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

Of Toasters and Molecular Ticker Tapes

Experiments in systems neuroscience can be seen as consisting of three steps: (1) selecting the signals we are interested in, (2) probing the system with carefully chosen stimuli, and (3) getting data out of the brain. Here I discuss how emerging techniques in molecular biology are starting to improve these three steps. To estimate its future impact on experimental neuroscience, I will stress the analogy of ongoing progress with that of microprocessor production techniques. These techniques have allowed computers to simplify countless problems; because they are easier to use than mechanical timers, they are even built into toasters. Molecular biology may advance even faster than computer speeds and has made immense progress in understanding and designing molecules. These advancements may in turn produce impressive improvements to each of the three steps, ultimately shifting the bottleneck from obtaining data to interpreting it

Interventions on Cultural Heritage: Architecture and Neuroscience for Mindful Projects

The paper aims to investigate the intersections between the disciplines of Architecture and Neuroscience focusing on interventions on cultural heritage. Starting from the assumption that the main objective of architecture is ensuring the well-being of the inhabitants at different scales, and that architecture (with its forms, its proportions, its spatial hierarchies, its relationships) generates behaviors, the authors investigate the terrain of overlap with neuroscience, (particularly in its openings towards the disciplines of psychology, social and behavioral sciences) in order to design meaningful cultural experiences. The concept of disciplinary contamination regarding cultural heritage is not to be discussed only in physical terms, but also in intangible terms including all social and cultural values of heritage buildings and sites. This is also because values associated with cultural heritage can be protected and enriched by an approach that generates reactions on a cognitive and emotional level, and it needs to be mediated both at the level of architectural interventions and museography. For this reason, starting from the first intuitions that some designers had during the twentieth century, the paper investigates possible ways of collaboration and experimentation and refers to studies currently underway

Can Computers overcome Humans? Consciousness interaction and its implications

Can computers overcome human capabilities? This is a paradoxical and controversial question, particularly because there are many hidden assumptions. This article focuses on that issue putting on evidence some misconception related with future generations of machines and the understanding of the brain. It will be discussed to what extent computers might reach human capabilities, and how it could be possible only if the computer is a conscious machine. However, it will be shown that if the computer is conscious, an interference process due to consciousness would affect the information processing of the system. Therefore, it might be possible to make conscious machines to overcome human capabilities, which will have limitations as well as humans. In other words, trying to overcome human capabilities with computers implies the paradoxical conclusion that a computer will never overcome human capabilities at all, or if the computer does, it should not be considered as a computer anymore.Comment: 16th IEEE Cognitive Informatics and Cognitive Computing preprint, 8 pages; Added references and short discussion for section

Embodied cognition in robots and human evolution

Thesis (S.M. in Science Writing)--Massachusetts Institute of Technology, Dept. of Humanities, Graduate Program in Science Writing, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 44).This thesis investigates the notion of embodied cognition in humans using the research of former University of Washington researcher William Calvin and robots using the research of former MIT professor Rodney Brooks. The idea is that the feedback from the physicality of humans is a precognition to our intelligence. The choice example I use for our physicality is the motion of throwing, particularly the javelin throw. For robotics, I focus on the development of 'eyes' in Brooks' robot Cog and show how it demonstrated behavior we deem to be intelligent using the feedback gleaned from 'seeing'. Altogether, I present evidence for and against the notion that we are who we are, cognitively speaking, because of the sensory feedback of our physical bodies, and what that may mean going forward in the future for our intelligence.by Conor L. Myhrvold.S.M.in Science Writin

Receptive field atlas and related CNN models

In this paper we demonstrate the potential of the cellular nonlinear/neural network paradigm (CNN) that of the analogic cellular computer architecture (called CNN Universal Machine | CNN-UM) in modeling different parts and aspects of the nervous system. The structure of the living sensory systems and the CNN share a lot of features in common: local interconnections ("receptive field architecture"), nonlinear and delayed synapses for the processing tasks, the potentiality of feedback and using the advantages of both the analog and logic signal-processing mode. The results of more than ten years of cooperative work of many engineers and neurobiologists have been collected in an atlas: what we present here is a kind of selection from these studies emphasizing the exibility of the CNN computing: visual, tactile and auditory modalities are concerned

Cellular Neural Networks with Switching Connections

Artificial neural networks are widely used for parallel processing of data analysis and visual information. The most prominent example of artificial neural networks is a cellular neural network (CNN), composed from two-dimensional arrays of simple first-order dynamical systems (“cells”) that are interconnected by wires. The information, to be processed by a CNN, represents the initial state of the network, and the parallel information processing is performed by converging to one of the stable spatial equilibrium states of the multi-stable CNN. This thesis studies a specific type of CNNs designed to perform the winner-take-all function of finding the largest among the n numbers, using the network dynamics. In a wider context, this amounts to automatically detecting a target spot in the given visual picture. The research, reported in this thesis, demonstrates that the addition of fast on-off switching (blinking) connections significantly improves the functionality of winner-take-all CNNs. Numerical calculations are performed to reveal the dependence of the probability, that the CNN correctly classifies the largest number, on the switching frequency

Self-organized criticality as a fundamental property of neural systems

The neural criticality hypothesis states that the brain may be poised in a critical state at a boundary between different types of dynamics. Theoretical and experimental studies show that critical systems often exhibit optimal computational properties, suggesting the possibility that criticality has been evolutionarily selected as a useful trait for our nervous system. Evidence for criticality has been found in cell cultures, brain slices, and anesthetized animals. Yet, inconsistent results were reported for recordings in awake animals and humans, and current results point to open questions about the exact nature and mechanism of criticality, as well as its functional role. Therefore, the criticality hypothesis has remained a controversial proposition. Here, we provide an account of the mathematical and physical foundations of criticality. In the light of this conceptual framework, we then review and discuss recent experimental studies with the aim of identifying important next steps to be taken and connections to other fields that should be explored.Peer Reviewe