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Time is essential for understanding the brain. A temporal theory for realizing major brain functions (e.g., sensation, cognition, motivation, attention, memory, learning, and motor action) is proposed that uses temporal codes, time-domain neural networks, correlation-based binding processes and signal dynamics. It adopts a signal-centric perspective in which neural assemblies produce circulating and propagating characteristic temporally patterned signals for each attribute (feature). Temporal precision is essential for temporal coding and processing. The characteristic spike patterns that constitute the signals enable general-purpose, multimodal, multidimensional vectorial representations of objects, events, situations, and procedures. Signals are broadcast and interact with each other in spreading activation time-delay networks to mutually reinforce, compete, and create new composite patterns. Sequences of events are directly encoded in the relative timings of event onsets. New temporal patterns are created through nonlinear multiplicative and thresholding signal interactions, such as mixing operations found in radio communications systems and wave interference patterns. The newly created patterns then become markers for bindings of specific combinations of signals and attributes (e.g., perceptual symbols, semantic pointers, and tags for cognitive nodes). Correlation operations enable both bottom-up productions of new composite signals and top-down recovery of constituent signals. Memory operates using the same principles: nonlocal, distributed, temporally coded memory traces, signal interactions and amplifications, and content-addressable access and retrieval. A short-term temporary store is based on circulating temporal spike patterns in reverberatory, spike-timing-facilitated circuits. A long-term store is based on synaptic modifications and neural resonances that select specific delay-paths to produce temporally patterned signals. Holographic principles of nonlocal representation, storage, and retrieval can be applied to temporal patterns as well as spatial patterns. These can automatically generate pattern recognition (wavefront reconstruction) capabilities, ranging from objects to concepts, for distributed associative memory applications. The evolution of proposed neural implementations of holograph-like signal processing and associative content-addressable memory mechanisms is discussed. These can be based on temporal correlations, convolutions, simple linear and nonlinear operations, wave interference patterns, and oscillatory interactions. The proposed mechanisms preserve high resolution temporal, phase, and amplitude information. These are essential for establishing high phase coherency and determining phase relationships, for binding/coupling, synchronization, and other operations. Interacting waves can sum constructively for amplification, or destructively, for suppression, or partially. Temporal precision, phase-locking, phase-dependent coding, phase-coherence, synchrony are discussed within the context of wave interference patterns and oscillatory interactions. Sequences of mixed neural oscillations are compared with a cascade of sequential mixing stages in a single-sideband carrier suppressed (SSBCS) radio communications system model. This mechanism suggests a manner by which multiple neural oscillation bands could interact to produce new emergent information-bearing oscillation bands, as well as to abolish previously generated bands. A hypothetical example illustrates how a succession of different oscillation carriers (gamma, beta, alpha, theta, and delta) could communicate and propagate (broadcast) information sequentially through a neural hierarchy of speech and language processing stages. Based on standard signal mixing principles, each stage emergently generates the next. The sequence of oscillatory bands generated in the mixing cascade model is consistent with neurophysiological observations. This sequence corresponds to stages of speech-language processing (sound/speech detection, acoustic-phonetics, phone/clusters, syllables, words/phrases, word sequences/sentences, and concepts/understanding). The oscillatory SSBCS cascade model makes specific predictions for oscillatory band frequencies that can be empirically tested. The principles postulated here may apply broadly for local and global oscillation interactions across the cortex. Sequences of oscillatory interactions can serve many functions, e.g., to regulate the flow and interaction of bottom-up, gamma-mediated and top-down, beta-mediated neural signals, to enable cross-frequency coupling. Some specific guidelines are offered as to how the general time-domain theory might be empirically tested. Neural signals need to be sampled and analyzed with high temporal resolution, without destructive windowing or filtering. Our intent is to suggest what we think is possible, and to widen both the scope of brain theory and experimental inquiry into brain mechanisms, functions, and behaviors.

