Machine learning in neuroscience
Machine learning in neuroscience

What is neuroscience?

Neuroscience is the branch of science that deals with the study of neurons of brain and it also works with chemistry, medicine, psychology, physiology, anatomy and molecular biology to comprehend the behavior and working of neurons. Also, Neuroscience is very important to understand human brain functions and many conditions including epilepsy, addiction, schizophrenia, language loss due to stroke, Down syndrome and many other disorders. This extensive study of neuroscience can lead to better medication and strategies development to treat neuron issues. Further we will discuss Machine Learning in Neuroscience.

Neuroscience also divided into many branches. Few of them are discuss below:

This discipline works for how human brain behaves in terms of emotions. It combines emotions and mood with psychology of a human.

Neuroinformatics:

Affective neuroscience:

This research field integrates and analyze large and high dimensional experimental data to provide computational tools and databases for researchers and clinicians. So, Neuroinformatics has wide use in managing and sharing neuroscience data. Analysts use this data for analysis of neurons.

Neuroimaging:

It includes study to image the structure of brain as well as its functions to monitor the health of brain as shown in figure 2.

Cultural neuroscience:

This field works on interrelation between cultural factors and neuron systems. so It describes the variations of healthiness and its measures between population of different ages.

Computational neuroscience:

This research field explains that how brain computes. Scientists simulate models of brain to understand its functions.

Cognitive neuroscience:

This field focuses on neural connections that are involve in mental processes. It majorly deals with biological processes or brain activities that underlie cognition. It also includes both branches i.e. neuroscience and psychology. Researchers measure neural factors to control thoughts during performance of various tasks.

Fig. 2. Images of mind in different states and neuroimaging methods

How neuroscience is related to ML?

In Fact, Machine Learning (ML) algorithms are use in medical applications especially for the purpose of diagnosing a disease and factors that may cause a disease. The medical data required for biomedical applications is usually output of medical devices that are fix in human bodies. Also mostly regression and classification models of ML are use in neuroscience related projects. Classification models work on decision boundary to distinguish one class from the other while regression model predicts the outcomes and finds relation of different input variables. ML is use in neuroscience to identify brain disorder or prediction that neurons will affect by disease or not.

On the other hand, neuroscience can help in generating artificial brains with the help of extensive analysis of  neurons of brain and its functions. ML algorithms and neuron networks can improved on the basis of simulated brain models. Artificial intelligence techniques of memorization, transfer learning and reinforcement learning can be  upgraded using research data in neuroscience.

Figure 3 : Supervised Machine Learning Algorithm to diagnose Disease

Human brain project:

The human brain project (HBP) launched in October 2013, is scientific research that went for ten years, is based on supercomputers and aims to provide knowledge to researchers in neuroscience domain. HBP consists of 12 subprojects as shown in figure  and explained as follow.

  1. Mouse Brain Organisation: This subproject guarantees the interpretation of the structure of the mouse brain and its functions
  2. Human Brain Organisation: It includes understanding of  the structure and also shape of the human brain, and its functions
  3. Systems and Cognitive Neuroscience: It explains the knowledge of functional activities of brain at systems-level
  4. Theoretical Neuroscience: It develops complex mathematical models to derive conclusions from data statistics
  5. Neuroinformatics Platform: It works on gathering brain data and also organizes it to make it available to researchers
  6. Brain Simulation Platform: This subproject involves analysis of  reconstruction of brain tissues and also its simulation models
  7. High-performance Analytics and Computing Platform: This platform provides the ICT capability to represent the brain in unique details, building of complex models and also its simulations
  8. Medical Informatics Platform: This subproject creates substructure for the sharing of medical research data to hospitals. Also It aims to understand disease clusters
  9. Neuromorphic Computing Platform: It aims to evolve and apply brain-inspired computations
  10. Neurorobotics Platform: This platform works on the development of real and virtual robots and also provides space for evaluating brain simulations
  11. Central Services: It involves managerial and also coordination tasks related to project
  12. Ethics and Society: This platform reports the feedback and impact of HBP on society
Figure 4 : Subprojects and CDPs of Human Brain Project

Research

As neuroscience works on functionality of brain and so its application areas involves diagnosis of neurons disorder and their treatment. Latest research has done in improvement, understanding and prediction of diseases. Neuroscientists collect vast data for study of brain. There has been an increasing attention in leveraging this large amount of data  across various stages of analysis, experiments and measurement techniques to understand brain function. Artificial intelligence when used with neuroscience, can yield mechanisms that produces human cognition. Detailed analysis of human perception, attention, memory and language done use machine learning to estimate behaviors. A lot of research done in history on the observation of living brain.

Machine learning is also use in analyzing brain graphs and predicting approaches for functional systems of neuroscience. Some imaging modalities such as functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG) and electroencephalography (EEG) etc. have been developed but now researchers use regularization methods in machine learning to well-establish these techniques. Moreover, neuroimaging and brain graphs are helpful for disease diagnosis, prognosis and predictive modeling. Machine learning is also used in cognitive neuroscience to describe how brain reacts in different states.

Discussions:

Causes underlying mental illness are difficult to determine and misunderstood very often. Psychiatrists are now intended to comprehend the complex brain patterns, functionality of neurons, genes and their behavior using machine learning techniques. Support vector machines, cross validation procedures and artificial neural networks are employed for analysis of neurological diseases. Medical tools and methods can advanced using these artificial intelligence algorithms. Work on patient’s treatment selection, dosage selection and health betterment is major neuroscientists’ concern in modern era. Machine learning algorithms are tuned to make psychiatrists’ treatment more precise and accurate. Generalized performance in brain models can only be obtained by training algorithms on fresh big data with cross validation and regularization techniques. 

To know more about Neurogaming in EEG and BCI

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