-to-trial variability is a reflection of the circuitry and cellular physiology that make up a
neuronal network. A pervasive yet puzzling feature of cortical circuits is that despite their …
neuronal network. A pervasive yet puzzling feature of cortical circuits is that despite their …
The dynamical regime of sensory cortex: stable dynamics around a single stimulus-tuned attractor account for patterns of noise variability
Correlated variability in cortical activity is ubiquitously quenched following stimulus onset, in
a stimulus-dependent manner. These modulations have been attributed to circuit dynamics …
a stimulus-dependent manner. These modulations have been attributed to circuit dynamics …