Computational Neuroscience

What is Computational Neuroscience?

The term computational neuroscience is often used to denote theoretical approaches in neuroscience, focusing on how the brain computes information. It is about using computational techniques to investigate the properties of nervous systems at different levels of detail. Computational neuroscience has much experience in multiscale modeling and the analysis of information processing by biological systems. Computational neuroscience has come to encompass a program of modeling neural activity and brain function, from subcellular biophysics to human behavior and advanced methods for analyzing neural data.

Origins of Computational Neuroscience

The origin of computational neuroscience may be the mathematical model developed of the squid giant axon action potential Alan L. Hodgkin and Andrew F. Huxley. Also, the Hodgkin and Huxley model remains a cornerstone of the field and is still extensively used in its original form, and they received the Nobel prize in 1963. Wilfrid Rall used mathematical approaches to show that the dendritic arborizations of neurons strongly affect the processing of synaptic input. He pioneered the use of digital computers in neuroscience. He developed compartmental modeling, which forms the basis for some software packages in computational neuroscience (such as GENESIS and NEURON).

What Has Computational Neuroscience Offer?

A major advantage of computational neuroscience is the accumulated know-how in simulator software development, especially for multiscale modeling. The multiscale simulator GENESIS allows using the kinetic module to include detailed biochemical pathways simulations into morphologically detailed neuron models or large neural network models. Computational neuroscience has analyzed neural coding from synapses, over spike trains in single neurons to information processing at the network and the systems levels. These modeling tools allow for accurate measurement and comparison of information transfer rates, detailed characterization of optimal spatiotemporal input profiles, and the definition of optimal coding schemes.

Fig.1 Convolutional neural network structure. (Kietzmann, 2018)

Information Theory

Recently, information theoretic methods have started to enter the systems biology domain. A common information-theoretic method in computational neuroscience is to obtain quantitative estimates of the mutual information between observed sets of data. Indeed, the actual goal of computational neuroscience is that of system identification. If the brain uses spikes to transmit information, understanding the neural code-how the brain encodes the information before sending it across the channel is tantamount to understanding how the brain works. With computational neuroscience as a main motivator, the next decade will expand information theory to include more powerful techniques for system identification and possibly even integrating control, computation, and information theory into a unified framework.

Deep Neural Networks (DNNs) - Exciting Tools in Computational Neuroscience

Computational neuroscience aims to find mechanistic explanations of how the nervous system processes information to give rise to cognitive function and behavior. The heart of this field is its model. Recently, the DNNs have become an exciting tool for computational neuroscience with full access to the activity and connectivity of all units, advanced visualization techniques, and analytic tools to map network representations to neural data. It allows for key insights into the recurrent computational dynamics of the brain, from sensory processing to flexible cognitive tasks.

Fig.2 Cartoon overview of different models in computational neuroscience. (Kietzmann, 2018)

Combination of Computational Neuroscience, Cognitive Science and Artificial Intelligence (AI)

Computational neuroscience has modeled how interacting neurons implement elementary components of cognition. Computational models that mimic brain information processing during perceptual, cognitive, and control tasks are developed and tested. Computational neuroscience needs cognitive science to challenge it to engage higher-level cognition to explain how the neurobiological dynamical components it studies contribute to cognition and behavior. Computational neuroscience also needs AI to provide the theoretical and technological basis for modeling cognitive functions with biologically plausible dynamical components. If cognitive science, computational neuroscience, and AI come together, scientists might explain human cognition with neurobiologically plausible computational models.

Fig.3 What does it mean to understand how the brain works? (Kriegeskorte, 2018)

Computational Neuroscience for Clinical Applications

Recently, the application of computational neuroscience to psychiatry research has shown great promise in establishing a link between phenomenological and pathophysiological aspects of mental disorders. On the one hand, the theory-driven approach plays an integrative role in this process by enabling correspondence between behavior and disorder-specific alterations at multiple levels of brain organization. On the other hand, the data-driven approach is an emerging field in computational neuroscience to identify disorder-specific features among high-dimensional big data. Remarkably, various machine-learning techniques have been applied to neuroimaging data, and the extracted disorder-specific features have been used for automatic case-control classification. However, rigorous tests on independent cohorts are critically required to translate this research into clinical applications.

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References

1. Kietzmann, T. C.; et al. Deep neural networks in computational neuroscience. BioRxiv. 2018, 133504.
2. Kriegeskorte, N.; Douglas, P. K. Cognitive computational neuroscience. Nature neuroscience. 2018, 21(9), 1148-1160.