Introduction
There is a lot of excitement about machine learning (ML). After all, it has enabled computers to outperform people in picture classification, defeat humans in complicated games like "Go" and give high-quality voice-to-text on popular mobile phones. The scientific community is taking notice of advances in machine learning.
The discipline of neurology is no different. Many opinion pieces have been written in recent years regarding the relevance of machine learning in neuroscience. Furthermore, when we look at the number of new findings concerning ML in neuroscience over the previous 20 years, we see that its application has been steadily increasing. Within this discipline, machine learning has been employed in a variety of ways.
We will catalog the uses of ML in neuroscience as well as the shared goal for ML in neuroscience in this blog.
Objectives of Machine Learning
Humans can observe and experience the environment because of the eyes. Humans make judgments depending on what they observe with their eyes. Humans act on their ideas depending on what they perceive with their eyes. Similarly, in order for a Machine learning model to emulate human intellect, it must have eyes that can capture pictures or have data as input and make decisions based on them.
Hence, the main objective of machine learning is to uncover patterns in user data and then generate predictions based on these complicated patterns to answer real-world queries and solve issues.
Objectives of Neuroscience
Neuroscience is the study of how the brain performs diverse perceptual, cognitive, and motor processes.
There are many objectives of Neuroscience, few of them are
- To use the scientific method to construct feasible independent research projects by integrating content, skills, and critical thinking.
- To gain a wide awareness of the nervous system's structure and function, as well as the depth of knowledge in cellular/molecular or behavioral/cognitive perspectives.
- To get a better grasp of the ethical problems surrounding the use of human subjects and animal subjects in neuroscience research.
And since the advent of new technologies neuroscience is exploring its new horizons. Big data can now be processed by intelligent computers using ML-based artificial intelligence approaches, opening up new possibilities for neuroscience such as how millions of neurons and nodes cooperate to manage vast amounts of information and how the brain develops and governs actions.
From Neuroscience to ML
The advantages of thoroughly analyzing biological intelligence for constructing machine learning models are twofold.
- First, neuroscience is a rich source of inspiration for new sorts of algorithms and architectures that are independent of and complementary to the mathematical and logic-based methodologies and concepts that have dominated traditional approaches to Machine Learning. For example, if a novel aspect of biological computing is shown to be crucial to supporting a cognitive function, we would consider it a good candidate for inclusion in machine learning systems.
- Second, neuroscience can give validation for existing machine learning algorithms. If a known algorithm is later discovered to be implemented in the brain, it provides strong support for its viability as an important component of a larger general intelligence system.
Human intellect is distinguished by an extraordinary capacity to keep and alter data in an active storage, this is referred to as working memory, and it is assumed to be instantiated inside the prefrontal cortex and related regions. According to traditional cognitive theories, this functioning is dependent on interactions between a central controller ("executive") and distinct, domain-specific memory buffers (e.g. visuo-spatial sketchpad). These models have inspired machine learning research, which has resulted in systems that explicitly preserve information across time. Historically, such attempts began with the introduction of recurrent neural network topologies with attractor dynamics and rich sequential activity, work that was inspired directly by neuroscience. This study paved the way for later, more thorough models of human behavior.
In difficult object identification tests, machine learning systems now match and exceed professional humans. Machines can make synthetic natural visuals and human speech simulations that are nearly indistinguishable from their real-world counterparts, translate across several languages, and create "neural art" in the style of well-known painters.
Much effort has to be done to close the IQ gap between machines and humans. Concepts from neuroscience will become more important in narrowing this gap. The introduction of new technologies for brain imaging and genetic bioengineering in neuroscience has begun to give a precise characterization of the computations occurring in neural networks, suggesting a revolution in our knowledge of mammalian brain function. The importance of neuroscience in the following main areas, both as a roadmap for the ML research agenda and as a supply of computational tools, cannot be overstated.
From ML to Neuroscience
Psychologists and neuroscientists frequently have only hazy ideas about the mechanisms behind the issues they examine. Machine learning is aiding them by formalizing these notions in a quantitative language and providing insights into their requirement and adequacy (or lack thereof) for intelligent action.
RL is an excellent example of its potential. After ideas from animal psychology aided in the development of reinforcement learning research, essential principles from the latter were fed back into neuroscience. In another area, research aimed at improving the performance of CNNs has revealed fresh insights into the nature of neural representations in high-level visual domains.
Machine learning research is also generating new ideas on how the brain may implement an algorithmic counterpart to backpropagation, the crucial technique that permits weights to be optimized over numerous layers of a hierarchical network toward a certain purpose
When taken together, machine learning models hold out the prospect of identifying processes through which the brain may implement algorithms. Furthermore, these studies demonstrate the possibility of synergistic interactions between machine learning and neuroscience: study targeted towards biological development.
Conclusion
When planning for future collaboration between the two domains, it is vital to remember that previous contributions of neuroscience to ML have seldom entailed a straightforward transfer of full-fledged solutions that could be directly re-implemented in machines. Rather, neuroscience has been beneficial in a more subtle way, prompting algorithmic-level questions about aspects of animal learning and intelligence of interest to machine learning and offering preliminary leads toward relevant processes.
In the future, we hope that increased collaboration between neuroscience and machine learning, as well as the identification of a common language between the two fields will enable a virtuous circle in which research is accelerated through shared theoretical insights and common empirical advances.
We think that the pursuit of machine learning will eventually lead to a greater understanding of our own minds and mental processes. Distilling intelligence into an algorithmic construct and comparing it to the human brain may reveal insights into some of the mind's most profound and persistent mysteries, such as the source of creativity, dreams, and, possibly one day, consciousness.
