utilizing-reconfigurable-maze-system-to-enhance-reproducibility

Utilizing a Reconfigurable Maze System to Enhance the Reproducibility of Spatial Navigation Tests in Rodents

Several maze shapes are used to test spatial navigation performance and behavioral phenotypes. Traditionally, each experiment requires a unique maze ...

Doshisha University

Multiple mazes are routinely used to test the performance of animals because each has disadvantages inherent to its shape. However, the maze shape cannot be flexibly and rapidly reproduced in a repeatable and scalable way in a single environment. Here, to overcome the lack of flexibility, scalability, reproducibility, and repeatability, we develop a reconfigurable maze system that consists of interlocking runways and an array of accompanying parts. It allows experimenters to rapidly and flexibly configure a variety of maze structures along the grid pattern in a repeatable and scalable manner. Spatial navigational behavior and hippocampal place coding were not impaired by the interlocking mechanism. As a proof-of-principle demonstration, we demonstrate that the maze morphing induces location remapping of the spatial receptive field. The reconfigurable maze thus provides flexibility, scalability, repeatability, and reproducibility, therefore facilitating consistent investigation into the neuronal substrates for learning and memory and allowing screening for behavioral phenotypes.

The Reconfigurable Maze Provides Flexible, Scalable, Reproducible, and Repeatable Tests.

A comparative analysis of animal behavior (e.g., male vs. female groups) has been widely used to elucidate behavior specific to one group since pre-Darwinian times. However, big data generated by new sensing technologies, e.g., GPS, makes it difficult for them to contrast group differences manually. This study introduces DeepHL, a deep learning-assisted platform for the comparative analysis of animal movement data, i.e., trajectories. This software uses a deep neural network based on an attention mechanism to automatically detect segments in trajectories that are characteristic of one group. It then highlights these segments in visualized trajectories, enabling biologists to focus on these segments, and helps them reveal the underlying meaning of the highlighted segments to facilitate formulating new hypotheses. We tested the platform on a variety of trajectories of worms, insects, mice, bears, and seabirds across a scale from millimeters to hundreds of kilometers, revealing new movement features of these animals.

Deep learning-assisted comparative analysis of animal trajectories with DeepHL.

Since the variables inherent to various diseases cannot be controlled directly in humans, behavioral dysfunctions have been examined in model organisms, leading to better understanding their underlying mechanisms. However, because the spatial and temporal scales of animal locomotion vary widely among species, conventional statistical analyses cannot be used to discover knowledge from the locomotion data. We propose a procedure to automatically discover locomotion features shared among animal species by means of domain-adversarial deep neural networks. Our neural network is equipped with a function which explains the meaning of segments of locomotion where the cross-species features are hidden by incorporating an attention mechanism into the neural network, regarded as a black box. It enables us to formulate a human-interpretable rule about the cross-species locomotion feature and validate it using statistical tests. We demonstrate the versatility of this procedure by identifying locomotion features shared across different species with dopamine deficiency, namely humans, mice, and worms, despite their evolutionary differences.

Cross-species behavior analysis with attention-based domain-adversarial deep neural networks.

Animals exhibit remarkable navigation abilities as if they have an internal compass. Head direction (HD) cells encoding the animal's heading azimuth are found in the brain of several animal species; the HD cell signals are dependent on the vestibular nuclei, where magnetic responsive cells are present in birds. However, it is difficult to determine whether HD cell signals drive the compass orientation in animals, as they do not necessarily rely on the magnetic compass under all circumstances. Recording of HD cell activities from the medial pallium of shearwater chicks () just before their first migration, during which they strongly rely on compass orientation, revealed that shearwater HD cells prefer a north orientation. The preference remained stable regardless of geolocations and environmental cues, suggesting the existence of a magnetic compass regulated by internally generated HD signals. Our findings provide insight into the integration of the direction and magnetoreception senses.

Head direction cells in a migratory bird prefer north.

Simultaneous monitoring of animal behavior and neuronal activity in the brain enables us to examine the neural underpinnings of behaviors. Conventionally, the neural activity data are buffered, amplified, multiplexed, and then converted from analog to digital in the head-stage amplifier, following which they are transferred to a storage server via a cable. Such tethered recording systems, intended for indoor use, hamper the free movement of animals in three-dimensional (3D) space as well as in large spaces or underwater, making it difficult to target wild animals active under natural conditions; it also presents challenges in realizing its applications to humans, such as the Brain-Machine Interfaces (BMI). Recent advances in micromachine technology have established a wireless logging device called a neurologger, which directly stores neural activity on ultra-compact memory media. The advent of the neurologger has triggered the examination of the neural correlates of 3D flight, underwater swimming of wild animals, and translocation experiments in the wild. Examples of the use of neurologgers will provide an insight into understanding the neural underpinnings of behaviors in the natural environment and contribute to the practical application of BMI. Here we outline the monitoring of the neural underpinnings of flying and swimming behaviors using neurologgers. We then focus on neuroethological findings and end by discussing their future perspectives.

Article	Year
The Reconfigurable Maze Provides Flexible, Scalable, Reproducible, and Repeatable Tests. iScience\| PubMed ID: 31918045	2020
Deep learning-assisted comparative analysis of animal trajectories with DeepHL. Nature communications\| PubMed ID: 33082335	2020
Cross-species behavior analysis with attention-based domain-adversarial deep neural networks. Nature communications\| PubMed ID: 34535659	2021
Head direction cells in a migratory bird prefer north. Science advances\| PubMed ID: 35119935	2022
A Review of Neurologgers for Extracellular Recording of Neuronal Activity in the Brain of Freely Behaving Wild Animals. Micromachines\| PubMed ID: 36144152	2022

Kaoru Ide

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