The interdisciplinary field of Computational Neuroscience is the study of the development, structure, physiology, information processing and cognitive abilities of the nervous system. Computational neuroscience uses mathematical multi-scale models, theoretical analysis, and simulations of neural function from the perspective of molecules, cells and networks, all the way to cognition and behavior.
Computational Neuroscience FAQ’s
What is Computational Neuroscience?
Computational Neuroscience works to identify dynamic neural networks to understand the principles that govern neural systems and brain activity potentially related to information processing and brain disease. The quantitative nature of the field is primarily concerned with complex computational analysis of electrical and chemical signals in the brain to understand the role of neurons in the processing of information using mathematical models at various spatial-temporal scales. These neural models provide experimental data that are used to generate new hypotheses that can further be tested by biological or psychological experiments.
Why is Computational Neuroscience Important?
Research studies in the field of computational neuroscience are complementary with psychology in understanding the nature of behaviors. Whereas a psychologist might attribute behavior to certain attitudes or emotions, a computational neuroscientist might interpret that behavior concerning the efficiency that certain groups of neurons propagate information in a specific context within the brain. They view the brain as a black box – there are numerous inputs and environmental variables introduced, something happens inside, then the behavior is performed. Computational neuroscience attempts to simulate brain function to find the unknown factors that influence human behavior.
As computational neuroscientists advance their understanding of the structure of the human brain at cellular and molecular levels, various fields of neuroscience develop and innovative applications begin to emerge. Important frontiers of computational neuroscience include the rapidly expanding field of artificial intelligence and machine learning, medical sciences, and human psychology. Further into the future, neuroscientists predict applications in optimizing computer chips, constructing very-large-scale integration (VLSI) architectures with neuromorphic engineering, advancing Human Machine Interface (HMI) technology for the handicapped and more.
What’s the Relationship Between Computational Neuroscience and Machine Learning?
Machine learning is the scientific study of statistical models and algorithms used to automate computer systems to perform specific tasks without explicit instruction. Some computational neuroscientists have attempted to integrate this technology into their research by developing new data analysis techniques that would allow them to analyze larger and more complex datasets that they otherwise wouldn’t be able to interrogate. However, these two terms should not be used interchangeably. For example, many computational neuroscience experimentalists are focused on creating dynamic neural network models that are as biologically realistic as possible. This aspect of computational neuroscience does not align with machine learning, whereas optimizing mathematical insights within data regarding how the brain stores information might provide a productive overlap.
Current Computational Neuroscience Research Topics
- Single-neuron modeling: Single neurons are the fundamental building block of any nervous system. Researchers utilize quantitative models to understand how these single-cell properties contribute to information processing and underlying behaviors.
- Development and direction of neural circuits: Computational neuroscientists are continuously concerned with how axons and dendrites are formed during development, how axons migrate to proper positions in the central and peripheral nervous systems, how molecular biology influences functional neural connections and more.
- The behavior of neural networks: Biological neural networks are complex and sparse compared to artificial neural networks. One of the goals in computational neuroscience is to understand the computational functions in specific areas of the brain, often attempted with simple models like the Ising model. The behaviors of these connected networks can be further understood by simplifying the complex interactions of inhibitory and excitatory neurons using the mean-field theory.
- Learning and memory: Human beings can memorize and recognize an immense number of faces, even those they have only seen once. Computational neuroscientists are attempting to understand how biological systems can perform such complex calculations so efficiently, and potentially build an intelligent machine that could replicate this ability.
- Computational Cognitive Neuroscience: CCN is focused on modeling the biological activity of the brain and cognitive processes to further understand perception, behavior, and decision making. Computational and cognitive neuroscience often intersect with machine learning and neural network theory.
Does EMOTIV Offer Computational Neuroscience Solutions?
EMOTIV offers a dynamic lineup of hardware and software solutions for computational neuroscience research. Computational neuroscientists can develop innovative insights using EMOTIV’s Brainwear paired with the capability to view, record, and export raw EEG data with the EmotivPRO Software. EMOTIV’s solutions have been validated in peer-reviewed scientific, medical and clinical studies and publications for neuroscience, biometrics, neuroethics, neuromarketing, and brain-controlled technology applications.
The award-winning EMOTIV EPOC+ headset provides professional-grade brain data for academic research within computational neuroscience. The EMOTIV Insight headset boasts minimal set-up time and electronics optimized to produce clean signals from anywhere, making it ideal for understanding memory behavior. The EMOTIV EPOC FLEX cap offers high density coverage and moveable electroencephalogram sensors optimal for research professionals.