Learning interpretable control inputs and dynamics underlying animal locomotion
TS Mullen, M Schimel, G Hennequin… - The Twelfth …, 2024 - openreview.net
A central objective in neuroscience is to understand how the brain orchestrates movement.
Recent advances in automated tracking technologies have made it possible to document …
Recent advances in automated tracking technologies have made it possible to document …
Structure-preserving recurrent neural networks for a class of Birkhoffian systems
S Xiao, M Chen, R Zhang, Y Tang - Journal of Systems Science and …, 2024 - Springer
In this paper, the authors propose a neural network architecture designed specifically for a
class of Birkhoffian systems—The Newtonian system. The proposed model utilizes recurrent …
class of Birkhoffian systems—The Newtonian system. The proposed model utilizes recurrent …
Reducing power requirements for high-accuracy decoding in iBCIs
BM Karpowicz, B Bhaduri… - Journal of Neural …, 2024 - iopscience.iop.org
Objective: Current intracortical brain-computer interfaces (iBCIs) rely predominantly on
threshold crossings (“spikes”) for decoding neural activity into a control signal for an external …
threshold crossings (“spikes”) for decoding neural activity into a control signal for an external …
When predict can also explain: few-shot prediction to select better neural latents
K Dabholkar, O Barak - arXiv preprint arXiv:2405.14425, 2024 - arxiv.org
Latent variable models serve as powerful tools to infer underlying dynamics from observed
neural activity. However, due to the absence of ground truth data, prediction benchmarks are …
neural activity. However, due to the absence of ground truth data, prediction benchmarks are …
Learning low-dimensional dynamics from whole-brain data improves task capture
The neural dynamics underlying brain activity are critical to understanding cognitive
processes and mental disorders. However, current voxel-based whole-brain dimensionality …
processes and mental disorders. However, current voxel-based whole-brain dimensionality …
Universal Differential Equations as a Common Modeling Language for Neuroscience
A ElGazzar, M van Gerven - arXiv preprint arXiv:2403.14510, 2024 - arxiv.org
The unprecedented availability of large-scale datasets in neuroscience has spurred the
exploration of artificial deep neural networks (DNNs) both as empirical tools and as models …
exploration of artificial deep neural networks (DNNs) both as empirical tools and as models …
Diffusion-Based Generation of Neural Activity from Disentangled Latent Codes
Recent advances in recording technology have allowed neuroscientists to monitor activity
from thousands of neurons simultaneously. Latent variable models are increasingly valuable …
from thousands of neurons simultaneously. Latent variable models are increasingly valuable …
Models of neural circuits as optimally driven dynamical systems
M Schimel - 2024 - repository.cam.ac.uk
Animal brains are composed of large numbers of neurons, whose time-varying activity is
shaped by their recurrent connections.% neurons are connected. Recent advances in …
shaped by their recurrent connections.% neurons are connected. Recent advances in …
Deciphering the Geometry of Primary Motor Cortical Manifolds: Observations From Naturalistic Movements and Implications for Intracortical Brain-Computer Interfaces
E Altan - 2023 - search.proquest.com
Each neuron in the primary motor cortex (M1) is like a musician in an orchestra, contributing
to a larger harmony under the constraint of a “neural manifold”—a geometric score …
to a larger harmony under the constraint of a “neural manifold”—a geometric score …
Uncovering the latent dynamics of whole-brain fMRI tasks with a sequential variational autoencoder
The neural dynamics underlying brain activity are critical to understanding cognitive
processes and mental disorders. However, current voxel-based whole-brain dimensionality …
processes and mental disorders. However, current voxel-based whole-brain dimensionality …