AIMC Topic: Avoidance Learning

Clear Filters Showing 11 to 20 of 23 articles

On the boundary conditions of avoidance memory reconsolidation: An attractor network perspective.

Neural networks : the official journal of the International Neural Network Society
The reconsolidation and extinction of aversive memories and their boundary conditions have been extensively studied. Knowing their network mechanisms may lead to the development of better strategies for the treatment of fear and anxiety-related disor...

Identifying the presence and timing of discrete mood states prior to therapy.

Behaviour research and therapy
The present study tested a novel, person-specific method for identifying discrete mood profiles from time-series data, and examined the degree to which these profiles could be predicted by lagged mood and anxiety variables and time-based variables, i...

Avoidance of non-localizable obstacles in echolocating bats: A robotic model.

PLoS computational biology
Most objects and vegetation making up the habitats of echolocating bats return a multitude of overlapping echoes. Recent evidence suggests that the limited temporal and spatial resolution of bio-sonar prevents bats from separately perceiving the obje...

Design and development of a robotic predator as a stimulus in conditioned place aversion for the study of the effect of ethanol and citalopram in zebrafish.

Behavioural brain research
Zebrafish are becoming a species of choice in psychopharmacology, laying a promising path to refined pharmacological manipulations and high-throughput behavioral phenotyping. The field of robotics has the potential to accelerate progress along this p...

A biohybrid fly-robot interface system that performs active collision avoidance.

Bioinspiration & biomimetics
We have designed a bio-hybrid fly-robot interface (FRI) to study sensorimotor control in insects. The FRI consists of a miniaturized recording platform mounted on a two-wheeled robot and is controlled by the neuronal spiking activity of an identified...

Self-supervised learning of the biologically-inspired obstacle avoidance of hexapod walking robot.

Bioinspiration & biomimetics
In this paper, we propose an integrated biologically inspired visual collision avoidance approach that is deployed on a real hexapod walking robot. The proposed approach is based on the Lobula giant movement detector (LGMD), a neural network for loom...

Deep(er) Learning.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Animals successfully thrive in noisy environments with finite resources. The necessity to function with resource constraints has led evolution to design animal brains (and bodies) to be optimal in their use of computational power while being adaptabl...

Learning by stimulation avoidance: A principle to control spiking neural networks dynamics.

PloS one
Learning based on networks of real neurons, and learning based on biologically inspired models of neural networks, have yet to find general learning rules leading to widespread applications. In this paper, we argue for the existence of a principle al...