Neural Unpredictability, the Interpretation of Quantum Theory, and the Mind-Body Problem

It has been suggested, on the one hand, that quantum states are just states of knowledge; and, on the other, that quantum theory is merely a theory of correlations. These suggestions are confronted with problems about the nature of psycho-physical parallelism and about how we could define probabilities for our individual future observations given our individual present and previous observations. The complexity of the problems is underlined by arguments that unpredictability in ordinary everyday neural functioning, ultimately stemming from small-scale uncertainties in molecular motions, may overwhelm, by many orders of magnitude, many conventionally recognized sources of observed ``quantum'' uncertainty. Some possible ways of avoiding the problems are considered but found wanting. It is proposed that a complete understanding of the relationship between subjective experience and its physical correlates requires the introduction of mathematical definitions and indeed of new physical laws.Comment: 27 pages, plain TeX, v2: missing reference inserted, related papers from http://www.poco.phy.cam.ac.uk/~mjd101

Building Machines That Learn and Think Like People

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar

Hormonal Modulation of Developmental Plasticity in an Epigenetic Robot

In autonomous robotics, there is still a trend to develop and tune controllers with highly explicit goals and environments in mind. However, this tuning means that these robotic models often lack the developmental and behavioral flexibility seen in biological organisms. The lack of flexibility in these controllers leaves the robot vulnerable to changes in environmental condition. Whereby any environmental change may lead to the behaviors of the robots becoming unsuitable or even dangerous. In this manuscript we look at a potential biologically plausible mechanism which may be used in robotic controllers in order to allow them to adapt to different environments. This mechanism consists of a hormone driven epigenetic mechanism which regulates a robot’s internal environment in relation to its current environmental conditions. As we will show in our early chapters, this epigenetic mechanism allows an autonomous robot to rapidly adapt to a range of different environmental conditions. This adaption is achieved without the need for any explicit knowledge of the environment. Allowing a single architecture to adapt to a range of challenges and develop unique behaviors. In later chapters however, we find that this mechanism not only allows for regulation of short term behavior, but also long development. Here we show how this system permits a robot to develop in a way that is suitable for its current environment. Further during this developmental process we notice similarities to infant development, along with acquisition of unplanned skills and abilities. The unplanned developments appears to leads to the emergence of unplanned potential cognitive abilities such as object permanence, which we assess using a range of different real world tests

ECOLOGY AND VIOLENCE: THE ENVIRONMENTAL DIMENSIONS OF WAR

Research reported by Thomas Homer-Dixon characterizes five social effects that can significantly increase the likelihood of violence in the emerging world, effects that are far deeper than can be controlled by security forces: (1) constrained agricultural production, often in ecologically marginal regions; (2) constrained economic productivity, mainly affecting people who are highly dependent on environmental resources and who are ecologically and economically marginal; (3) migration of these affected people in search of better lives; (4) greater segmentation of society, usually along existing ethnic cleavages; and (5) disruption of institutions, especially the state.1 These kinds of social effects create tensions that can erupt in violent expression. It is difficult to envision how additional security forces will solve the embedded social problems that link violence with economic, social, ethnic, and even religious frustrations. This manuscript seeks to address these concerns. Part I elaborates ways in which these issues of violence manifest themselves in a globalized economy. Part II discusses the business implications of these tensions and suggests a way in which business can be a mediating actor to lessen these tensions. Part III concludes with a suggestion for a recharacterization of the corporation in a way to sensitize it to the ecological-mindedness necessary to address the potential issues of violence in societies. We propose sustainable peace as an aim to which businesses should orient their actions both for reasons of the good of avoiding the activities that contribute to the spilling of blood as well as for the good of sustainable economic enterprises, which are fostered by stable, peaceful relationships. Thus, business must do what it does best and address economic development, even in terms of the extraction of natural resources. But it must also be attentive to the rights of others, to the development of community and meaning, and to stop violence when it is likely. Given the dangers ecological stresses pose for the planet, it is hard to think of a more compelling reason to reorient business behavior.http://deepblue.lib.umich.edu/bitstream/2027.42/40084/3/wp698.pd

Neuronal oscillations, information dynamics, and behaviour: an evolutionary robotics study

