Adaptive training adjusts a training task with the goal of improving learning outcomes. Adaptive training has been shown to improve human performance in attention, working memory capacity, and motor control tasks. Additionally, correlations have been observed between neural EEG spectral features (4–13 Hz) and the performance of some cognitive tasks. This relationship suggests some EEG features may be useful in adaptive training regimens. Here, we anticipated that adding a neural measure into a behavioral-based adaptive training system would improve (...) human performance on a subsequent transfer task. We designed, developed, and conducted a between-subjects study of 44 participants comparing three training regimens: Single Item Fixed Difficulty (SIFD), Behaviorally Adaptive Training (BAT), and Combined Adaptive Training (CAT) using both behavioral and EEG measures. Results showed a statistically significant transfer task performance advantage of the CAT-based system relative to SIFD and BAT systems of 6 and 9 percentage points, respectively. Our research shows a promising pathway for designing closed-loop BCI systems based on both users' behavioral performance and neural signals for augmenting human performance. (shrink)

As a type of evidence glass can be very useful contact trace material in a wide range of offences including burglaries and robberies, hit-and-run accidents, murders, assaults, ram-raids, criminal damage and thefts of and from motor vehicles. All of that offer the potential for glass fragments to be transferred from anything made of glass which breaks, to whoever or whatever was responsible. Variation in manufacture of glass allows considerable discrimination even with tiny fragments. In this study, we worked glass classification (...) and testing of artificial neural network model created by the JustNN. The aim of the study is help investigator in identifying the type of glass found in arena of the crime. The Neural Network model was trained and validated using the type of glass dataset. The accuracy of model in predicting the type of glass reached 96.7%. Thus neural network is suitable for predicating type of glasses. (shrink)

Augmented reality-based brain–computer interface (AR–BCI) has a low signal-to-noise ratio (SNR) and high real-time requirements. Classical machine learning algorithms that improve the recognition accuracy through multiple averaging significantly affect the information transfer rate (ITR) of the AR–SSVEP system. In this study, a fast recognition method based on a separable convolutional neural network (SepCNN) was developed for an AR-based P300 component (AR–P300). SepCNN achieved single extraction of AR–P300 features and improved the recognition speed. A nine-target AR–P300 single-stimulus paradigm was (...) designed to be administered with AR holographic glasses to verify the effectiveness of SepCNN. Compared with four classical algorithms, SepCNN significantly improved the average target recognition accuracy (81.1%) and information transmission rate (57.90 bits/min) of AR–P300 single extraction. SepCNN with single extraction also attained better results than classical algorithms with multiple averaging. (shrink)

The ongoing coronavirus 2019 pandemic caused by the severe acute respiratory syndrome coronavirus 2 has resulted in a severe ramification on the global healthcare system, principally because of its easy transmission and the extended period of the virus survival on contaminated surfaces. With the advances in computer-aided diagnosis and artificial intelligence, this paper presents the application of deep learning and adversarial network for the automatic identification of COVID-19 pneumonia in computed tomography scans of the lungs. The complexity and time limitation (...) of the reverse transcription-polymerase chain reaction swab test makes it disadvantageous to depend solely on as COVID-19’s central diagnostic mechanism. Since CT imaging systems are of low cost and widely available, we demonstrate that the drawback of the RT-PCR can be alleviated with a faster, automated, and reduced contact diagnostic process via the use of a neural network model for the classification of infected and noninfected CT scans. In our proposed model, we explore the benefit of transfer learning as a means of resolving the problem of inadequate dataset and the importance of semisupervised generative adversarial network for the extraction of well-mapped features and generation of image data. Our experimental evaluation indicates that the proposed semisupervised model achieves reliable classification, taking advantage of the reflective loss distance between the real data sample space and the generated data. (shrink)

Emotion recognition plays an important part in human-computer interaction. Currently, the main challenge in electroencephalogram -based emotion recognition is the non-stationarity of EEG signals, which causes performance of the trained model decreasing over time. In this paper, we propose a two-level domain adaptation neural network to construct a transfer model for EEG-based emotion recognition. Specifically, deep features from the topological graph, which preserve topological information from EEG signals, are extracted using a deep neural network. These features are (...) then passed through TDANN for two-level domain confusion. The first level uses the maximum mean discrepancy to reduce the distribution discrepancy of deep features between source domain and target domain, and the second uses the domain adversarial neural network to force the deep features closer to their corresponding class centers. We evaluated the domain-transfer performance of the model on both our self-built data set and the public data set SEED. In the cross-day transfer experiment, the ability to accurately discriminate joy from other emotions was high: sadness, anger, and fear on the self-built data set. The accuracy reached 74.93% on the SEED data set. In the cross-subject transfer experiment, the ability to accurately discriminate joy from other emotions was equally high: sadness, anger, and fear on the self-built data set. The average accuracy reached 87.9% on the SEED data set, which was higher than WGAN-DA. The experimental results demonstrate that the proposed TDANN can effectively handle the domain transfer problem in EEG-based emotion recognition. (shrink)

