Quantum noise induced entanglement and chaos in the dissipative quantum model of brain

We discuss some features of the dissipative quantum model of brain in the frame of the formalism of quantum dissipation. Such a formalism is based on the doubling of the system degrees of freedom. We show that the doubled modes account for the quantum noise in the fluctuating random force in the system-environment coupling. Remarkably, such a noise manifests itself through the coherent structure of the system ground state. The entanglement of the system modes with the doubled modes is shown to be permanent in the infinite volume limit. In such a limit the trajectories in the memory space are classical chaotic trajectories.Comment: 14 page

Accuracy of Circulation Estimation Schemes Applied to Discretised Velocity Field Data

Numerical experiments are conducted on the velocity field of the Oseen vortex to determine the effect of random errors in the velocity field on the circulation estimate. The circulation is estimated by either a velocity integral or a vorticity integral over a particular region of integration. A novel method for the determination of this region is used. The accuracy of circulation estimation schemes is characterised in terms of the velocity sample spacing, the amount of random noise in the velocity field and the vorticity estimation scheme used. It is found that, in general, the velocity integral outperforms the vorticity integral in terms of reducing total error

Chaotic exploration and learning of locomotion behaviours

We present a general and fully dynamic neural system, which exploits intrinsic chaotic dynamics, for the real-time goal-directed exploration and learning of the possible locomotion patterns of an articulated robot of an arbitrary morphology in an unknown environment. The controller is modeled as a network of neural oscillators that are initially coupled only through physical embodiment, and goal-directed exploration of coordinated motor patterns is achieved by chaotic search using adaptive bifurcation. The phase space of the indirectly coupled neural-body-environment system contains multiple transient or permanent self-organized dynamics, each of which is a candidate for a locomotion behavior. The adaptive bifurcation enables the system orbit to wander through various phase-coordinated states, using its intrinsic chaotic dynamics as a driving force, and stabilizes on to one of the states matching the given goal criteria. In order to improve the sustainability of useful transient patterns, sensory homeostasis has been introduced, which results in an increased diversity of motor outputs, thus achieving multiscale exploration. A rhythmic pattern discovered by this process is memorized and sustained by changing the wiring between initially disconnected oscillators using an adaptive synchronization method. Our results show that the novel neurorobotic system is able to create and learn multiple locomotion behaviors for a wide range of body configurations and physical environments and can readapt in realtime after sustaining damage

Dissipation and spontaneous symmetry breaking in brain dynamics

We compare the predictions of the dissipative quantum model of brain with neurophysiological data collected from electroencephalograms resulting from high-density arrays fixed on the surfaces of primary sensory and limbic areas of trained rabbits and cats. Functional brain imaging in relation to behavior reveals the formation of coherent domains of synchronized neuronal oscillatory activity and phase transitions predicted by the dissipative model.Comment: Restyled, slight changes in title and abstract, updated bibliography, J. Phys. A: Math. Theor. Vol. 41 (2008) in prin

Morphological identification of Bighead Carp, Silver Carp, and Grass Carp eggs using random forests machine learning classification

Visual identification of fish eggs is difficult and unreliable due to a lack of information on the morphological egg characteristics of many species. We used random forests machine learning to predict the identity of genetically identified Bighead Carp Hypophthalmichthys nobilis, Grass Carp Ctenopharyngodon idella, and Silver Carp H. molitrix eggs based on egg morphometric and environmental characteristics. Family, genus, and species taxonomic-level random forests models were explored to assess the performance and accuracy of the predictor variables. The egg characteristics of Bighead Carp, Grass Carp, and Silver Carp were similar, and they were difficult to distinguish from one another. When combined into a single invasive carp class, the random forests models were ≥ 97% accurate at identifying invasive carp eggs, with a ≤5% false positive rate. Egg membrane diameter was the most important predictive variable, but the addition of ten other variables resulted in a 98% success rate for identifying invasive carp eggs from 26 other upper Mississippi River basin species. Our results revealed that a combination of morphometric and environmental measurements can be used to identify invasive carp eggs. Similar machine learning approaches could be used to identify the eggs of other fishes. These results will help managers more easily and quickly assess invasive carp reproduction

Ecological mechanisms in cognitive science

© The Author(s) 2019. In 2010, Bechtel and Abrahamsen defined and described what it means to be a dynamic causal mechanistic explanatory model. They discussed the development of a mechanistic explanation of circadian rhythms as an exemplar of the process and challenged cognitive science to follow this example. This article takes on that challenge. A mechanistic model is one that accurately represents the real parts and operations of the mechanism being studied. These real components must be identified by an empirical programme that decomposes the system at the correct scale and localises the components in space and time. Psychological behaviour emerges from the nature of our real-time interaction with our environments—here we show that the correct scale to guide decomposition is picked out by the ecological perceptual information that enables that interaction. As proof of concept, we show that a simple model of coordinated rhythmic movement, grounded in information, is a genuine dynamical mechanistic explanation of many key coordination phenomena

