Oscillations, metastability and phase transitions in brain and models of cognition

Neuroscience is being practiced in many different forms and at many different organizational levels of the Nervous System. Which of these levels and associated conceptual frameworks is most informative for elucidating the association of neural processes with processes of Cognition is an empirical question and subject to pragmatic validation. In this essay, I select the framework of Dynamic System Theory. Several investigators have applied in recent years tools and concepts of this theory to interpretation of observational data, and for designing neuronal models of cognitive functions. I will first trace the essentials of conceptual development and hypotheses separately for discerning observational tests and criteria for functional realism and conceptual plausibility of the alternatives they offer. I will then show that the statistical mechanics of phase transitions in brain activity, and some of its models, provides a new and possibly revealing perspective on brain events in cognition

Building expert systems: cognitive emulation.

Chapter 1 briefly introduces the concept of cognitive emulation, and outlines its current status. Chapter 2 reviews psychological research on human expert thinking. First, the study of expert thinking is placed in the context of modern cognitive psychology. Next, the principal methods and techniques employed by psychologists examining expert cognition are examined. The remainder of the chapter is given over to a review of the published literature on the nature and development of human expertise. Chapter 3 reviews the main arguments for and against cognitive emulation in expert system design. The tentative conclusion reached is that a significant degree of emulation is inevitable, but that a pure, unselective strategy of emulation is neither realistic nor desirable. Chapter 4 examines the prospects for cognitive emulation from a more pragmatic angle. Several factors are identified that represent constraints on the usefulness of a cognitive approach. However, a second set of factors is identified which should facilitate an emulation strategy - especially in the longer term. Some guidance is given on when to seriously consider adopting an emulation strategy. Chapter 5 presents a critical survey of expert system research that has already addressed the emulation issue. Six basic approaches to cognitive emulation are distinguished and evaluated. This helps draw out in more detail the implications of an emulation strategy for knowledge acquisition, knowledge representation and system architecture. The chapter concludes by discussing the issues that arise when different approaches to emulation are combined. Some guidance is offered on how this might be achieved. Chapter 6 summarizes the main themes and issues to have emerged, the design advice contained in the thesis, and the original contributions made by the thesis

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

Reliability and Validity of the GWalk for Use in Postural Control

Introduction: Clinical examinations are highly subjective when compared to the more sensitive and robust measures observed with force platform assessment. Currently, few methods exist to quantify objective postural control deficits in an easier and more accessible way for clinicians. Purpose: The purpose of this study was to examine the reliability and validity of a wireless inertial sensing device, the BTS GWalk, during postural control assessment. Methods: Fifty-six participants (27 male, 22 ± 1.9 years, 29 female, 21 ± 0.9 years) performed three trials each of quiet standing with eyes open (EO) and eyes closed (EC) on a force platform (FP). Participants were fitted with the BTS GWalk, which was placed on the lower back. To establish reliability, trials were administered over two time points approximately 48-72 hours apart. Raw center of pressure (COP) data from the FP and GWalk were exported and further analyzed using Excursion (ExcML/ExcAP) in the mediolateral and anteroposterior directions. Reliability of both devices was determined using a repeated measures ANOVA and corresponding ICC values. Criterion validity was determined using Pearson’s correlations in SPSS v 23.0 Results: Repeated measures ANOVAs showed no significance for time or device. In the EO condition, the GWalk demonstrated excellent reliability in the ExcML (ICC=.929) and ExcAP (ICC=.791) directions. In the EC condition, the GWalk showed excellent reliability in ExcML and AP (ICC=.909, .781). However, the repeated measures ANOVA showed significant differences for device (p ML (EO r= .703, EC r= .703), ExcAP (EO r= .732, EC r= .736). Discussion: Results of the current study indicate the GWalk is a reliable and moderately valid measurement of postural control in healthy populations, but currently is not recommended for comparison against COP parameters. Further research should examine the use of the GWalk against a measure of center of mass, to potentially provide an objective postural control assessment in clinical settings

Biomarkers of Rehabilitation Therapy Vary According To Stroke Severity

Biomarkers that capture treatment effects could improve the precision of clinical decision making for restorative therapies. We examined the performance of candidate structural, functional,and angiogenesis-related MRI biomarkers before and after a 3-week course of standardized robotic therapy in 18 patients with chronic stroke and hypothesized that results vary significantly according to stroke severity. Patients were 4.1 ± 1 months poststroke, with baseline arm Fugl-Meyer scores of 20–60. When all patients were examined together, no imaging measure changed over time in a manner that correlated with treatment-induced motor gains. However, when also considering the interaction with baseline motor status, treatment-induced motor gains were significantly related to change in three functional connectivity measures: ipsilesional motor cortex connectivity with (1) contralesional motor cortex (p = 0 003), (2) contralesional dorsal premotor cortex (p = 0 005), and (3) ipsilesional dorsal premotor cortex (p = 0 004). In more impaired patients, larger treatment gains were associated with greater increases in functional connectivity, whereas in less impaired patients larger treatment gains were associated with greater decreases in functional connectivity. Functional connectivity measures performed best as biomarkers of treatment effects after stroke. The relationship between changes in functional connectivity and treatment gains varied according to baseline stroke severity. Biomarkers of restorative therapy effects are not one-size-fits-all after stroke