AIMC Topic: Avoidance Learning

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Advancing Cell Therapy to Enhance Passive Avoidance Memory: Integrative Approaches Combining Neural-Like Cells and Rosmarinic Acid Through Behavioral, Molecular, and Histological Analyses.

Neurochemical research
Alzheimer's disease (AD) is characterized by progressive neurodegeneration, synaptic dysfunction, and cognitive decline. Regenerative strategies aim to replace lost neurons and modulate the inflammatory milieu to restore neural networks. This study e...

An integrative assay for measuring social aversion and motivation in freely behaving mice.

Cell reports methods
Social aversion is a key feature of numerous mental health disorders, yet we lack adequate behavioral tools to interrogate social aversion in model systems. Here, we developed a behavioral task-selective access to unrestricted social interaction (SAU...

The impact of partner interaction on brief social buffering in adolescent female rats as analyzed by deep learning-based object detection algorithms.

Physiology & behavior
Social buffering is a phenomenon whereby the stress response of anyone exposed to a distressing stimulus is alleviated by the presence of conspecific(s). In this study, we aimed to determine whether brief buffering (only 3 min) with conspecific immed...

Of rats and robots: A mutual learning paradigm.

Journal of the experimental analysis of behavior
Robots are increasingly used alongside Skinner boxes to train animals in operant conditioning tasks. Similarly, animals are being employed in artificial intelligence research to train various algorithms. However, both types of experiments rely on uni...

Modeling crash avoidance behaviors in vehicle-pedestrian near-miss scenarios: Curvilinear time-to-collision and Mamba-driven deep reinforcement learning.

Accident; analysis and prevention
Interactions between vehicle-pedestrian at intersections often lead to safety-critical situations. This study aims to model the crash avoidance behaviors of vehicles during interactions with pedestrians in near-miss scenarios, contributing to the dev...

Firing feature-driven neural circuits with scalable memristive neurons for robotic obstacle avoidance.

Nature communications
Neural circuits with specific structures and diverse neuronal firing features are the foundation for supporting intelligent tasks in biology and are regarded as the driver for catalyzing next-generation artificial intelligence. Emulating neural circu...

Brain-Controlled 2D Navigation Robot Based on a Spatial Gradient Controller and Predictive Environmental Coordinator.

IEEE journal of biomedical and health informatics
OBJECTIVE: Brain-computer interfaces (BCIs) have been used in two-dimensional (2D) navigation robotic devices, such as brain-controlled wheelchairs and brain-controlled vehicles. However, contemporary BCI systems are driven by binary selective contro...

Investigating Pain-Related Avoidance Behavior using a Robotic Arm-Reaching Paradigm.

Journal of visualized experiments : JoVE
Avoidance behavior is a key contributor to the transition from acute pain to chronic pain disability. Yet, there has been a lack of ecologically valid paradigms to experimentally investigate pain-related avoidance. To fill this gap, we developed a pa...

Zebrafish exhibit associative learning for an aversive robotic stimulus.

Lab animal
Zebrafish have quickly emerged as a species of choice in preclinical research, holding promise to advance the field of behavioral pharmacology through high-throughput experiments. Besides biological and heuristic considerations, zebrafish also consti...

Evolved Transistor Array Robot Controllers.

Evolutionary computation
For the first time, a field programmable transistor array (FPTA) was used to evolve robot control circuits directly in analog hardware. Controllers were successfully incrementally evolved for a physical robot engaged in a series of visually guided be...