In this lesson, Artificial Intelligence: Relation with Neuroscience, we are going to learn a brief history of the ideas from neuroscience that assisted the development of AI. So let’s dive deeper into the major ideas and prominent figures associated with it.
Neurosciences and artificial intelligence (AI) are two fascinating fields that have begun to intersect in exciting ways. The application of neurosciences in AI holds great potential for enhancing our understanding of the human brain and improving the capabilities of AI systems. But before delving into the details, let’s ask a very fundamental question to ourselves.
- How do brains process information?
Neuroscience is a scientific discipline dedicated to investigating the nervous system, with a primary focus on understanding the intricate workings of the brain. While the precise mechanisms through which the brain facilitates thoughts and cognitive processes remain one of the greatest enigmas in science, the very existence of thought has been recognized for millennia due to observable evidence. One such evidence is the impact of severe head injuries, which can result in mental impairment or incapacitation. Over the course of history, humans have also observed that the human brain possesses unique attributes that distinguish it from other organs and systems in the body.
These observations have prompted a deep curiosity to unravel the mysteries behind the brain’s functionality and its role in generating complex mental phenomena. In approximately 335 BCE, Aristotle made an observation that man possesses the largest brain relative to his body size compared to other animals. This early recognition hinted at the potential significance of the brain in human cognition and behavior. However, it took several centuries for the scientific community to fully appreciate the role of the brain as the seat of consciousness.
Prior to the middle of the 18th century, there was a lack of consensus on the specific organ responsible for consciousness. Various organs, including the heart and the spleen, were considered as potential candidates for housing consciousness. These beliefs stemmed from ancient philosophical and medical traditions that attributed important functions to different organs in the body. It was not until the middle of the 18th century, with the advancements in anatomical studies and scientific inquiry, that the brain’s central role in consciousness became widely acknowledged. Pioneering work by anatomists and neuroscientists, including Andreas Vesalius and Thomas Willis, helped establish the brain’s significance in cognition, sensory perception, and overall human functioning.
The Idea of Artificial Brain:
The intricate structure of neurons in the human brain has been a significant inspiration for researchers in developing artificial neural networks or in layman’s terms an artificial brain. The unique features and connectivity patterns observed in neurons have motivated scientists to replicate these structures in computational models known as neural networks.
Neurons in the brain are highly interconnected through synapses, forming complex networks capable of processing and transmitting information. The structure of a neuron, with its dendrites receiving input and the axon transmitting output, allows for the flow of electrical and chemical signals. This structural design enables neurons to integrate and process information in a parallel and distributed manner. Researchers recognized the potential of mimicking this neural architecture in computational systems to emulate the brain’s information processing capabilities. Artificial neural networks consist of interconnected nodes, analogous to neurons, called artificial neurons or “nodes.” These nodes receive input, process it through mathematical operations, and transmit output to other connected nodes.
The connectivity patterns in neural networks are often inspired by the synapses observed in the brain. Connections between artificial neurons are assigned weights that determine the strength of the connection and influence the information flow. These weights are adjusted through a learning process, allowing the network to adapt and improve its performance based on the provided data. The layered structure of neural networks, organized into input, hidden, and output layers, draws parallels to the hierarchical organization observed in the brain’s neural circuits. Each layer performs specific computations and progressively extracts higher-level features from the input data. By leveraging the principles of neural architecture and connectivity, researchers have successfully developed neural networks capable of performing various tasks. From image and speech recognition to natural language processing and even autonomous decision-making, neural networks have demonstrated their ability to learn, generalize, and make predictions based on patterns in data. While artificial neural networks are simplified abstractions of the brain’s complexity, they serve as powerful tools for solving complex problems and advancing AI research. The structure and functioning of neurons provided a blueprint for designing computational models that exhibit certain aspects of intelligence and contribute to the development of AI systems that can learn and process information in ways inspired by the human brain.
The subject of Artificial Intelligence is broader than one can imagine. If you’re curious to know how different other fields have assisted the development of AI. Read AI & Economics, AI & Control Theory, or AI & Mathematics. Even for increasing your curiosity, there is an ongoing discussion of developing AI models using more advanced neuroscience methods. Read about neuroAI here.
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