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Learning Circuits: Neuroscience Inspirations and AI Algorithms
- Learning involves biological circuits in the brain and artificial circuits in artificial neural networks (ANN). The balance between innate and learned behaviors is crucial for optimal learning, similar to the bias-variance tradeoff in supervised learning.
- Learning post-birth is necessary due to the limited capacity of the genome in DNA and the dynamic nature of the world, requiring continuous adaptation and learning.
- Neural plasticity allows for changes in neural networks through growth and reorganization, supporting functions like learning, memory, and cognition.
- Long-term potentiation (LTP) and long-term depression (LTD) modulate synaptic strength, while spike-timing-dependent plasticity (STDP) refines synaptic changes based on timing.
- Hebbian theory states 'neurons wire together if they fire together,' serving as a foundational principle for learning and neural connections.
- Elastic Weight Consolidation (EWC) helps in continual learning by penalizing changes to important weights when learning new tasks, preventing catastrophic forgetting.
- Dopaminergic neurons play a key role in reward learning, influencing motivation, reinforcement, and emotion regulation.
- Reinforcement learning (RL) models how agents make decisions to maximize rewards, inspired by the brain's reward systems like dopamine pathways.
- Temporal-difference (TD) learning combines bootstrapping and sampling to update value estimates incrementally, reshaping actions based on reward signals.
- Advanced RL systems like AlphaGo use deep neural networks and planning algorithms to master complex decision-making tasks, showcasing the power of deep RL.
- AlphaZero further refines the RL framework by learning purely from self-play, demonstrating the effectiveness of deep RL in mastering diverse games and decision-making.
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