16,226 research outputs found
Can biological quantum networks solve NP-hard problems?
There is a widespread view that the human brain is so complex that it cannot
be efficiently simulated by universal Turing machines. During the last decades
the question has therefore been raised whether we need to consider quantum
effects to explain the imagined cognitive power of a conscious mind.
This paper presents a personal view of several fields of philosophy and
computational neurobiology in an attempt to suggest a realistic picture of how
the brain might work as a basis for perception, consciousness and cognition.
The purpose is to be able to identify and evaluate instances where quantum
effects might play a significant role in cognitive processes.
Not surprisingly, the conclusion is that quantum-enhanced cognition and
intelligence are very unlikely to be found in biological brains. Quantum
effects may certainly influence the functionality of various components and
signalling pathways at the molecular level in the brain network, like ion
ports, synapses, sensors, and enzymes. This might evidently influence the
functionality of some nodes and perhaps even the overall intelligence of the
brain network, but hardly give it any dramatically enhanced functionality. So,
the conclusion is that biological quantum networks can only approximately solve
small instances of NP-hard problems.
On the other hand, artificial intelligence and machine learning implemented
in complex dynamical systems based on genuine quantum networks can certainly be
expected to show enhanced performance and quantum advantage compared with
classical networks. Nevertheless, even quantum networks can only be expected to
efficiently solve NP-hard problems approximately. In the end it is a question
of precision - Nature is approximate.Comment: 38 page
EEG in the classroom: Synchronised neural recordings during video presentation
We performed simultaneous recordings of electroencephalography (EEG) from
multiple students in a classroom, and measured the inter-subject correlation
(ISC) of activity evoked by a common video stimulus. The neural reliability, as
quantified by ISC, has been linked to engagement and attentional modulation in
earlier studies that used high-grade equipment in laboratory settings. Here we
reproduce many of the results from these studies using portable low-cost
equipment, focusing on the robustness of using ISC for subjects experiencing
naturalistic stimuli. The present data shows that stimulus-evoked neural
responses, known to be modulated by attention, can be tracked in for groups of
students with synchronized EEG acquisition. This is a step towards real-time
inference of engagement in the classroom.Comment: 14 pages, 5 figures, 3 tables. Preprint version. Revision of original
preprint. Supplementary materials added as ancillary fil
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