Compressed sensing, sparsity, and dimensionality in neuronal information processing and data analysis
S Ganguli, H Sompolinsky - Annual review of neuroscience, 2012 - annualreviews.org
The curse of dimensionality poses severe challenges to both technical and conceptual
progress in neuroscience. In particular, it plagues our ability to acquire, process, and model …
progress in neuroscience. In particular, it plagues our ability to acquire, process, and model …
Statistical mechanics of complex neural systems and high dimensional data
Recent experimental advances in neuroscience have opened new vistas into the immense
complexity of neuronal networks. This proliferation of data challenges us on two parallel …
complexity of neuronal networks. This proliferation of data challenges us on two parallel …
[HTML][HTML] Single session cross-frequency bifocal tACS modulates visual motion network activity in young healthy population and stroke patients
Background Phase synchronization over long distances underlies inter-areal
communication and importantly, modulates the flow of information processing to adjust to …
communication and importantly, modulates the flow of information processing to adjust to …
AI of brain and cognitive sciences: from the perspective of first principles
L Chen, Z Chen, L Jiang, X Liu, L Xu, B Zhang… - arXiv preprint arXiv …, 2023 - arxiv.org
Nowadays, we have witnessed the great success of AI in various applications, including
image classification, game playing, protein structure analysis, language translation, and …
image classification, game playing, protein structure analysis, language translation, and …
Understanding the computational difficulty of a binary-weight perceptron and the advantage of input sparseness
Z Bi, C Zhou - Journal of Physics A: Mathematical and …, 2019 - iopscience.iop.org
Limited precision of synaptic weights is a key aspect of both biological and hardware
implementation of neural networks. To assign low-precise weights during learning is a non …
implementation of neural networks. To assign low-precise weights during learning is a non …
Expectation propagation on the diluted Bayesian classifier
A Braunstein, T Gueudré, A Pagnani, M Pieropan - Physical Review E, 2021 - APS
Efficient feature selection from high-dimensional datasets is a very important challenge in
many data-driven fields of science and engineering. We introduce a statistical mechanics …
many data-driven fields of science and engineering. We introduce a statistical mechanics …
Sparse Hopfield network reconstruction with ℓ 1 regularization
H Huang - The European Physical Journal B, 2013 - Springer
We propose an efficient strategy to infer sparse Hopfield network based on magnetizations
and pairwise correlations measured through Glauber samplings. This strategy incorporates …
and pairwise correlations measured through Glauber samplings. This strategy incorporates …
Single Session Cross-Frequency Bifocal Tacs Modulates Visual Motion Network Activity in Young Healthy and Stroke Patients
Background: Phase synchronization over long distances underlies inter-areal
communication and importantly, modulates the flow of information processing to adjust to …
communication and importantly, modulates the flow of information processing to adjust to …
Stability of the replica symmetric solution in diluted perceptron learning
A Lage-Castellanos, A Pagnani… - Journal of Statistical …, 2013 - iopscience.iop.org
We study the role played by dilution in the average behavior of a perceptron model with
continuous coupling with the replica method. We analyze the stability of the replica …
continuous coupling with the replica method. We analyze the stability of the replica …
Effective Bayesian inference for sparse factor analysis models
KJ Sharp - 2011 - search.proquest.com
We study how to perform effective Bayesian inference in high-dimensional sparse Factor
Analysis models with a zero-norm, sparsity-inducing prior on the model parameters. Such …
Analysis models with a zero-norm, sparsity-inducing prior on the model parameters. Such …