Oscillatory neural activity is closely related to cognition and behaviour, with synchronisation mechanisms playing a key role in the integration and functional organization of different cortical areas. Nevertheless, its informational content and relationship with behaviour - and hence cognition - are still to be fully understood. This thesis is concerned with better understanding the role of neuronal oscillations and information dynamics towards the generation of embodied cognitive behaviours and with investigating the efficacy of such systems as practical robot controllers. To this end, we develop a novel model based on the Kuramoto model of coupled phase oscillators and perform three minimally cognitive evolutionary robotics experiments. The analyses focus both on a behavioural level description, investigating the robot’s trajectories, and on a mechanism level description, exploring the variables’ dynamics and the information transfer properties within and between the agent’s body and the environment. The first experiment demonstrates that in an active categorical perception task under normal and inverted vision, networks with a definite, but not too strong, propensity for synchronisation are more able to reconfigure, to organise themselves functionally, and to adapt to different behavioural conditions. The second experiment relates assembly constitution and phase reorganisation dynamics to performance in supervised and unsupervised learning tasks. We demonstrate that assembly dynamics facilitate the evolutionary process, can account for varying degrees of stimuli modulation of the sensorimotor interactions, and can contribute to solving different tasks leaving aside other plasticity mechanisms. The third experiment explores an associative learning task considering a more realistic connectivity pattern between neurons. We demonstrate that networks with travelling waves as a default solution perform poorly compared to networks that are normally synchronised in the absence of stimuli. Overall, this thesis shows that neural synchronisation dynamics, when suitably flexible and reconfigurable, produce an asymmetric flow of information and can generate minimally cognitive embodied behaviours

The Genetics Of Mosquito Heat-Seeking Behavior

Temperature is a highly dynamic feature of the world, and one that deeply affects living things. Organisms have evolved sophisticated sensory-­motor systems to detect and avoid excessive heat or cold—a behavior termed thermotaxis. In rare cases, however, animals use thermosensation not only to regulate their body temperature, but also to locate food sources in their environment. One example of such an adaptation is found in the female Aedes aegypti mosquito, which becomes attracted to the body heat of endothermic (“warm-­blooded”) hosts when in pursuit of a blood meal. Mosquitoes are remarkably adept at finding hosts in their environment and have become major vectors of human disease, but much remains to be understood about the ethology and sensory neurogenetics of this notorious insect. In this thesis, we used high-­throughput quantitative behavioral assays and genome-­editing techniques to investigate the behavioral rules and molecular basis of mosquito thermotaxis. We have found that female Aedes aegypti are exquisitely sensitive to thermal contrast, and are capable of heat-­seeking in diverse ambient environmental temperatures. By seeking relative warmth and avoiding relative cool, mosquitoes can thermotax towards heated targets. However, mosquitoes also avoid stimuli exceeding the body temperature of their hosts. In this manner, Ae. aegypti are maximally attracted to thermal stimuli approximating endothermic hosts such as humans. We have discovered that the insect thermosensor TRPA1, in addition to playing conserved roles in thermoregulation and chemosensation, is important for thermotactic tuning of heat-­seeking. AaegTRPA1-­/-­ mutant mosquitoes fail to avoid high-­temperature stimuli, and do not distinguish between thermal targets that resemble hosts and those that are inappropriately hot. This AaegTRPA1-­dependent tuning of thermotaxis may be critical for mosquitoes host-­seeking in a complex thermal environment in which hosts are warmer than ambient air, but cooler than surrounding sun-­warmed surfaces. These results demonstrate that evolutionarily conserved thermosensors, conventionally used for maintaining thermoregulatory homeostasis, can be repurposed by blood-­feeding arthropods to help locate and recognize the thermal signatures of their hosts. Our characterization of the behavioral strategies underlying heat-­seeking also helps to establish mosquitoes as a promising model system for the study of thermosensation and thermotaxis. These efforts may inform the design of next-­generation repellents and traps for the control mosquito-­borne diseases

UV Induced Processes in Biomolecules, Hydrated Clusters and Stark Deflected Beams

