Summary
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.
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| Web resources: | https://cordis.europa.eu/project/id/101117737 |
| Start date: | 01-10-2023 |
| End date: | 30-09-2028 |
| Total budget - Public funding: | 1 499 375,00 Euro - 1 499 375,00 Euro |
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Original description
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.Status
SIGNEDCall topic
ERC-2023-STGUpdate Date
12-03-2024
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