Virtual Partner Interaction (VPI): Exploring Novel Behaviors via Coordination Dynamics

Inspired by the dynamic clamp of cellular neuroscience, this paper introduces VPI—Virtual Partner Interaction—a coupled dynamical system for studying real time interaction between a human and a machine. In this proof of concept study, human subjects coordinate hand movements with a virtual partner, an avatar of a hand whose movements are driven by a computerized version of the Haken-Kelso-Bunz (HKB) equations that have been shown to govern basic forms of human coordination. As a surrogate system for human social coordination, VPI allows one to examine regions of the parameter space not typically explored during live interactions. A number of novel behaviors never previously observed are uncovered and accounted for. Having its basis in an empirically derived theory of human coordination, VPI offers a principled approach to human-machine interaction and opens up new ways to understand how humans interact with human-like machines including identification of underlying neural mechanisms

Comprehensive Survey of SNPs in the Affymetrix Exon Array Using the 1000 Genomes Dataset

Microarray gene expression data has been used in genome-wide association studies to allow researchers to study gene regulation as well as other complex phenotypes including disease risks and drug response. To reach scientifically sound conclusions from these studies, however, it is necessary to get reliable summarization of gene expression intensities. Among various factors that could affect expression profiling using a microarray platform, single nucleotide polymorphisms (SNPs) in target mRNA may lead to reduced signal intensity measurements and result in spurious results. The recently released 1000 Genomes Project dataset provides an opportunity to evaluate the distribution of both known and novel SNPs in the International HapMap Project lymphoblastoid cell lines (LCLs). We mapped the 1000 Genomes Project genotypic data to the Affymetrix GeneChip Human Exon 1.0ST array (exon array), which had been used in our previous studies and for which gene expression data had been made publicly available. We also evaluated the potential impact of these SNPs on the differentially spliced probesets we had identified previously. Though the 1000 Genomes Project data allowed a comprehensive survey of the SNPs in this particular array, the same approach can certainly be applied to other microarray platforms. Furthermore, we present a detailed catalogue of SNP-containing probesets (exon-level) and transcript clusters (gene-level), which can be considered in evaluating findings using the exon array as well as benefit the design of follow-up experiments and data re-analysis

Transfer of learning between unimanual and bimanual rhythmic movement coordination: transfer is a function of the task dynamic.

Under certain conditions, learning can transfer from a trained task to an untrained version of that same task. However, it is as yet unclear what those certain conditions are or why learning transfers when it does. Coordinated rhythmic movement is a valuable model system for investigating transfer because we have a model of the underlying task dynamic that includes perceptual coupling between the limbs being coordinated. The model predicts that (1) coordinated rhythmic movements, both bimanual and unimanual, are organised with respect to relative motion information for relative phase in the coupling function, (2) unimanual is less stable than bimanual coordination because the coupling is unidirectional rather than bidirectional, and (3) learning a new coordination is primarily about learning to perceive and use the relevant information which, with equal perceptual improvement due to training, yields equal transfer of learning from bimanual to unimanual coordination and vice versa [but, given prediction (2), the resulting performance is also conditioned by the intrinsic stability of each task]. In the present study, two groups were trained to produce 90° either unimanually or bimanually, respectively, and tested in respect to learning (namely improved performance in the trained 90° coordination task and improved visual discrimination of 90°) and transfer of learning (to the other, untrained 90° coordination task). Both groups improved in the task condition in which they were trained and in their ability to visually discriminate 90°, and this learning transferred to the untrained condition. When scaled by the relative intrinsic stability of each task, transfer levels were found to be equal. The results are discussed in the context of the perception–action approach to learning and performance

Mass spectrometry-directed structure elucidation and total synthesis of ultra-long chain (O-acyl)-ω-hydroxy fatty acids

The (O-acyl)-ω-hydroxy FAs (OAHFAs) comprise an unusual lipid subclass present in the skin, vernix caseosa, and meibomian gland secretions. Although they are structurally related to the general class of FA esters of hydroxy FAs (FAHFAs), the ultra-long chain (30-34 carbons) and the putative -substitution of the backbone hydroxy FA suggest that OAHFAs have unique biochemistry. Complete structural elucidation of OAHFAs has been challenging because of their low abundance within complex lipid matrices. Furthermore, because these compounds occur as a mixture of closely related isomers, insufficient spectroscopic data have been obtained to guide structure confirmation by total synthesis. Here, we describe the full molecular structure of ultra-long chain OAHFAs extracted from human meibum by exploiting the gas-phase purification of lipids through multistage MS and novel multidimensional ion activation methods. The analysis elucidated sites of unsaturation, the stereochemical configuration of carbon-carbon double bonds, and ester linkage regiochemistry. Such isomer-resolved MS guided the first total synthesis of an ultra-long chain OAHFA, which, in turn, confirmed the structure of the most abundant OAHFA found in human meibum, OAHFA 50:2. The availability of a synthetic OAHFA opens new territory for future investigations into the unique biophysical and biochemical properties of these lipids