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22 papers · ranked by Valyu relevance

01
BbioRxiv15 Nov 2023
A Spatio-temporal Investigation of Dynamics of a Two-dimensional Multi-scale Fitz-Hugh Nagumo Neuronal Network
Alireza Gharahi, Majid Mohajerani
The multi scale architecture by Breakspear and Stam [2] introduces a framework to consider the dynamical processes specific to a nested hierarchy of spatial scales, from neuronal masses to cortical columns and functional brain regions. They hypothesize that the neural dynamics is a function of the structural properties…
87relevance
02
BbioRxiv22 Sep 2023
Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations
Yin-Jui Chang, Yuan-I Chen, Hannah M. Stealey, Yi Zhao + 5 more
Neural mechanisms and underlying directionality of signaling among brain regions depend on neural dynamics spanning multiple spatiotemporal scales of population activity. Despite recent advances in multimodal measurements of brain activity, there is no broadly accepted multiscale dynamical models for the collective…
87relevance
03
PPubMed1 Mar 2024
Neurobiologically realistic neural network enables cross-scale modeling of neural dynamics
Yin-Jui Chang, Yuan-I Chen, Hsin-Chih Yeh, Samantha R. Santacruz
Fundamental principles underlying computation in multi-scale brain networks illustrate how multiple brain areas and their coordinated activity give rise to complex cognitive functions. Whereas brain activity has been studied at the micro- to meso-scale to reveal the connections between the dynamical patterns and the…
86relevance
04
BbioRxiv2 Mar 2021
Multi-scale hierarchical neural network models that bridge from single neurons in the primate primary visual cortex to object recognition behavior
Tiago Marques, Martin Schrimpf, James J. DiCarlo
Object recognition relies on inferior temporal (IT) cortical neural population representations that are themselves computed by a hierarchical network of feedforward and recurrently connected neural population called the ventral visual stream (areas V1, V2, V4 and IT). While recent work has created some reasonably…
86relevance
05
AarXiv10 Sep 2021
Hierarchical Deep Learning of Multiscale Differential Equation Time-Steppers
Yuying Liu, Nathan Kutz, Steven L. Brunton
Nonlinear differential equations rarely admit closed-form solutions, thus requiring numerical time-stepping algorithms to approximate solutions. Further, many systems characterized by multiscale physics exhibit dynamics over a vast range of timescales, making numerical integration computationally expensive due to…
83relevance
06
PPubMed23 Dec 2021
Hierarchical deep learning of multiscale differential equation time-steppers Hierarchical deep learning of multiscale differential equation time-steppers
Yuying Liu, J. Nathan Kutz, Steven L. Brunton
Nonlinear differential equations rarely admit closed-form solutions, thus requiring numerical time-stepping algorithms to approximate solutions. Further, many systems characterized by multiscale physics exhibit dynamics over a vast range of timescales, making numerical integration expensive. In this work, we develop a…
82relevance
07
PPubMed24 May 2025
Neural Encoding and Decoding at Scale
Yizi Zhang, Yanchen Wang, Mehdi Azabou, Alexandre Andre + 5 more
Recent work has demonstrated that large-scale, multi-animal models are powerful tools for characterizing the relationship between neural activity and behavior. Current large-scale approaches, however, focus exclusively on either predicting neural activity from behavior (encoding) or predicting behavior from neural…
81relevance
08
PPubMed23 May 2022
Birdsong classification based on ensemble multi-scale convolutional neural network
Jiang Liu, Yan Zhang, Danjv Lv, Jing Lu + 4 more
'Yue Yin' 'Haifeng Xu'] With the intensification of ecosystem damage, birds have become the symbolic species of the ecosystem. Ornithology with interdisciplinary technical research plays a great significance for protecting birds and evaluating ecosystem quality. Deep learning shows great progress for birdsongs…
80relevance
09
AarXiv20 Apr 2026
OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens
Konstantin F. Willeke, Polina Turishcheva, Alex Gilbert, Goirik Chakrabarty + 17 more
Scaling data and artificial neural networks has transformed AI, driving breakthroughs in language and vision. Whether similar principles apply to modeling brain activity remains unclear. Here we leveraged a dataset of 3.1 million neurons from the visual cortex of 73 mice across 323 sessions, totaling more than 150…
80relevance
10
AarXiv2017
Multi Resolution LSTM For Long Term Prediction In Neural Activity Video
Yilin Song, Jonathan Viventi, Yao Wang
Epileptic seizures are caused by abnormal, overly synchronized, electrical activity in the brain. The abnormal electrical activity manifests as waves, propagating across the brain. Accurate prediction of the propagation velocity and direction of these waves could enable realtime responsive brain stimulation to suppress…
78relevance
11
CchemRxiv16 May 2022
Neural Scaling of Deep Chemical Models
Nathan Frey, Ryan Soklaski, Simon Axelrod, Siddharth Samsi + 3 more
