Decoding neurons – Bridging mind and machines in the era of artificial intelligence

Neuroscience and Artificial Intelligence (AI) are both rapidly advancing fields that have significant potential for synergy. Neuroscientists aim to decipher the brain and its activities, and how each neuron provides commands to different parts of our bodies. It plays a vital role in the development of deep learning algorithms inspired by the mechanism that the brain uses to process information through an interconnected network of neurons. Hence, AI researchers are now focusing on building machines that can act like a human brain.

Brain neurons and AI models are both involved in processing information, but there are some key differences between the two, here are a few comparisons:

Structure Consists of complex dendrites, axons and synapses which enable communication with other neurons. Structured network which consists of layers of artificial neurons.
Learning Capable of synaptic plasticity which improves the strength of connections with multiple neurons. Capable of training based on data and adjusting the weights of their connection to improve performance.
Processing Power Can perform complex tasks with improved efficiency due to the presence of billions of neurons. Less efficient compared to the human brain since the data can be incomplete or incorrect.
Flexibility Existing neural circuits can perform a wide range of tasks and are adaptable. Cannot adapt to new and unexpected scenarios since the rules are defined to perform specific tasks.
Energy consumption Energy-efficient and powerful. Vast energy consumption and less effective.

Mimic the neurons – AI POV

AI has several types of ANNs (Artificial Neural Networks) which can perform specific tasks. ANN’s performance is highly dependent on the quality and quantity of data used to train it, as well as the architecture and parameters of the network itself. These are computational methods that are replicated using schematics and actions performed by the brain. The study of a brain’s neural networks provides AI researchers with a platform to develop ANNs that can use data as input and based on patterns, provide output. And they are used for image and speech recognition, large language models, and the developing metaverse.

  1. Feedforward neural network (FNN): FNNs are a type of ANN that is commonly used in Machine Learning (ML). In this network, data flows in one direction, data enters the network, it is processed, and a final output is produced. Use cases are speech recognition for sentiment analytics and financial forecasting.
  2. Recurrent neural network (RNN): RNNs are a type of ANN that is designed to process sequential data. Unlike an FNN, which processes input data in a single pass from input to output, RNNs process one element at a time. Use cases are image captioning in insurance and video analysis in fraud.
  3. Convolutional neural networks (CNN): CNNs are a type of deep learning algorithm that is commonly used for image classification and computer vision tasks. They are designed to automatically learn and extract features from the input images, without requiring manual feature engineering. Use cases are object detection in insurance, facial recognition for KYC & onboarding and self-driving cars.

Implementation of neuroscience in AI

Understanding how the brain and the neural network work plays a vital role in the implementation of neuroscience in AI. For example, the decision-making activity in the human brain takes place in the frontal cortex of the brain. The RNN model mimics decision-making in AI.

Study the neural basis of AI models: Studying the human brain including its structure, functions, and also the neural network can be useful in AI models. The neural basis of the models provides insights into the action and reaction, response time, flow etc., and can provide information to improve the model.

Use neuroimaging data to improve AI: Neuroimaging techniques, such as Functional MRI (FMRI), can yield insights into how the brain processes information. There is a stimulus in the human brain which is an event that initiates a reaction in an organ or a tissue. Understanding such stimuli further simplifies how the brain processes information.

AI models and neural architecture: To enable accuracy in AI models, creating AI architecture which mimics neural architecture plays a very important role. This includes models that use spiking neurons, which are more like biological neurons than the traditional artificial neurons used in most AI models.

Incorporate feedback mechanisms: Feedback mechanisms are an important aspect of the brain’s processing, and by incorporating them into AI models, you can improve their performance and make them robust.

AI use cases that are influenced by neuroscience

Brain-Computer Interfaces (BCIs): BCIs use ML algorithms to interpret brain signals and translate them into commands that can control external devices such as prosthetic limbs or computer systems. These systems can be used to restore mobility to individuals with paralysis or other disabilities.

Natural Language Processing (NLP): NLP is the ability of computers to understand and interpret human language. NLP researchers have looked to the brain for inspiration, modelling the processes that the brain uses to process language.

Vision-based AI: The human visual system is incredibly complex, with billions of neurons dedicated to processing visual information. AI researchers have been inspired by this system and have developed computer vision algorithms that simulate the processes of the human visual system.

Cognitive computing: Cognitive computing is a type of AI that is designed to mimic the way that humans think and reason. This includes the ability to recognise patterns, understand language, and make decisions based on incomplete or uncertain information.

In conclusion, the intersection of neuroscience and AI has the potential to lead to many exciting developments and breakthroughs in both fields. By working together, researchers can leverage insights from neuroscience to improve AI and use AI to better understand the brain.

However, it is important to approach the intersection with caution and address any potential ethical concerns that may arise.

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