Time is of the essence in how neural codes, synchronies, and oscillations might function in encoding, representation, transmission, integration, storage, and retrieval of information in brains. This Hypothesis and Theory article examines observed and possible relations between codes, synchronies, oscillations, and types of neural networks they require. Towards reverse-engineering informational functions in brains, prospective, alternative neural architectures incorporating principles from radio modulation and demodulation, active reverberant circuits, distributed content-addressable memory, signal-signal time-domain correlation and convolution operations, spike-correlation-based holography, and self-organizing, autoencoding anticipatory systems are outlined. Synchronies and oscillations are thought to subserve many possible functions: sensation, perception, action, cognition, motivation, affect, memory, attention, anticipation, and imagination. These include direct involvement in coding attributes of events and objects through phase-locking as well as characteristic patterns of spike latency and oscillatory response. They are thought to be involved in segmentation and binding, working memory, attention, gating and routing of signals, temporal reset mechanisms, inter-regional coordination, time discretization, time-warping transformations, and support for temporal wave-interference based operations. A high level, partial taxonomy of neural codes consists of channel, temporal pattern, and spike latency codes. The functional roles of synchronies and oscillations in prospective neural codes, including oscillatory phase-offset codes, are outlined. Various forms of multiplexing neural signals are considered: time-division, frequency-division, code-division, oscillatory-phase, synchronized channels, oscillatory hierarchies, polychronous ensembles. An expandable, annotative neural spike train framework for encoding low- and high-level attributes of events and objects is proposed. Coding schemes require appropriate neural architectures for their interpretation. Time-delay, oscillatory, wave-interference, synfire chain, polychronous, and neural timing networks are discussed. Some novel concepts for formulating an alternative, more time-centric theory of brain function are discussed. As in radio communication systems, brains can be regarded as networks of dynamic, adaptive transceivers that broadcast and selectively receive multiplexed temporally-patterned pulse signals. These signals enable complex signal interactions that select, reinforce, and bind common subpatterns and create emergent lower dimensional signals that propagate through spreading activation interference networks. If memory traces share the same kind of temporal pattern forms as do active neuronal representations, then distributed, holograph-like content-addressable memories are made possible via temporal pattern resonances.

Here we present evidence for the ubiquity of fine spike timing and temporal coding broadly observed across sensory systems and widely conserved across diverse phyla, spanning invertebrates and vertebrates. A taxonomy of basic neural coding types includes channel activation patterns, temporal patterns of spikes, and patterns of spike latencies. Various examples and types of combination temporal-channel codes are discussed, including firing sequence codes. Multiplexing of temporal codes and mixed channel-temporal codes are considered. Neurophysiological and perceptual evidence for temporal coding in many sensory modalities is surveyed: audition, mechanoreception, electroreception, vision, gustation, olfaction, cutaneous senses, proprioception, and the vestibular sense. Precise phase-locked, phase-triggered, and spike latency codes can be found in many sensory systems. Temporal resolutions on millisecond and submillisecond scales are common. General correlation-based representations and operations are discussed. In almost every modality, there is some role for temporal coding, often in surprising places, such as color vision and taste. More investigations into temporal coding are well-warranted.

The escalating epidemic of obesity has increased the incidence of obesity-induced complications to historically high levels. Adipose tissue is a dynamic energy depot, which stores energy and mobilizes it during nutrient deficiency. Excess nutrient intake resulting in adipose tissue expansion triggers lipid release and aberrant adipokine, cytokine and chemokine production, and signaling that ultimately lead to adipose tissue inflammation, a hallmark of obesity. This low-grade chronic inflammation is thought to link obesity to insulin resistance and the associated comorbidities of metabolic syndrome such as dyslipidemia and hypertension, which increase risk of type 2 diabetes and cardiovascular disease. In this review, we focus on and discuss members of the chemokine system for which there is clear evidence of participation in the development of obesity and obesity-induced pathologies.

Mastery modeling was implemented to support the transfer of classroom diabetes content to the clinical area. Interactive teaching methodologies that exemplified mastery modeling included scripted classroom video and vignettes, simulation-based learning experiences, and a worksheet relating to diabetes-specific groups to use in the clinical setting. When mastery modeling teaching methodologies were implemented, transfer of knowledge through clinical reasoning was noted at a higher level for the diabetes-specific groups over the standard groups.