Massive scale, both in terms of data availability and computation, enables significant breakthroughs in key application areas of deep learning such as natural language processing (NLP) and computer vision. There is emerging evidence that scale may be a key ingredient in scientific deep learning, but the importance of…
77relevance
12
PPubMed23 Jul 2024
Towards a “universal translator” for neural dynamics at single-cell, single-spike resolution
Yizi Zhang, Yanchen Wang, Donato Jiménez Benetó, Zixuan Wang + 6 more
Neuroscience research has made immense progress over the last decade, but our understanding of the brain remains fragmented and piecemeal: the dream of probing an arbitrary brain region and automatically reading out the information encoded in its neural activity remains out of reach. In this work, we build towards a…
75relevance
13
PPubMed9 Jul 2020
Adaptive time scales in recurrent neural networks
Silvan C. Quax, Michele D’Asaro, Marcel A. J. van Gerven
Recent experiments have revealed a hierarchy of time scales in the visual cortex, where different stages of the visual system process information at different time scales. Recurrent neural networks are ideal models to gain insight in how information is processed by such a hierarchy of time scales and have become widely…
73relevance
14
AarXiv24 May 2025
Neural Encoding and Decoding at Scale
Yizi Zhang, Yanchen Wang, Mehdi Azabou, Alexandre Andre + 6 more
'Zixuan Wang' 'Hanrui Lyu' 'The International Brain Laboratory' 'Eva Dyer' 'Liam Paninski' 'Cole Hurwitz'] Recent work has demonstrated that large-scale, multi-animal models are powerful tools for characterizing the relationship between neural activity and behavior. Current large-scale approaches, however, focus…
73relevance
15
CchemRxiv22 Apr 2025
Learning Hierarchical Coarse-Grained Models for Complex Chemical Systems
Authors not listed
Multiscale modeling of complex chemical systems—ranging from polymers to biomolecules—requires coarse-grained (CG) techniques to bridge atomic-scale interactions with mesoscopic behavior. Traditional CG methods rely on handcrafted potentials, limiting their transferability across systems. We propose a…
73relevance
16
PPubMed23 Feb 2023
Abstract representations emerge naturally in neural networks trained to perform multiple tasks
W. Jeffrey Johnston, Stefano Fusi
Humans and other animals demonstrate a remarkable ability to generalize knowledge across distinct contexts and objects during natural behavior. We posit that this ability to generalize arises from a specific representational geometry, that we call abstract and that is referred to as disentangled in machine learning.…
72relevance
17
AarXiv2023
Multi Time Scale World Models
Vaisakh Shaj, Saleh Gholam Zadeh, Ozan Demir, Luiz R. Douat + 1 more
'Gerhard Neumann'] Intelligent agents use internal world models to reason and make predictions about different courses of their actions at many scales [22]. Devising learning paradigms and architectures that allow machines to learn world models that operate at multiple levels of temporal abstractions while dealing with…
71relevance
18
CchemRxiv18 Dec 2025
Mechanistically Constrained Multi-Fidelity Scaling Enables Accurate 100 m³ Fermentation Predictions from Only Three Calibration Volumes
Authors not listed
Accurate extrapolation in data-scarce scientific systems remains a central challenge for machine intelligence. In microbial bioprocessing, kinetic parameters change non-monotonically with reactor volume due to interacting hydrodynamic, oxygen-transfer, and mixing effects, rendering classical empirical scaling laws…
68relevance
19
CchemRxiv18 May 2022
Multi-fidelity machine learning models for improved high-throughput screening predictions
David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò
We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property ofinterest. This problem arises in discovery processes…
68relevance
20
BbioRxiv15 May 2025
Data Heterogeneity Limits the Scaling Effect of Pretraining Neural Data Transformers
Linxing Preston Jiang, Shirui Chen, Emmanuel Tanumihardja, Xiaochuang Han + 3 more
A key challenge in analyzing neuroscience datasets is the profound variability they exhibit across sessions, animals, and data modalities–i.e., heterogeneity. Several recent studies have demonstrated performance gains from pretraining neural foundation models on multi-session datasets, seemingly overcoming this…
67relevance
21
BbioRxiv8 Sep 2026
Biologically grounded locality priors close the data gap for vision transformers in neural prediction
Matteo Farina, Pietro Zamberlan, Arno Onken, Ulisse Ferrari
For datasets with thousands of neurons and images, vision transformers have proven successful at predicting neural responses to stimuli. However, they are expected to underperform in low-data regimes, where CNNs and Gaussian processes are considered more effective. We ask whether transformers can be made competitive…
66relevance
22
CchemRxiv28 Nov 2022
BigBind: Learning from Nonstructural Data for Structure-Based Virtual Screening
Michael Brocidiacono, Paul Francoeur, Rishal Aggarwal, Konstantin Popov + 2 more
Recent attempts at utilizing deep learning for structure-based virtual screening have focused on training models to predict binding affinity from protein-ligand complexes with known crystal structures. The PDBbind dataset is the current standard for training such models, but its small size (less than 20K binding…
54relevance

