Biology subjects

Vasilaki, E.

Publications and source records attributed to Vasilaki, E..

2 recordsLinked to original sources

bioRxiv2018

Abstract concept learning in a simple neural network inspired by the insect brain

The capacity to learn abstract concepts such as sameness and difference is considered a higher-order cognitive function, typically thought to be dependent on top-down neocortical processing. It is therefore surprising that honey bees apparantly have this capacity. Here we report a model of the structures of the honey bee brain that can learn same-ness and difference, as well as a range of complex and simple associative learning tasks. Our model is constrained by the known connections and properties of the mushroom body, including the protocerebral tract, and provides a good fit to the learning rates and performances of real bees in all tasks, including learning sameness and difference. The model proposes a novel mechanism for learning the abstract concepts of sameness and difference that is compatible with the insect brain, and is not dependent on top-down or executive control processing.

neuroscience↗

bioRxiv2017

Analysis of behaviour in the Active Allothetic Place Avoidance task based on cluster analysis of the rat movement motifs

The Active Allothetic Place Avoidance test (AAPA) is a useful tool to study spatial memory in a dynamic world. In this task a rat, freely moving on a rotating circular arena, has to avoid a sector where shocks are presented. The standard analysis of memory performance in the AAPA task relies on evaluating individual performance measures. Here we present a new method of analysis for the AAPA test that focuses on the movement paths of the animals and utilizes a clustering algorithm to automatically extract the stereotypical types of behaviour as reflected in the recorded paths. We apply the method to experiments that study the effect of silver nanoparticles (AgNPs) on the reference memory and identify six major classes of movement motifs not previously described in AAPA tests. The method allows us to analyse the data with no prior expectations about the motion to be seen in the experiments.

neuroscience↗