summary: A new study combined deep learning with neural activity data from mice to unravel the mystery of how mice move through their environments.
By analyzing the firing patterns of “head direction” neurons and “grid cells,” researchers can now accurately predict the position and orientation of a mouse, allowing researchers to unravel the complex brain functions involved in navigation. Ta. The method, developed in collaboration with the U.S. Army Research Laboratory, represents a major advance in understanding spatial cognition and could revolutionize autonomous navigation in AI systems.
The findings highlight the potential for integrating biological insights with artificial intelligence to enhance machine navigation without relying on GPS technology.
Important facts:
- Decoding navigation with deep learning: Researchers used deep learning models to decipher neural activity in mice and accurately predict the mouse’s location and orientation based solely on the firing patterns of “head direction” neurons and “grid cells.”
- Joint research with the US Army Research Institute: This research was conducted in collaboration with the U.S. Army Research Laboratory to integrate biological insights and machine learning to improve autonomous navigation in non-GPS intelligent systems.
- The potential of AI systems: This discovery may inform the design of AI systems that can autonomously navigate in unknown environments, leveraging the neural mechanisms underlying spatial perception and navigation found in biological systems.
sauce: cell press
Researchers combined deep learning models with experimental data to “decipher” neural activity in mice.
Using this method, researchers could now determine exactly where a mouse is in an open environment and in what direction it is facing, just by looking at its neural firing patterns.
Deciphering neural activity can provide insight into the function and behavior of individual neurons and even entire brain regions.
These findings were published on February 22nd. biophysical journalit could also inform the design of intelligent machines that currently struggle to move autonomously.
In collaboration with researchers at the U.S. Army Research Laboratory, lead author Vasilios Marlas’ team used deep learning models to investigate two types of neurons involved in navigation. “Head direction” neurons. It encodes information about which direction the animal is facing. , and “grid cells” that encode his two-dimensional information about the animal’s position within the spatial environment.
“Current intelligence systems have proven to be great at pattern recognition, but when it comes to navigation, these so-called intelligence systems don’t work very well without GPS coordinates or something else to guide the process. ” says Mathematician Marlas. At the University of Tennessee, Knoxville.
“I think the next step for artificial intelligence systems is to integrate biological information with existing machine learning methods.”
Unlike previous studies that sought to understand the behavior of grid cells, the team based their approach on experimental data rather than simulation data.
The data collected as part of the previous study included neural firing patterns collected via internal probes and “ground truth” video footage about the actual position, head position, and movements of mice exploring open spaces. It was composed of a combination of environment.
Analysis involves integration of activity patterns across head directions and groups of grid cells.
“To understand and represent these neural structures, we need mathematical models that describe higher-order connectivity. That is, we want to understand how one neuron activates another. “We want to understand how groups and teams of neurons work,” he says. Marulus.
Using the new method, the researchers were able to predict mouse position and head direction with higher accuracy than previously described methods. Next, they plan to incorporate information from other types of neurons involved in navigation to analyze more complex patterns.
Ultimately, the researchers hope their method will help design intelligent machines that can navigate unfamiliar environments without using GPS or satellite information. “The ultimate goal is to use this information to develop machine learning architectures that can successfully navigate unknown terrain autonomously without GPS or satellite guidance,” Marlas says.
About this neuroscience research news
author: Christopher Behnke
sauce: cell press
contact: Christopher Behnke – Cell Press
image: Image credited to Neuroscience News
Original research: Open access.
“Topological deep learning framework for neural spike decodingWritten by Vasileios Maroulas et al. biophysical journal
abstract
Topological deep learning framework for neural spike decoding
The brain’s spatial perception system uses different ensembles of neurons to assist in environmentally based navigation. Two of the ways the brain encodes spatial information are through head direction cells and grid cells. While the brain uses head direction cells to determine direction, grid cells are made up of layers of deck-like neurons that are overlaid to provide environment-based navigation.
These neurons fire in ensembles, with multiple neurons firing simultaneously to activate a single head direction or grid. We hope to capture this firing structure and use it to decipher head direction and animal position from head direction and grid cell activity.
Understanding, representing, and deciphering these neural structures requires models that encompass higher-order connectivity that goes beyond the one-dimensional connectivity provided by traditional graph-based models.
To that end, in this study, we develop a topological deep learning framework for neural spike train decoding. Our framework combines the power of unsupervised simple-complex discovery and deep learning through a novel architecture we develop here called a simple convolutional recurrent neural network.
Simple complexes, topological spaces that use not only vertices and edges but also higher-dimensional objects, naturally generalize graphs and capture more than just pairwise relationships.
Furthermore, this approach does not require prior knowledge of neural activity beyond the number of spikes, thus eliminating the need for similarity measurements.
The effectiveness and versatility of simple convolutional neural networks is demonstrated in the prediction of head orientation and trajectory via a dataset of head orientation and grid cells.