# name
Investigating the neural computations underlying the learning of a delay response task

# metadata.dc.description[].value
Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2020-2021. Director: Manuel Molano-Mazón, Co-Director: Albert Compte Braquets,
Tutor: Roser Sala Llonch.

# metadata.dc.description.abstract[].value
The behaviour of experimental animals reflects their physical and cognitive state. Animal models
are a fundamental tool and resource to study such states. When analysing behavioural studies,
different learning patterns can be distinguished: a gradual improvement or a sudden understanding.
The former is a progressive method used for developing a new behaviour by dividing it into several
stages. In addition to gradual improvement, learning also occurs by abrupt understanding, also
known as aha moment, which is defined as a moment of abrupt insight or discovery.
Lately, recent development of deep neural networks has had a remarkable impact on animal
research. One strategy that has emerged as a promising tool for investigating the behaviour of
animals performing a task is to study recurrent neural networks (RNNs) whose connection weights
have been optimized to perform the same tasks as trained animals.
In this work we have created simulated networks that emulate the learning processes in animals.
Specifically, we have trained Long Short-Term Memory (LSTM) networks, which are a special type
of RNN, with a shaping protocol on a Delayed Response (DR) task, that is a typical approach for
studying mice behaviour. For this purpose, we have used Reinforcement learning (RL), which
concerns goal-oriented algorithms.
In order to analyse both mice and RNNs behaviour patterns, we have focused on the aha moment
and compared their behaviours. We have complemented the study with an exploration of the effect
of shaping in RNNs training.

# metadata.dc.rights[].value
cc-by-nc-nd (c) Azcárate Bescós, Leyre, 2021

# metadata.dc.source[].value
Treballs Finals de Grau (TFG) - Enginyeria Biomèdica

# metadata.dc.title[].value
Investigating the neural computations underlying the learning of a delay response task