Results for Multi-scale Neural Models

A Spatio-temporal Investigation of Dynamics of a Two-dimensional Multi-scale Fitz-Hugh Nagumo Neuronal Network

Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations

Neurobiologically realistic neural network enables cross-scale modeling of neural dynamics

Multi-scale hierarchical neural network models that bridge from single neurons in the primate primary visual cortex to object recognition behavior

Hierarchical Deep Learning of Multiscale Differential Equation Time-Steppers

Hierarchical deep learning of multiscale differential equation time-steppers Hierarchical deep learning of multiscale differential equation time-steppers

Neural Encoding and Decoding at Scale

Birdsong classification based on ensemble multi-scale convolutional neural network

OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens

Multi Resolution LSTM For Long Term Prediction In Neural Activity Video

Neural Scaling of Deep Chemical Models

Towards a “universal translator” for neural dynamics at single-cell, single-spike resolution

Adaptive time scales in recurrent neural networks

Neural Encoding and Decoding at Scale

Learning Hierarchical Coarse-Grained Models for Complex Chemical Systems

Abstract representations emerge naturally in neural networks trained to perform multiple tasks

Multi Time Scale World Models

Mechanistically Constrained Multi-Fidelity Scaling Enables Accurate 100 m³ Fermentation Predictions from Only Three Calibration Volumes

Multi-fidelity machine learning models for improved high-throughput screening predictions

Data Heterogeneity Limits the Scaling Effect of Pretraining Neural Data Transformers

Biologically grounded locality priors close the data gap for vision transformers in neural prediction

BigBind: Learning from Nonstructural Data for Structure-Based Virtual Screening

A Spatio-temporal Investigation of Dynamics of a Two-dimensional Multi-scale Fitz-Hugh Nagumo Neuronal Network

Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations

Neurobiologically realistic neural network enables cross-scale modeling of neural dynamics

Multi-scale hierarchical neural network models that bridge from single neurons in the primate primary visual cortex to object recognition behavior

Hierarchical Deep Learning of Multiscale Differential Equation Time-Steppers

Hierarchical deep learning of multiscale differential equation time-steppers Hierarchical deep learning of multiscale differential equation time-steppers

Neural Encoding and Decoding at Scale

Birdsong classification based on ensemble multi-scale convolutional neural network

OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens

Multi Resolution LSTM For Long Term Prediction In Neural Activity Video

Neural Scaling of Deep Chemical Models

Towards a “universal translator” for neural dynamics at single-cell, single-spike resolution

Adaptive time scales in recurrent neural networks

Neural Encoding and Decoding at Scale

Learning Hierarchical Coarse-Grained Models for Complex Chemical Systems

Abstract representations emerge naturally in neural networks trained to perform multiple tasks

Multi Time Scale World Models

Mechanistically Constrained Multi-Fidelity Scaling Enables Accurate 100 m³ Fermentation Predictions from Only Three Calibration Volumes

Multi-fidelity machine learning models for improved high-throughput screening predictions

Data Heterogeneity Limits the Scaling Effect of Pretraining Neural Data Transformers

Biologically grounded locality priors close the data gap for vision transformers in neural prediction

BigBind: Learning from Nonstructural Data for Structure-Based Virtual Screening