2. Artificial Neuroplasticity

Have you ever noticed how your brain adapts when you learn something new—rewiring itself to build new connections? This incredible ability, called neuroplasticity, isn’t just a fascinating fact from neuroscience; it’s a powerful tool you can harness for yourself.

Imagine training your mind to become more flexible and adaptable. By understanding and applying the principles of neuroplasticity, you can enhance your own learning and problem-solving skills. Similar systems can be applied in AI .

Definition

Neuroplasticity refers to the brain’s ability to change and adapt its structure and function in response to experience whether learning or injury. It’s a dynamic process that allows the brain to form new connections and strengthen existing ones, making it more resilient and adaptable. It is of three types:

• Experience Independent Plasticity
• Experience Dependent Plasticity
• Experience Dependent Plasticity

Some of the relevant mechanisms of neuroplasticity include neurogenesis and pruning.

Technological Approaches to Artificial Neuroplasticity

Conventional neural networks have fixed weights and architectures. They need retraining for new information. Hence, artificial neuroplasticity is emerging as a potential solution for dynamic adaptation in Artificial Neural Networks (ANNs).

Obstacles in Adoption

Stability Plasticity Dilemma

The key dilemma is stability (preserving the existing knowledge) with plasticity(integrating new information).

Catastrophic forgetting

Learning new information causes the network to forget the old information.

Emerging Technologies based on artificial neuroplasticity

Liquid Neural Networks

Liquid neural networks can change feature parameters in real-time according to a set of differential equations. This allows the network to adapt to new information without retraining.

Neuromodulated Plasticity

Inspired by neuromodulators like dopamine, these systems can adjust learning rates and strategies based on rewards, novelty or uncertainty. They can help overcome stability and plasticity dilemmas. This could also be called artificial neurotransmitters modeling.

Memory Augmented Neural networks

These systems provide separate memory systems to neural systems that provides neuroplasticity by separating computation from memory. Examples include Neural Turing Machines(NTM) and Differential Neural Computers. This approach is useful to development of long term memory and adaptation.

Solution inspired by artificial neuroplasticity

Experience Replay

Drawing inspiration from memory consolidation during sleep, experience replay involves periodically revisiting the previous experiences interleaving with new learning experience. This helps in reinforcement learning by integration of new learning without forgetting the older learning experience.

Elastic weight Consolidation

This technique assigns weights based on importance of their contribution to previously learned tasks. This approach is similar to how the brain consolidates and strengthens some networks while prunes or weakens the others which are used less.

Applications of artificial neuroplasticity

Adaptive Robotics

A robot could dynamically adapt to walking in different terrains and environments based on learning and continue to function in spite of certain parts failures

Lifelong Learning Systems

As the environment conditions change over time , the artificial neuroplasticity based AI can adapt to the new conditions and continue to function without the need for retraining or replacement.