Mathematical simplicity and biological plausibility. The combining of these two axes is at the core of the inception and development of FAPESP’s Research, Innovation and Dissemination Center for Neuromathematics (RIDC NeuroMat). “Without biology, mathematicians can at most generate nice models, that could have internal coherence, but remain absolutely useless to provide a conceptual framework for neuroscience. Without mathematics, neuroscientists are unable to move to a higher level of abstraction and could fall into the trap of driving all their scientific power to gather minute details that have little to inform to a systemic understanding of neural nets,” said NeuroMat’s coordinator, Antonio Galves.
The workshop “High-Performance Computing, Stochastic Modeling and Databases in Neuroscience,” that FAPESP's Research, Innovation and Dissemination Center for Neuromathematics (RIDC NeuroMat) held in the last week of April, was an occasion for strengthening ties among brain-science international consortia. Specifically, this event contributed to engaging NeuroMat’s technology-transfer team within the network that is facilitated by the International Neuroinformatics Coordinating Facility (INCF) with the forthcoming creation of creation of an INCF Special Interest Group on “stochastic modeling and statistical analysis of neural systems” and a formal collaboration with the INCF Program on Standards for Data Sharing, particularly with a task force on electrophysiology and on neuroimaging. INCF’s scientific director Sean Hill attended NeuroMat’s workshop and gave a talk on computational challenges of understanding the brain.
According to the International Neuroinformatics Coordinating Facility (INCF), the current large-scale international brain initiatives are from the USA (the BRAIN Initiative and the Allen Institute), Europe (Human Brain Project), Japan (Brain/MINDS), Israel (IBT), Republic of Korea (Korea Brain Research Institute), China (China Brain) and Australia (AusBrain). Therefore, there are no internationally recognized Brazilian brain projects, not even Latin-american projects.
Almost simultaneously readers of the scholar journal Scientific Reports and the Portuguese and English editions of the electronic encyclopaedia Wikipedia had access to the same finding: the relationship between predictability and reaction time is sigmoid, not linear. This finding goes in contrast to what is known as Hick’s Law, a theory on the time a person takes to make a decision as a function of the possibilities that this person faces: more specifically, it is traditionally stated that the reaction time increases as a linear function of the log of the number of alternatives. On March 15, 2016, NeuroMat member André Frazão Helene and colleagues published "On Sequence Learning Models: Open-loop Control Not Strictly Guided by Hick’s Law,” on Scientific Reports, that on March 21 led to a full rewriting of the Portuguese Wikipedia entry on "Lei de Hick" and the incorporation of a section on "Exceptions to Hick's Law” on the English Wikipedia, on March 24. Modifications on the encyclopaedia were in strict accordance with findings in the new scholar publication.
Will the penalty taker shoot for the right, left or center of the football goal? Professional goalkeepers know that they must take into consideration as much information as possible to prevent the score: the history of the rival player, the position of the taker before contact, and so on. By doing so, goalkeepers are in fact generating a model to improve their prediction about how to stop the hit, though they might not even realize the cognitive process at play. This general idea —that one could call the Goalkeeper’s Dilemma— is potentially the basis of a renewed contribution to the understanding of brain functioning, at least according to members of an ongoing research project at FAPESP’s Research, Innovation and Dissemination Center for Neuromathematics (RIDC NeuroMat).
Featuring this week:
Stay informed on our latest news!
|Follow Us on Facebook|