Modern Neuroscience and Quantum Logic

  • Sisir RoyEmail author


The decision making from a neuroscience perspective is one of the outstanding problems in twenty first-century science. The evolution of higher brain function has given us the capacity for flexible decision making. Gerstner et al. (Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition. Cambridge University Press, Cambridge, 2014) made an attempt to understand decision making based on an interacting neuronal model. According to their proposal, various neuronal populations with different options take part in a competition, and the population with the highest activity wins that competition. Here, the group of strongly connected neurons is considered to play the important role in making the choice. This procedure of Gerstner has been elaborated in this chapter to understand decision making. Next, we address the neuronal architecture necessary for implementing the logic called “quantum logic”. In fact, it becomes necessary and important to find out which neuronal circuitry in this architecture is responsible for decisions and, at the same time, what are the underlying processes they follow in arriving at a decision. McCollum (Systems of Logical systems; Neuroscience and quantum logic: Foundation of science, Springer, Berlin, 2002) made an attempt to understand and apply quantum logic within the framework of modern neuroscience. It is noted that he is more realistic in his approach to understanding decision making and brain function. He made the interesting observation in that, in the beginning, one does not need to assume all kinds of mathematical formalism like Hilbert space structure, quantum probability, etc. Instead, it is worthwhile to study the functioning of the neuronal architecture first, and then, as the next step, to look for a suitable logic or mathematical tools to explain the observed results. But the central issue, i.e., “Where is the decision taken”, still remains a mystery.


Decision making Quantum logic Interacting neuron Stochastic equation 


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Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.National Institute of Advanced Studies, IISc CampusBengaluruIndia

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