Neuroscience

Neuroscience is the scientific study of the nervous system. Traditionally, neuroscience is recognized as a branch of biology. However, it is currently an interdisciplinary science that collaborates with other fields such as chemistry, cognitive science, computer science, engineering, linguistics, mathematics, medicine (including neurology), genetics, and allied disciplines including philosophy, physics, and psychology. It also exerts influence on other fields, such as neuroeducation, neuroethics, and neurolaw. The term neurobiology is often used interchangeably with the term neuroscience, although the former refers specifically to the biology of the nervous system, whereas the latter refers to the entire science of the nervous system (thus can include elements of psychology as well as the purely physical sciences).

The scope of neuroscience has broadened to include different approaches used to study the molecular, cellular, developmental, structural, functional, evolutionary, computational, and medical aspects of the nervous system. The techniques used by neuroscientists have also expanded enormously, from molecular and cellular studies of individual nerve cells to imaging of sensory and motor tasks in the brain. Recent theoretical advances in neuroscience have also been aided by the study of neural networks.

As a result of the increasing number of scientists who study the nervous system, several prominent neuroscience organizations have been formed to provide a forum to all neuroscientists and educators. For example, the International Brain Research Organization was founded in 1960, the International Society for Neurochemistry in 1963, the European Brain and Behaviour Society in 1968, and the Society for Neuroscience in 1969.

There are two main projects related with Neuroscience as they are:

• Human connectome project (inferring connectome)
• Human Brain project (create a synthetic brain by reverse-engineering for High-Performance computing)

My main concerns and interests about this topic are:

• Connectomics: the inference and study of the structural,
• Relations between connectomes and high-level emerged properties (behavior and personality).
• Relation with connectionism (Artificial Neural Networks methods for pattern discovering and statistical modeling).

See also

fMRI, EEG, dMRI

Papers

• Brown, E. N., Kass, R. E., & Mitra, P. P. (2004). Multiple neural spike train data analysis: state-of-the-art and future challenges. Nature Neuroscience, 7, 456-461.

Books

• Bear, M. F., Connors, B. W., Paradiso, M., Bear, M. F., Connors, B. W., & Neuroscience, M. A. (1996). Exploring the brain. Neuroscience: Williams & Wilkins.
• Purves, D., Augustine, G. J., Fitzpatrick, D., Katz, L. C., LaMantia, A., McNamara, J. O., & Williams, S. M. (2001). Neuroscience. Sunderland. MA: Sinauer Associates.
• Siegelbaum, S. A., & Hudspeth, A. J. (2000). Principles of neural science. E. R. Kandel, J. H. Schwartz, & T. M. Jessell (Eds.). New York: McGraw-hill.
• Sporns, O. (2010). Networks of the Brain. MIT press.
• Sejnowski, T. J. (1994). The computational brain. MIT press.
• Dayan, P., & Abbott, L. F. (2003). Theoretical neuroscience: computational and mathematical modeling of neural systems. Journal of Cognitive Neuroscience, 15(1), 154-155.
• Ermentrout, G. B., & Terman, D. H. (2010). Mathematical foundations of neuroscience (Vol. 35). Springer Science & Business Media.
• Wilson, H. R. (1999). Spikes, decisions, and actions: the dynamical foundations of neurosciences. Oxford University Press.