Convolutional neural networks, which automatically learn features from raw data to approximate functions, are being increasingly applied to the end-to-end analysis of electroencephalographic signals, especially for decoding brain states in brain-computer interfaces. Nevertheless, CNNs introduce a large number of trainable parameters, may require long training times, and lack in interpretability of learned features. The aim of this study is to propose a CNN design for P300 decoding with emphasis on its lightweight design while guaranteeing high performance, on the effects (...) of different training strategies, and on the use of post-hoc techniques to explain network decisions. The proposed design, named MS-EEGNet, learned temporal features in two different timescales in an efficient and optimized way, and was validated on three P300 datasets. The CNN was trained using different strategies and was compared with several state-of-the-art algorithms. Furthermore, variants of the baseline MS-EEGNet were analyzed to evaluate the impact of different hyper-parameters on performance. Lastly, saliency maps were used to derive representations of the relevant spatio-temporal features that drove CNN decisions. MS-EEGNet was the lightest CNN compared with the tested SOA CNNs, despite its multiple timescales, and significantly outperformed the SOA algorithms. Post-hoc hyper-parameter analysis confirmed the benefits of the innovative aspects of MS-EEGNet. Furthermore, MS-EEGNet did benefit from transfer learning, especially using a low number of training examples, suggesting that the proposed approach could be used in BCIs to accurately decode the P300 event while reducing calibration times. Representations derived from the saliency maps matched the P300 spatio-temporal distribution, further validating the proposed decoding approach. This study, by specifically addressing the aspects of lightweight design, transfer learning, and interpretability, can contribute to advance the development of deep learning algorithms for P300-based BCIs. (shrink)

Ancient beliefs such as astral projection, human possession, abduction and other similar are not only universal, taught by all religions, but also used as premises for core believes/expectations, such as after-life, eternal damnation, reincarnation, and many others. Transferring Consciousness to a Synthetic Body is also a feature of interest in our actual knowledge, both religious as for science. If immortality were an option, would you take it into consideration more seriously? Most people would probably dismiss the question since immortality isn’t (...) a real deal to contract. But what if having eternal life was a possibility in today’s world? The possibility of the transfer of human consciousness to a synthetic body can soon become a reality, and it could help the world for the better. Thus, until recently, the subject was mostly proposed by religion(s) and saw as a spiritual [thus, not ‘materially real’ or ‘forthwith accomplishable’] proposal therefore not really fully engaged or trust if not a religious believer. Now, technology is evolving, and so are we. The world has come to a point where artificial intelligence is breaking the boundaries of our perception of human consciousness and intelligence. And with this so is our understanding about the ancient question ‘who are we?’ concerning consciousness and how this human feature sticks to our body or it can become an entity beyond the material flesh. Without being exhaustive with the theme's development [leaving enough room for further investigations], we would like to take it for a spin and see how and where the religious and neuroscience realms intersect with it for a global, perhaps holistic understanding. Developments in neurotechnology favor the brain to broaden its physical control further the restraints of the human body. Accordingly, it is achievable to both acquire and provide information from and to the brain and also to organize feedback processes in which a person's thoughts can influence the activity of a computer or reversely. (shrink)

Drowsy driving is a major cause of automobile accidents. Previous studies used neuroimaging based approaches such as analysis of electroencephalogram (EEG) activities to understand the brain dynamics of different cortical regions during drowsy driving. However, the coupling between brain regions responding to this vigilance change is still unclear. To have a comprehensive understanding of neural mechanisms underlying drowsy driving, in this study we use transfer entropy, a model-free measure of effective connectivity based on information theory. We investigate the (...) pattern of information transfer between brain regions when the vigilance level, which is derived from the driving performance, changes from alertness to drowsiness. Results show that the couplings between pairs of frontal, central, and parietal areas increased at the intermediate level of vigilance, which suggests that an enhancement of the cortico-cortical interaction is necessary to maintain the task performance and prevent behavioral lapses. Additionally, the occipital-related connectivity magnitudes monotonically decreases as the vigilance level declines, which further supports the cortical gating of sensory stimuli during drowsiness. Neurophysiological evidence of mutual relationships between brain regions measured by Transfer entropy might elucidate the understanding of cortico-cortical communication during drowsy driving. (shrink)

