56 research outputs found
lfads-torch: A modular and extensible implementation of latent factor analysis via dynamical systems
Latent factor analysis via dynamical systems (LFADS) is an RNN-based
variational sequential autoencoder that achieves state-of-the-art performance
in denoising high-dimensional neural activity for downstream applications in
science and engineering. Recently introduced variants and extensions continue
to demonstrate the applicability of the architecture to a wide variety of
problems in neuroscience. Since the development of the original implementation
of LFADS, new technologies have emerged that use dynamic computation graphs,
minimize boilerplate code, compose model configuration files, and simplify
large-scale training. Building on these modern Python libraries, we introduce
lfads-torch -- a new open-source implementation of LFADS that unifies existing
variants and is designed to be easier to understand, configure, and extend.
Documentation, source code, and issue tracking are available at
https://github.com/arsedler9/lfads-torch .Comment: 4 pages, 1 figure, 1 tabl
Targeted Neural Dynamical Modeling
Latent dynamics models have emerged as powerful tools for modeling and
interpreting neural population activity. Recently, there has been a focus on
incorporating simultaneously measured behaviour into these models to further
disentangle sources of neural variability in their latent space. These
approaches, however, are limited in their ability to capture the underlying
neural dynamics (e.g. linear) and in their ability to relate the learned
dynamics back to the observed behaviour (e.g. no time lag). To this end, we
introduce Targeted Neural Dynamical Modeling (TNDM), a nonlinear state-space
model that jointly models the neural activity and external behavioural
variables. TNDM decomposes neural dynamics into behaviourally relevant and
behaviourally irrelevant dynamics; the relevant dynamics are used to
reconstruct the behaviour through a flexible linear decoder and both sets of
dynamics are used to reconstruct the neural activity through a linear decoder
with no time lag. We implement TNDM as a sequential variational autoencoder and
validate it on simulated recordings and recordings taken from the premotor and
motor cortex of a monkey performing a center-out reaching task. We show that
TNDM is able to learn low-dimensional latent dynamics that are highly
predictive of behaviour without sacrificing its fit to the neural data
Representation learning for neural population activity with Neural Data Transformers
Neural population activity is theorized to reflect an underlying dynamical
structure. This structure can be accurately captured using state space models
with explicit dynamics, such as those based on recurrent neural networks
(RNNs). However, using recurrence to explicitly model dynamics necessitates
sequential processing of data, slowing real-time applications such as
brain-computer interfaces. Here we introduce the Neural Data Transformer (NDT),
a non-recurrent alternative. We test the NDT's ability to capture autonomous
dynamical systems by applying it to synthetic datasets with known dynamics and
data from monkey motor cortex during a reaching task well-modeled by RNNs. The
NDT models these datasets as well as state-of-the-art recurrent models.
Further, its non-recurrence enables 3.9ms inference, well within the loop time
of real-time applications and more than 6 times faster than recurrent baselines
on the monkey reaching dataset. These results suggest that an explicit dynamics
model is not necessary to model autonomous neural population dynamics. Code:
https://github.com/snel-repo/neural-data-transformer
Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces
We present a new deep multi-state Dynamic Recurrent Neural Network (DRNN) architecture for Brain Machine Interface (BMI) applications. Our DRNN is used to predict Cartesian representation of a computer cursor movement kinematics from open-loop neural data recorded from the posterior parietal cortex (PPC) of a human subject in a BMI system. We design the algorithm to achieve a reasonable trade-off between performance and robustness, and we constrain memory usage in favor of future hardware implementation. We feed the predictions of the network back to the input to improve prediction performance and robustness. We apply a scheduled sampling approach to the model in order to solve a statistical distribution mismatch between the ground truth and predictions. Additionally, we configure a small DRNN to operate with a short history of input, reducing the required buffering of input data and number of memory accesses. This configuration lowers the expected power consumption in a neural network accelerator. Operating on wavelet-based neural features, we show that the average performance of DRNN surpasses other state-of-the-art methods in the literature on both single- and multi-day data recorded over 43 days. Results show that multi-state DRNN has the potential to model the nonlinear relationships between the neural data and kinematics for robust BMIs
Building population models for large-scale neural recordings: opportunities and pitfalls
Modern recording technologies now enable simultaneous recording from large
numbers of neurons. This has driven the development of new statistical models
for analyzing and interpreting neural population activity. Here we provide a
broad overview of recent developments in this area. We compare and contrast
different approaches, highlight strengths and limitations, and discuss
biological and mechanistic insights that these methods provide
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