Communication problems (eg, dysphonia, dysfluency and language and articulation disorders), swallowing disorders (dysphagia and globus), cough and upper airway symptoms, resulting from functional neurological disorder (FND), are commonly encountered by speech and language professionals. However, there are few descriptions in the literature of the most effective practical management approaches. This consensus document aims to provide recommendations for assessment and intervention that are relevant to both adults and young people. An international panel of speech and language professionals with expertise in FND were approached to take part. Participants responded individually by email to a set of key questions regarding best practice for assessment and interventions. Next, a video conference was held in which participants discussed and debated the answers to these key questions, aiming to achieve consensus on each issue. Drafts of the collated consensus recommendations were circulated until consensus was achieved. FND should be diagnosed on the basis of positive clinical features. Speech and language therapy for FND should address illness beliefs, self-directed attention and abnormal movement patterns through a process of education, symptomatic treatment and cognitive behavioural therapy within a supportive therapeutic environment. We provide specific examples of these strategies for different symptoms. Speech and language professionals have a key role in the management of people with communication and related symptoms of FND. It is intended that these expert recommendations serve as both a practical toolkit and a starting point for further research into evidence-based treatments.

Medical providers' unconscious biases may contribute to health disparities. Awareness and self-reflection strategies commonly used to teach cultural competence in academic settings are generally ineffective in reducing unconscious bias or motivating change. This article describes the innovative teaching strategies implemented in a graduate setting (N = 75) to increase nursing learners' acceptance and management of unconscious bias. Strategies used guided the debriefing and feedback that incorporated implicit association testing, interactive audience polling, categorized management strategies, and perspective taking. Strategies resulted in positive learner feedback, including a high likelihood to learn more about unconscious bias, acceptance of unconscious bias influence on health disparities, and importance of using management strategies to address personal bias. Increasingly diverse patient populations require nurses who have the skills to understand, assess, and correct unconscious biases. To accomplish this goal, consistent exposure to unconscious bias curricula that includes focused debriefing, feedback, and management strategies is needed at all levels of nursing education. [J Nurs Educ. 2017;56(11):692-696.].

Functional neurological disorder (FND) is a common and disabling disorder, often misunderstood by clinicians. Although viewed sceptically by some, FND is a diagnosis that can be made accurately, based on positive clinical signs, with clinical features that have remained stable for over 100 years. Despite some progress in the last decade, people with FND continue to suffer subtle and overt forms of discrimination by clinicians, researchers and the public. There is abundant evidence that disorders perceived as primarily affecting women are neglected in healthcare and medical research, and the course of FND mirrors this neglect. We outline the reasons why FND is a feminist issue, incorporating historical and contemporary clinical, research and social perspectives. We call for parity for FND in medical education, research and clinical service development so that people affected by FND can receive the care they need.

Mathematics anxiety is a widespread problem that inhibits learning, despite it being preventable and manageable. This thesis reports a study which developed an intervention to support learners directly in the recognition and management of their mathematics anxiety. The research was conducted in two phases. Firstly, the existence of a substantial level of mathematics anxiety was established in eleven-year-old pupils in a Midlands comprehensive school, through an adapted mathematics anxiety scale. Secondly, pupils from this group with the highest levels of mathematics anxiety were offered the opportunity to take part in an intervention, and 13 pupils agreed. Three Design-Based Research cycles generated 32 clinical interviews which were delivered, recorded and analysed. The thesis supports the literature in that mathematics anxiety is negatively correlated to mathematical achievement, and more common in female learners than male learners. Qualitative analysis of the intervention found it to be efficacious. The thesis develops the Growth Zone Model introduced by Johnston-Wilder and Lee (2010) through the application of psychological strategies to reduce general anxiety and introduces a dynamic view of the model that is based in learner autonomy. Implications for future research and practical applications in school settings are discussed.

Vocal interactions between humans and non-human animals are pervasive, but studies are often limited to communication within species. Here, we conducted a pilot exploration of vocal interactions between visitors to the San Diego Zoo Safari Park and Sampson, an 18-year-old male Hyacinth Macaw residing near the entrance. Over the course of one hour, 82 vocal and behavioral events were recorded, and various relationships between human and bird behavior were noted. Analyses of this type, applied to large datasets with assistance from artificial intelligence, could be used to better understand the impacts, positive or negative, of human visitors on animals in managed care.