The Cognitive Neuroscience Journal

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Rapid Communication - The Cognitive Neuroscience Journal (2022) Volume 5, Issue 2

An overview of the relationship between AI and neuroscience research

Konrad Nguemeni*

Department of Psychology, Dalhousie University, Nova Scotia, Canada

Corresponding Author:
Konrad Nguemeni
Department of Psychology
Dalhousie University
Nova Scotia, Canada

Received: 05-Apr-2022, Manuscript No. AACNJ-22-110; Editor assigned: 07-Apr-2022, PreQC No. AACNJ-22-110(PQ); Reviewed: 21-Apr-2022, QC No. AACNJ-22-110; Revised: 23-Apr-2022, Manuscript No. AACNJ-22-110(R); Published: 30-Apr-2022, DOI: 10.35841/aacnj-5.2.110

Citation: Nguemeni K. An overview of the relationship between AI and neuroscience research. Cogn Neurosci J. 2022;5(2):110

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Artificial Intelligence (AI) and neuroscience have a long history of partnership. Over the last few decades, advances in neuroscience have spawned a new generation of in silico neural networks inspired by brain architecture, thanks to tremendous gains in computer processing power. Many of the superior perceptual and cognitive abilities of biological systems, such as object recognition and decision making, are now available to AI systems. Furthermore, AI is increasingly being used in neuroscience research, altering our understanding of how the brain works. Deep learning has been used to mimic how the cerebral cortex's convolutional layers and recurrent connections govern essential activities like visual processing, memory, and motor control. The employment of neuroscience-inspired techniques is particularly exciting.


Artificial intelligence, Neuroscience, Neural imaging, Computational psychiatry.


Traditionally, the capacities of advanced biological things, most notably humans, have been used to define intelligence. As a result, Artificial Intelligence (AI) research has mostly concentrated on the development of computers that can observe, learn, and reason, with the ultimate goal of developing an Artificial General Intelligence (AGI) system that can mimic human intelligence, also known as Turing-powerful systems. Given this goal, it's no surprise that scientists, mathematicians, and philosophers working on artificial intelligence have drawn inspiration from the brain's mechanical, structural, and functional features [1].

Despite its neuroscience-inspired origins, current AI's biological plausibility is debatable. Indeed, despite recent notions to the contrary, there is little evidence that back propagation of mistake is responsible for the change of synaptic connections between neurons. In the brain, there may be an approximation of back propagation. While constructing brain-like systems is definitely not required to fulfil all AI goals, as proven by the above-mentioned achievements, biologically realistic AI has a significant advantage in terms of understanding and modelling information processing in the brain. Brain mechanisms can also be viewed as an evolutionary verified template for intelligence, developed over millions of years for adaptability, speed, and energy economy. As a result, greater integration of brain-inspired procedures may aid in improving AI's capabilities and efficiency. These concepts have sparked renewed interest in the development of neuroscience-inspired AI and reinforced the relationship between AI and neuroscience research [2].

Predictive coding, feedback alignment, equilibrium propagation, Hebbian-like learning rules, and zero-divergence inference learning have all been proposed as methods of propagation. Other recent efforts to bridge the gap between artificial and biological neural networks have resulted in the development of spiking neural networks that approximate stochastic potential-based communication between neurons, as well as attention-like mechanisms such as transformer structures. AI and neuroscience have a mutually beneficial relationship, and AI is quickly becoming an indispensable tool in neuroscience research. New concepts about how the same processes are managed within the brain are emerging from AI models meant to execute intelligence-based tasks [3].

Despite the use of standardised diagnostic criteria in clinical manuals such as the Diagnostic and Statistical Manual of Mental Disorders (DSM) and the International Classification of Diseases and Disorders (ICD), Diseases, psychiatric disorders, and developmental abnormalities are still mostly diagnosed mainly on a patient's subjective behavioural symptoms and self-report assessments. Not only is this method problematic due to its subjectivity, but it also results in a phenomenologicalneurobiological explanatory gap. However, massive advances in processing power, combined with the collecting of large neuroimaging datasets, have allowed researchers to begin to overcome this gap by utilising AI to identify, model, and perhaps cure psychiatric and developmental diseases in the previous few decades.

It is currently doable to check the medicine pathways that turn out human noesis for the primary time in history. It's doable as a result of experimental ways and techniques for finding out the structure of the brain square measure currently offered, and since the theoretical information is offered that gives the likelihood of a theoretical analysis of neural mechanisms of noesis, and since the pc power is currently offered that gives the likelihood of large-scale simulations and numerical analyses of those mechanisms [4].

However, the complexness of the brain, and therefore the psychological feature processes it produces, entails that integrated multidisciplinary experience is required to mix these lines of analysis. The process perspective on neurocognition, aimed toward understanding however the neural dynamics and neural mechanisms of the brain turn out noesis, will play an elementary role during this respect, as a result of it focuses on the final word aim of neurocognition. So, AI has a vital role to play during this method [5].


Importantly, whereas it's not been mentioned in nice detail within the current review, it ought to be thought-about that the link between AI and neurobiology isn't merely two-way, however rather additionally includes the sector of scientific discipline. Indeed, over the years, a lot of AI analysis has been target-hunting by theories of brain functioning established by psychological feature scientists. for instance, the convolutional neural networks mentioned earlier during this review (in the section on visual processing) were impressed partly by process models of noesis among the brain, as well as principles like nonlinear feature maps and pooling of inputs, that were themselves derived from observations from neuroscience studies in animals.

In turn, neural networks are accustomed guide new psychological feature models of intellectual talents, as well as perception, memory, and language, giving rise to the connectionism movement among scientific discipline. If we tend to square measure to use AI to model and doubtless elucidate brain functioning, the first focus of scientific discipline, it's necessary that we tend to still not solely use biological information from neurobiology studies, however additionally psychological feature models, to inspire the subject area, mechanistic, and algorithmic style of artificial neural networks.


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