For learning the latent (i.e., hidden) structure in the visual system of mice, CEBRA can predict unseen movie frames directly from brain signals alone after an initial training period mapping brain signals and movie features. Credit: Neuroscience News
Machine-Learning Algorithm Predicts What Mice See From Brain Data
Summary: EPFL researchers have developed a novel machine-learning algorithm called CEBRA, which can predict what mice see based on decoding their neural activity.
The algorithm maps brain activity to specific frames and can predict unseen movie frames directly from brain signals alone after an initial training period.
CEBRA can also be used to predict movements of the arm in primates and to reconstruct the positions of rats as they move around an arena, suggesting potential clinical applications.
Researchers from École polytechnique fédérale de Lausanne (EPFL) have developed a machine-learning algorithm called CEBRA that can learn the hidden structure in neural code to reconstruct what a mouse sees when it watches a movie or the movements of the arm in primates.
CEBRA is based on contrastive learning, a technique that enables researchers to consider neural data and behavioral labels like reward, measured movements, or sensory features such as colors or textures of images.
CEBRA’s strengths include its ability to combine data across modalities, limit nuances, and reconstruct synthetic data. The algorithm has exciting potential applications in animal behavior, gene-expression data, and neuroscience research.
Is it possible to reconstruct what someone sees based on brain signals alone? The answer is no, not yet. But EPFL researchers have made a step in that direction by introducing a new algorithm for building artificial neural network models that capture brain dynamics with an impressive degree of accuracy.
Rooted in mathematics, the novel machine learning algorithm is called CEBRA (pronounced zebra), and learns the hidden structure in the neural code.
What information the CEBRA learns from the raw neural data can be tested after training by decoding – a method that is used for brain-machine-interfaces (BMIs) – and they’ve shown they can decode from the model what a mouse sees while it watches a movie.
But CEBRA is not limited to visual cortex neurons, or even brain data. Their study also shows it can be used to predict the movements of the arm in primates, and to reconstruct the positions of rats as they freely run around an arena.
The study is published in Nature.
“This work is just one step towards the theoretically-backed algorithms that are needed in neurotechnology to enable high-performance BMIs,” says Mackenzie Mathis, EPFL’s Bertarelli Chair of Integrative Neuroscience and PI of the study.
For learning the latent (i.e., hidden) structure in the visual system of mice, CEBRA can predict unseen movie frames directly from brain signals alone after an initial training period mapping brain signals and movie features.
The data used for the video decoding was open-access through the Allen Institute in Seattle, WA. The brain signals are obtained either directly by measuring brain activity via electrode probes inserted into the visual cortex area of the mouse’s brain, or using optical probes which consist of using genetically modified mice, engineered so that activated neurons glow green.
During the training period, CEBRA learns to map the brain activity to specific frames. CEBRA performs well with less than 1% of neurons in the visual cortex, considering that, in mice, this brain area consists of roughly 0.5 million neurons.
“Concretely, CEBRA is based on contrastive learning, a technique that learns how high-dimensional data can be arranged, or embedded, in a lower-dimensional space called a latent space, so that similar data points are close together and more-different data points are further apart,” explains Mathis.
“This embedding can be used to infer hidden relationships and structure in the data. It enables researchers to jointly consider neural data and behavioral labels, including measured movements, abstract labels like “reward,” or sensory features such as colors or textures of images.”
“CEBRA excels compared to other algorithms at reconstructing synthetic data, which is critical to compare algorithms,” says Steffen Schneider, the co-first author of the paper. “Its strengths also lie in its ability to combine data across modalities, such as movie features and brain data, and it helps limit nuances, such as changes to the data that depend on how they were collected.”
“The goal of CEBRA is to uncover structure in complex systems. And, given the brain is the most complex structure in our universe, it’s the ultimate test space for CEBRA. It can also give us insight into how the brain processes information and could be a platform for discovering new principles in neuroscience by combining data across animals, and even species.” says Mathis.
“This algorithm is not limited to neuroscience research, as it can be applied to many datasets involving time or joint information, including animal behavior and gene-expression data. Thus, the potential clinical applications are exciting.”
About this machine learning research news
Author: Press Office Source: EPFL Contact: Press Office – EPFL Image: The image is credited to Neuroscience News
Learnable latent embeddings for joint behavioural and neural analysis
Mapping behavioural actions to neural activity is a fundamental goal of neuroscience. As our ability to record large neural and behavioural data increases, there is growing interest in modelling neural dynamics during adaptive behaviours to probe neural representations.
In particular, although neural latent embeddings can reveal underlying correlates of behaviour, we lack nonlinear techniques that can explicitly and flexibly leverage joint behaviour and neural data to uncover neural dynamics.
Here, we fill this gap with a new encoding method, CEBRA, that jointly uses behavioural and neural data in a (supervised) hypothesis- or (self-supervised) discovery-driven manner to produce both consistent and high-performance latent spaces. We show that consistency can be used as a metric for uncovering meaningful differences, and the inferred latents can be used for decoding.
We validate its accuracy and demonstrate our tool’s utility for both calcium and electrophysiology datasets, across sensory and motor tasks and in simple or complex behaviours across species. It allows leverage of single- and multi-session datasets for hypothesis testing or can be used label free.
Lastly, we show that CEBRA can be used for the mapping of space, uncovering complex kinematic features, for the production of consistent latent spaces across two-photon and Neuropixels data, and can provide rapid, high-accuracy decoding of natural videos from visual cortex.