New possibilities to modify function and direct repair in the central nervous system (CNS) have been established by the merger of gene transfer technology with neural transplantation. Rapid advances in viral‐mediated DNA‐delivery procedures permit the study of novel gene expression in neurons and glial cells. Foreign genes, transferred by a virus vector, can be used to generate new cell lines, identify transplanted cells, and express growth factors or enzymes for neurotransmitter synthesis. In addition to CNS cell types, non‐ (...) class='Hi'>neural cells are also being studied with transgene technology in the nervous system. Functional effects have been obtained with grafts of genetically modified cells in animal models of several nervous system disorders, and the recent results set the stage for potential application of these techniques to human CNS gene therapy. (shrink)

In timing perception studies, the timing of one event is usually manipulated relative to another, and participants are asked to judge if the two events were synchronous, or to judge which of the two events occurred first. Responses are analyzed to determine a measure of central tendency, which is taken as an estimate of the timing at which the two events are perceptually synchronous. When these estimates do not coincide with physical synchrony, it is often assumed that the sensory signals (...) are asynchronous, as though the transfer of information concerning one input has been accelerated or decelerated relative to the other. Here we show that, while this is a viable interpretation, it is equally plausible that such effects are driven by shifts in the criteria used to differentiate simultaneous from asynchronous inputs. Our analyses expose important ambiguities concerning the interpretation of simultaneity judgement data, which have hitherto been underappreciated. (shrink)

Information and communication technologies , with their increasing and widespread utilization in daily life, may exert an important impact on brain performances. The development of their use for improving several cerebral processes, by abolishing the brain/machine interface, is envisaged and is subject to debate. The scientific research on brain implants and brain gene transfer aiming to restore central nervous system functions, altered by disease or trauma, may contribute to this debate. Indeed, the advances that are enabling non drug-mediated approaches (...) for direct interventions on the brain are raising the possibility not only of restoring neural functions for therapeutic purposes, but also of monitoring, controlling and ultimately modifying from the outside these functions for enhancement. This paper gives an overview of the state of the art of brain implants and brain gene transfer for neurological diseases with a particular emphasis on Parkinson’s disease and a forecast on their possible application for other conditions. In doing so, the authors, who are neuroscientists working in the field of gene transfer for neuropsychiatric diseases, aim to establish a scientifically, medically and technically realistic framework to open up discussion on relevant ethical issues in this domain. (shrink)

Prism Adaptation is a useful method to study the mechanisms of sensorimotor adaptation. After-effects following adaptation to the prismatic deviation constitute the probe that adaptive mechanisms occurred, and current evidence suggests an involvement of the cerebellum at this level. Whether after-effects are transferable to another task is of great interest both for understanding the nature of sensorimotor transformations and for clinical purposes. However, the processes of transfer and their underlying neural substrates remain poorly understood. Transfer from throwing (...) to pointing is known to occur only in individuals who had previously reached a good level of expertise in throwing, not in novices. The aim of this study was to ascertain whether anodal stimulation of the cerebellum could boost after-effects transfer from throwing to pointing in novice participants. Healthy participants received anodal or sham transcranial direction current stimulation of the right cerebellum during a PA procedure involving a throwing task and were tested for transfer on a pointing task. Terminal errors and kinematic parameters were in the dependent variables for statistical analyses. Results showed that active stimulation had no significant beneficial effects on error reduction or throwing after-effects. Moreover, the overall magnitude of transfer to pointing did not change. Interestingly, we found a significant effect of the stimulation on the longitudinal evolution of pointing errors and on pointing kinematic parameters during transfer assessment. These results provide new insights on the implication of the cerebellum in transfer and on the possibility to use anodal tDCS to enhance cerebellar contribution during PA in further investigations. From a network approach, we suggest that cerebellum is part of a more complex circuitry responsible for the development of transfer which is likely embracing the primary motor cortex due to its role in motor memories consolidation. This paves the way for further work entailing multiple-sites stimulation to explore the role of M1-cerebellum dynamic interplay in transfer. (shrink)

