Learning low-dimensional dynamics from whole-brain data improves task
capture
- URL: http://arxiv.org/abs/2305.14369v1
- Date: Thu, 18 May 2023 18:43:13 GMT
- Title: Learning low-dimensional dynamics from whole-brain data improves task
capture
- Authors: Eloy Geenjaar, Donghyun Kim, Riyasat Ohib, Marlena Duda, Amrit
Kashyap, Sergey Plis, Vince Calhoun
- Abstract summary: We introduce a novel approach to learning low-dimensional approximations of neural dynamics by using a sequential variational autoencoder (SVAE)
Our method finds smooth dynamics that can predict cognitive processes with accuracy higher than classical methods.
We evaluate our approach on various task-fMRI datasets, including motor, working memory, and relational processing tasks.
- Score: 2.82277518679026
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The neural dynamics underlying brain activity are critical to understanding
cognitive processes and mental disorders. However, current voxel-based
whole-brain dimensionality reduction techniques fall short of capturing these
dynamics, producing latent timeseries that inadequately relate to behavioral
tasks. To address this issue, we introduce a novel approach to learning
low-dimensional approximations of neural dynamics by using a sequential
variational autoencoder (SVAE) that represents the latent dynamical system via
a neural ordinary differential equation (NODE). Importantly, our method finds
smooth dynamics that can predict cognitive processes with accuracy higher than
classical methods. Our method also shows improved spatial localization to
task-relevant brain regions and identifies well-known structures such as the
motor homunculus from fMRI motor task recordings. We also find that non-linear
projections to the latent space enhance performance for specific tasks,
offering a promising direction for future research. We evaluate our approach on
various task-fMRI datasets, including motor, working memory, and relational
processing tasks, and demonstrate that it outperforms widely used
dimensionality reduction techniques in how well the latent timeseries relates
to behavioral sub-tasks, such as left-hand or right-hand tapping. Additionally,
we replace the NODE with a recurrent neural network (RNN) and compare the two
approaches to understand the importance of explicitly learning a dynamical
system. Lastly, we analyze the robustness of the learned dynamical systems
themselves and find that their fixed points are robust across seeds,
highlighting our method's potential for the analysis of cognitive processes as
dynamical systems.
Related papers
- Enhancing learning in artificial neural networks through cellular heterogeneity and neuromodulatory signaling [52.06722364186432]
We propose a biologically-informed framework for enhancing artificial neural networks (ANNs)
Our proposed dual-framework approach highlights the potential of spiking neural networks (SNNs) for emulating diverse spiking behaviors.
We outline how the proposed approach integrates brain-inspired compartmental models and task-driven SNNs, bioinspiration and complexity.
arXiv Detail & Related papers (2024-07-05T14:11:28Z) - DSAM: A Deep Learning Framework for Analyzing Temporal and Spatial Dynamics in Brain Networks [4.041732967881764]
Most rs-fMRI studies compute a single static functional connectivity matrix across brain regions of interest.
These approaches are at risk of oversimplifying brain dynamics and lack proper consideration of the goal at hand.
We propose a novel interpretable deep learning framework that learns goal-specific functional connectivity matrix directly from time series.
arXiv Detail & Related papers (2024-05-19T23:35:06Z) - Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks [59.38765771221084]
We present a physiologically inspired speech recognition architecture compatible and scalable with deep learning frameworks.
We show end-to-end gradient descent training leads to the emergence of neural oscillations in the central spiking neural network.
Our findings highlight the crucial inhibitory role of feedback mechanisms, such as spike frequency adaptation and recurrent connections, in regulating and synchronising neural activity to improve recognition performance.
arXiv Detail & Related papers (2024-04-22T09:40:07Z) - A Neuromorphic Approach to Obstacle Avoidance in Robot Manipulation [16.696524554516294]
We develop a neuromorphic approach to obstacle avoidance on a camera-equipped manipulator.
Our approach adapts high-level trajectory plans with reactive maneuvers by processing emulated event data in a convolutional SNN.
Our results motivate incorporating SNN learning, utilizing neuromorphic processors, and further exploring the potential of neuromorphic methods.
arXiv Detail & Related papers (2024-04-08T20:42:10Z) - Contrastive-Signal-Dependent Plasticity: Forward-Forward Learning of
Spiking Neural Systems [73.18020682258606]
We develop a neuro-mimetic architecture, composed of spiking neuronal units, where individual layers of neurons operate in parallel.
We propose an event-based generalization of forward-forward learning, which we call contrastive-signal-dependent plasticity (CSDP)
Our experimental results on several pattern datasets demonstrate that the CSDP process works well for training a dynamic recurrent spiking network capable of both classification and reconstruction.
arXiv Detail & Related papers (2023-03-30T02:40:28Z) - Expressive architectures enhance interpretability of dynamics-based
neural population models [2.294014185517203]
We evaluate the performance of sequential autoencoders (SAEs) in recovering latent chaotic attractors from simulated neural datasets.
We found that SAEs with widely-used recurrent neural network (RNN)-based dynamics were unable to infer accurate firing rates at the true latent state dimensionality.
arXiv Detail & Related papers (2022-12-07T16:44:26Z) - Decomposed Linear Dynamical Systems (dLDS) for learning the latent
components of neural dynamics [6.829711787905569]
We propose a new decomposed dynamical system model that represents complex non-stationary and nonlinear dynamics of time series data.
Our model is trained through a dictionary learning procedure, where we leverage recent results in tracking sparse vectors over time.
In both continuous-time and discrete-time instructional examples we demonstrate that our model can well approximate the original system.
arXiv Detail & Related papers (2022-06-07T02:25:38Z) - Neuronal Learning Analysis using Cycle-Consistent Adversarial Networks [4.874780144224057]
We use a variant of deep generative models called - CycleGAN, to learn the unknown mapping between pre- and post-learning neural activities.
We develop an end-to-end pipeline to preprocess, train and evaluate calcium fluorescence signals, and a procedure to interpret the resulting deep learning models.
arXiv Detail & Related papers (2021-11-25T13:24:19Z) - Dynamic Neural Diversification: Path to Computationally Sustainable
Neural Networks [68.8204255655161]
Small neural networks with a constrained number of trainable parameters, can be suitable resource-efficient candidates for many simple tasks.
We explore the diversity of the neurons within the hidden layer during the learning process.
We analyze how the diversity of the neurons affects predictions of the model.
arXiv Detail & Related papers (2021-09-20T15:12:16Z) - Backprop-Free Reinforcement Learning with Active Neural Generative
Coding [84.11376568625353]
We propose a computational framework for learning action-driven generative models without backpropagation of errors (backprop) in dynamic environments.
We develop an intelligent agent that operates even with sparse rewards, drawing inspiration from the cognitive theory of planning as inference.
The robust performance of our agent offers promising evidence that a backprop-free approach for neural inference and learning can drive goal-directed behavior.
arXiv Detail & Related papers (2021-07-10T19:02:27Z) - Recurrent Neural Network Learning of Performance and Intrinsic
Population Dynamics from Sparse Neural Data [77.92736596690297]
We introduce a novel training strategy that allows learning not only the input-output behavior of an RNN but also its internal network dynamics.
We test the proposed method by training an RNN to simultaneously reproduce internal dynamics and output signals of a physiologically-inspired neural model.
Remarkably, we show that the reproduction of the internal dynamics is successful even when the training algorithm relies on the activities of a small subset of neurons.
arXiv Detail & Related papers (2020-05-05T14:16:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.