2,644 research outputs found

    Human Conscious Experience is Four-Dimensional and has a Neural Correlate Modeled by Einstein's Special Theory of Relativity

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    In humans, knowing the world occurs through spatial-temporal experiences and interpretations. Conscious experience is the direct observation of conscious events. It makes up the content of consciousness. Conscious experience is organized in four dimensions. It is an orientation in space and time, an understanding of the position of the observer in space and time. A neural correlate for four-dimensional conscious experience has been found in the human brain which is modeled by Einstein’s Special Theory of Relativity. Spacetime intervals are fundamentally involved in the organization of coherent conscious experiences. They account for why conscious experience appears to us the way it does. They also account for assessment of causality and past-future relationships, the integration of higher cognitive functions, and the implementation of goal-directed behaviors. Spacetime intervals in effect compose and direct our conscious life. The relativistic concept closes the explanatory gap and solves the hard problem of consciousness (how something subjective like conscious experience can arise in something physical like the brain). There is a place in physics for consciousness. We describe all physical phenomena through conscious experience, whether they be described at the quantum level or classical level. Since spacetime intervals direct the formation of all conscious experiences and all physical phenomena are described through conscious experience, the equation formulating spacetime intervals contains the information from which all observable phenomena may be deduced. It might therefore be considered expression of a theory of everything

    Artificial Intelligence in the Context of Human Consciousness

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    Artificial intelligence (AI) can be defined as the ability of a machine to learn and make decisions based on acquired information. AI’s development has incited rampant public speculation regarding the singularity theory: a futuristic phase in which intelligent machines are capable of creating increasingly intelligent systems. Its implications, combined with the close relationship between humanity and their machines, make achieving understanding both natural and artificial intelligence imperative. Researchers are continuing to discover natural processes responsible for essential human skills like decision-making, understanding language, and performing multiple processes simultaneously. Artificial intelligence attempts to simulate these functions through techniques like artificial neural networks, Markov Decision Processes, Human Language Technology, and Multi-Agent Systems, which rely upon a combination of mathematical models and hardware

    Patterned Plantar Stimulation During Gait

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    It is well established that the soles of the feet are involved and aid in balance control. However, it is not well understood the exact role that the feet play in gait control. During walking, the center of pressure (CoP) takes a predictable and repeated path along the plantar surfaces, going from heel to toe. This CoP has been established to be vital for postural control during standing, the plantar surfaces may perform a similar role during walking by perceiving this CoP path. Most studies use vibro-tactile stimulation on the plantar surfaces during the entire gait cycle, including the swing phase. However, no studies have investigated the effects of different patterns of sequential stimulation on the plantar surfaces during the stance phase of gait. Therefore, the following chapters describe a method of testing this effect, and demonstrating how such patterned plantar stimulation alters gait in healthy young adults. This method of testing was developed such that plantar stimulation would activate specifically during the stance phase of the gait cycle, and activate in a gait-like or an abnormal sequence. We then hypothesized that stimulation in an abnormal sequence would result in gait and balance deficits when compared to stimulation that followed the natural sequence during walking. Additionally, that walking on an inclined surface would increase the effects of the tactile stimulation sequences on such measures when compared with no stimulation. We tested a total of nine healthy adults and found very minimal effects from the stimulation in any pattern. This demonstrates that healthy adults have the ability to adjust and reweigh sensory information from the plantar surfaces such that gait and balance outcomes show minimal or no deficits when foot-sole tactile sensory sequences are manipulated during slow walking. Additionally, that the perception of the CoP movement may be predominately supplied by slow adapting fibers that are not typically sensitive to vibrations. This work gives indication to the flexibility and adaptability of a healthy motor control system and demonstrates a method of testing such a system with an online stimulation control software

    A morphospace of functional configuration to assess configural breadth based on brain functional networks

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    The best approach to quantify human brain functional reconfigurations in response to varying cognitive demands remains an unresolved topic in network neuroscience. We propose that such functional reconfigurations may be categorized into three different types: i) Network Configural Breadth, ii) Task-to-Task transitional reconfiguration, and iii) Within-Task reconfiguration. In order to quantify these reconfigurations, we propose a mesoscopic framework focused on functional networks (FNs) or communities. To do so, we introduce a 2D network morphospace that relies on two novel mesoscopic metrics, Trapping Efficiency (TE) and Exit Entropy (EE), which capture topology and integration of information within and between a reference set of FNs. In this study, we use this framework to quantify the Network Configural Breadth across different tasks. We show that the metrics defining this morphospace can differentiate FNs, cognitive tasks and subjects. We also show that network configural breadth significantly predicts behavioral measures, such as episodic memory, verbal episodic memory, fluid intelligence and general intelligence. In essence, we put forth a framework to explore the cognitive space in a comprehensive manner, for each individual separately, and at different levels of granularity. This tool that can also quantify the FN reconfigurations that result from the brain switching between mental states.Comment: main article: 24 pages, 8 figures, 2 tables. supporting information: 11 pages, 5 figure

    Decoding cognition from spontaneous neural activity

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    In human neuroscience, studies of cognition are rarely grounded in non-task-evoked, ‘spontaneous’ neural activity. Indeed, studies of spontaneous activity tend to focus predominantly on intrinsic neural patterns (for example, resting-state networks). Taking a ‘representation-rich’ approach bridges the gap between cognition and resting-state communities: this approach relies on decoding task-related representations from spontaneous neural activity, allowing quantification of the representational content and rich dynamics of such activity. For example, if we know the neural representation of an episodic memory, we can decode its subsequent replay during rest. We argue that such an approach advances cognitive research beyond a focus on immediate task demand and provides insight into the functional relevance of the intrinsic neural pattern (for example, the default mode network). This in turn enables a greater integration between human and animal neuroscience, facilitating experimental testing of theoretical accounts of intrinsic activity, and opening new avenues of research in psychiatry

    Controllability of structural brain networks.