The nanoscale processes by which UV irradiation initiates damage in biological material have not yet been fully elucidated. This represents a barrier to innovations in radiotherapy and also limits our understanding of the molecular origins of life. Experiments on gas-phase biological building blocks can reveal detailed information via comparisons with high-level calculations, while parallel studies of biomolecular clusters offer a route to assess the effects of condensed biological environments. This thesis investigates the stabilities and relaxation pathways of isolated and clustered nucleobases in neutral electronic excited states and ionic states. Furthermore, it presents a new laser thermal desorption facility and reports advances in applying Stark deflection to narrow the range of molecular and cluster configurations in continuous supersonic beams. The key analytical tool in this thesis is UV multi-photon ionization time-of-flight mass spectrometry (MPI-TOF). High-resolution measurements on the RNA base uracil and its fully-deuterated analogue provide new evidence supporting a ring-opening process in a neutral electronic excited state. Comparing experiments on uracil in supersonic beams and from the laser thermal desorption source show that the MPI channel that marks this ring-opening process is not sensitive to the vaporization method and aid comparisons with time-resolved experiments at Heriot Watt University. Exploiting the capabilities of reflectron mass spectrometry to study the metastable dissociation of excited ions and cluster ions is a major theme in this thesis. Photon energy thresholds for metastable dissociation from multi-photon ionized uracil and thymine are reported for the first time. We argue that these thresholds can provide a new route to deduce the adiabatic energies of neutral electronic excited states. Further metastable dissociation experiments and detailed comparisons with ab initio calculations provide the most detailed study of the stable configurations of thymine-water cluster ions to date. The variety of different structural configurations of biomolecules and clusters in neutral target beams presents a major challenge in the experimental study of radiation-induced processes. Stark deflection in electric field gradients offers a powerful method to address this challenge and has previously been used to select specific polar species from mixed pulsed supersonic beams. However, the low pulsing frequencies of such beams are unsuitable to study many collision-induced processes. This thesis reports on the development of a system at the Open University that uses Stark deflection to manipulate continuous beams with the ultimate aim of probing electron attachment processes in selected clusters. Controlled deflection of nitromethane is demonstrated in a helium beam with 94% recovery of the deflected molecules. The manipulation of nucleobases using Stark deflection is achieved for the first time, with continuous argon and krypton expansions providing the necessary rotational cooling. Isolated and clustered nucleobases show very different responses to the deflecting fields, providing a method to gain insights into the origins of specific fragment ions in MPI and electron impact ionization experiments

Bio-inspired Dynamic Control Systems with Time Delays

The world around us exhibits a rich and ever changing environment of startling, bewildering and fascinating complexity. Almost everything is never as simple as it seems, but through the chaos we may catch fleeting glimpses of the mechanisms within. Throughout the history of human endeavour we have mimicked nature to harness it for our own ends. Our attempts to develop truly autonomous and intelligent machines have however struggled with the limitations of our human ability. This has encouraged some to shirk this responsibility and instead model biological processes and systems to do it for us. This Thesis explores the introduction of continuous time delays into biologically inspired dynamic control systems. We seek to exploit rich temporal dynamics found in physical and biological systems for modelling complex or adaptive behaviour through the artificial evolution of networks to control robots. Throughout, arguments have been presented for the modelling of delays not only to better represent key facets of physical and biological systems, but to increase the computational potential of such systems for the synthesis of control. The thorough investigation of the dynamics of small delayed networks with a wide range of time delays has been undertaken, with a detailed mathematical description of the fixed points of the system and possible oscillatory modes developed to fully describe the behaviour of a single node. Exploration of the behaviour for even small delayed networks illustrates the range of complex behaviour possible and guides the development of interesting solutions. To further exploit the potential of the rich dynamics in such systems, a novel approach to the 3D simulation of locomotory robots has been developed focussing on minimising the computational cost. To verify this simulation tool a simple quadruped robot was developed and the motion of the robot when undergoing a manually designed gait evaluated. The results displayed a high degree of agreement between the simulation and laser tracker data, verifying the accuracy of the model developed. A new model of a dynamic system which includes continuous time delays has been introduced, and its utility demonstrated in the evolution of networks for the solution of simple learning behaviours. A range of methods has been developed for determining the time delays, including the novel concept of representing the time delays as related to the distance between nodes in a spatial representation of the network. The application of these tools to a range of examples has been explored, from Gene Regulatory Networks (GRNs) to robot control and neural networks. The performance of these systems has been compared and contrasted with the efficacy of evolutionary runs for the same task over the whole range of network and delay types. It has been shown that delayed dynamic neural systems are at least as capable as traditional Continuous Time Recurrent Neural Networks (CTRNNs) and show significant performance improvements in the control of robot gaits. Experiments in adaptive behaviour, where there is not such a direct link between the enhanced system dynamics and performance, showed no such discernible improvement. Whilst we hypothesise that the ability of such delayed networks to generate switched pattern generating nodes may be useful in Evolutionary Robotics (ER) this was not borne out here. The spatial representation of delays was shown to be more efficient for larger networks, however these techniques restricted the search to lower complexity solutions or led to a significant falloff as the network structure becomes more complex. This would suggest that for anything other than a simple genotype, the direct method for encoding delays is likely most appropriate. With proven benefits for robot locomotion and the open potential for adaptive behaviour delayed dynamic systems for evolved control remain an interesting and promising field in complex systems research