Descrizione del progetto
Chiarire la potenza del cervello umano studiando i meccanismi di apprendimento
Le notevoli capacità di apprendimento del cervello umano sono tuttora superiori alla potenza computazionale dell’apprendimento automatico; tuttavia, alcuni aspetti delle intricate operazioni di apprendimento dei molteplici circuiti neurali interconnessi a livello cerebrale sono tuttora inspiegabili. Finanziato dal Consiglio europeo della ricerca, il progetto DopamineLearnLoops aggiornerà il modello convenzionalmente accettato secondo cui un singolo errore nel sistema della dopamina aggiorna l’intera rete, proponendo una nuova teoria fondata su sistemi di apprendimento dinamici multipli basati sulla dopamina che operano in circuiti paralleli. Utilizzando tecniche all’avanguardia, il progetto determinerà i meccanismi sottostanti ai circuiti, il loro funzionamento in un ambiente dinamico e gli algoritmi utilizzati. La comprensione del modo in cui il cervello affronta problemi complessi contribuirà senza dubbio allo sviluppo di nuovi algoritmi di apprendimento profondo ispirati al funzionamento cerebrale.
Obiettivo
The brain’s ability to learn is arguably its most exceptional capacity. Learning in biological brains far surpasses machine learning and requires much less training. How does the brain accomplish this? Why is biological learning still better than the most advanced machine learning algorithms to date? According to the standard model of reward-based learning in the brain, a single error signal is broadcast from the dopamine system and used to update the entire network, implementing a simple form of reinforcement learning. However, the standard model fails to predict several recent experimental findings, leaving open the question of how learning is implemented in the brain. In this project, I propose a new theory of how the brain learns: learning is implemented by multiple dopamine-based learning systems working in parallel circuit loops. These loops relay partial error signals to specific processing areas and permit independent evaluation of the value of different features in the external environment as well as the internal state, enabling learning of complex tasks with multiple relevant features. The loops are engaged dynamically according to the demands of the task, enabling the system to be flexible for learning a wide variety of behaviours of varying complexity. The presence of multiple dynamic parallel learning loops might enable the ability to generalize learning, which is currently the hallmark of biological intelligence. We will use state-of-the art techniques under the framework of our theory to elucidate basic mechanisms underlying the functional circuitry of the learning system (Aim 1), how it operates under different behavioural dynamics (Aim 2), and what algorithm it implements (Aim 3). Success of this project will enable a novel understanding of how the brain learns complex tasks as well as pave the way for the development of new brain-inspired deep reinforcement-learning algorithms.
Campo scientifico
Programma(i)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Argomento(i)
Meccanismo di finanziamento
HORIZON-ERC - HORIZON ERC GrantsIstituzione ospitante
32000 Haifa
Israele