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    Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function

    Network constraints on learnability of probabilistic motor sequences

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    Human learners are adept at grasping the complex relationships underlying incoming sequential input. In the present work, we formalize complex relationships as graph structures derived from temporal associations in motor sequences. Next, we explore the extent to which learners are sensitive to key variations in the topological properties inherent to those graph structures. Participants performed a probabilistic motor sequence task in which the order of button presses was determined by the traversal of graphs with modular, lattice-like, or random organization. Graph nodes each represented a unique button press and edges represented a transition between button presses. Results indicate that learning, indexed here by participants' response times, was strongly mediated by the graph's meso-scale organization, with modular graphs being associated with shorter response times than random and lattice graphs. Moreover, variations in a node's number of connections (degree) and a node's role in mediating long-distance communication (betweenness centrality) impacted graph learning, even after accounting for level of practice on that node. These results demonstrate that the graph architecture underlying temporal sequences of stimuli fundamentally constrains learning, and moreover that tools from network science provide a valuable framework for assessing how learners encode complex, temporally structured information.Comment: 29 pages, 4 figure

    Automatic single-trial discrimination of mental arithmetic, mental singing and the no-control state from prefrontal activity: toward a three-state NIRS-BCI

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    <p>Abstract</p> <p>Background</p> <p>Near-infrared spectroscopy (NIRS) is an optical imaging technology that has recently been investigated for use in a safe, non-invasive brain-computer interface (BCI) for individuals with severe motor impairments. To date, most NIRS-BCI studies have attempted to discriminate two mental states (e.g., a mental task and rest), which could potentially lead to a two-choice BCI system. In this study, we attempted to automatically differentiate three mental states - specifically, intentional activity due to 1) a mental arithmetic (MA) task and 2) a mental singing (MS) task, and 3) an unconstrained, "no-control (NC)" state - to investigate the feasibility of a three-choice system-paced NIRS-BCI.</p> <p>Results</p> <p>Deploying a dual-wavelength frequency domain near-infrared spectrometer, we interrogated nine sites around the frontopolar locations while 7 able-bodied adults performed mental arithmetic and mental singing to answer multiple-choice questions within a system-paced paradigm. With a linear classifier trained on a ten-dimensional feature set, an overall classification accuracy of 56.2% was achieved for the MA vs. MS vs. NC classification problem and all individual participant accuracies significantly exceeded chance (i.e., 33%). However, as anticipated based on results of previous work, the three-class discrimination was unsuccessful for three participants due to the ineffectiveness of the mental singing task. Excluding these three participants increases the accuracy rate to 62.5%. Even without training, three of the remaining four participants achieved accuracies approaching 70%, the value often cited as being necessary for effective BCI communication.</p> <p>Conclusions</p> <p>These results are encouraging and demonstrate the potential of a three-state system-paced NIRS-BCI with two intentional control states corresponding to mental arithmetic and mental singing.</p

    Integrating Cortical Sensorimotor Representations Across Spatial Scales and Task Contexts

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    Our understanding of how brains function is stratified between two very different scales: mesoscale (what function a given cortical area performs), measured with tools like fMRI; and microscale (what a given neuron does), measured with implanted microelectrodes. While extensive research has been done to characterize brain activity at both of these spatial scales, describing relationships between these two domains has proven difficult. Identifying ways to integrate findings between these scales is valuable for both research and clinical applications, but is particularly important for intracortical brain-computer interfaces (BCIs), which aim to restore motor function after paralysis or amputation. In humans, the brain is much larger than the available microelectrode arrays, so determining where to place the arrays is a critical aspect of ensuring optimal performance. BCIs preferentially target primary motor and somatosensory cortices, due to their direct relationship to motor control and critical role in skilled and dexterous movements. However, despite these areas displaying a relatively ordered spatial organization, it is difficult to accurately predict the behavior of neurons recorded from a given area for several reasons. Mesoscale activity is overlapping, with activity relating to multiple different movements observed in a single area. Additionally, neurons have flexible behavior, displaying different “tuning” to similar behavior under different contexts. Here I present my research integrating neuroimaging-based cortical mapping with directly-recorded neural activity in human sensorimotor cortex. First, I examine how the large-scale organization of sensorimotor representations measured with fMRI is affected by contextual sensory information. I then examine how spatially separate neural populations recorded with intracortical microelectrode arrays encode different types of movement. Finally, I examine whether how population encoding changes to reflect contextual sensory information using the same task as in the fMRI study. Together, these results provide a foundation for reconciling neural activity across spatial scales and task contexts, and will inform the design and placement of more capable BCI systems
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