Does motor behavior early in development have the same signatures of habituation, dishabituation, and Spencer-Thompson dishabituation known from infant perception and cognition? And do these signatures explain the choice preferences in A not B motor decision tasks? We provide new empirical evidence that gives an affirmative answer to the first question together with a unified neural dynamic model that gives an affirmative answer to the second question.In the perceptual and cognitive domains, habituation is the weakening of an orientation response (...) to a stimulus over perceptual experience. Switching to a novel stimulus leads to dishabituation, the re-establishment of the orientation response. In Spencer-Thompson dishabituation, the renewed orientation response transfers to the original stimulus. The change in orientation responses over perceptual experience explains infants' behavior in preferential looking tasks: Familiarity preference early during exposure and novelty preference late during exposure. In the motor domain, perseveration in the A not B task could be interpreted as a form of familiarity preference. There are hints that this preference reverses after enough experience with the familiar movement. We provide a unified account for habituation and patterns of preferential selection in which neural dynamic fields generate perceptual or motor representations. The build-up of activation in excitatory fields leads to familiarity preference, the build-up of activation in inhibitory fields leads to novelty preference. We show that the model accounts for the new experimental evidence for motor habituation, but is also compatible with earlier accounts for perceptual habituation and motor perseveration. We discuss how excitatory and inhibitory memory traces may regulate exploration and exploitation for both orientation to objects and motor behaviors. (shrink)

This paper concentrates on the event-triggered H ∞ filter design for the discrete-time Markovian jump neural networks under random missing measurements and cyber attacks. Considering that the controlled system and the filtering can exchange information over a shared communication network which is vulnerable to the cyber attacks and has limited bandwidth, the event-triggered mechanism is proposed to relieve the communication burden of data transmission. A variable conforming to Bernoulli distribution is exploited to describe the stochastic phenomenon since the missing (...) measurements occur with random probability. Furthermore, seeing that the communication networks are vulnerable to external malicious attacks, the transferred information via the shared communication network may be changed by the injected false information from the attackers. Based on the above consideration, sufficient conditions for the filtering error system to maintain asymptotically stable are provided with predefined H ∞ performance. In the end, three numerical examples are given to verify the proposed theoretical results. (shrink)

University spin-outs, creating businesses from university intellectual property, are a relatively common phenomena. As a knowledge transfer channel, the spin-out business model is attracting extensive attention. In this paper, the impacts of six equities on the acquisition of USOs, including founders, university, banks, business angels, venture capitals, and other equity, are comprehensively analyzed based on theoretical and empirical studies. Firstly, the average distribution of spin-out equity at formation is calculated based on the sample data of 350 UK USOs. According (...) to this distribution, a radial basis function neural network model is employed to forecast the effects of each equity on the acquisition. To improve the classification accuracy, the novel set-membership method is adopted in the training process of the RBF NN. Furthermore, a simulation test is carried out to measure the effects of six equities on the acquisition of USOs. The simulation results show that the increase of university’s equity has a negative effect on the acquisition of USOs, whereas the increase of remaining five equities has positive effects. Finally, three suggestions are provided to promote the development and growth of USOs. (shrink)

In this paper, based on computer reading and processing of music frequency, amplitude, timbre, image pixel, color filling, and so forth, a method of image style transfer guided by music feature data is implemented in real-time playback, using existing music files and image files, processing and trying to reconstruct the fluent relationship between the two in terms of auditory and visual, generating dynamic, musical sound visualization with real-time changes in the visualization. Although recommendation systems have been well developed in (...) real applications, the limitations of CF algorithms are slowly coming to light as the number of people increases day by day, such as the data sparsity problem caused by the scarcity of rated items, the cold start problem caused by new items and new users. The work is dynamic, with real-time changes in music and sound. Taking portraits as an experimental case, but allowing users to customize the input of both music and image files, this new visualization can provide users with a personalized service of mass customization and generate personalized portraits according to personal preferences. At the same time, we take advantage of the BP neural network’s ability to handle complex nonlinear problems and construct a rating prediction model between the user and item attribute features, referred to as the PSO-BP rating prediction model, by combining the features of global optimization of particle swarm optimization algorithm, and make further improvements based on the traditional collaborative filtering algorithm. (shrink)

Neural network models have recently made significant achievements in solving vehicle scheduling problems. Adaptive ant colony algorithm provides a new idea for neural networks to solve complex system problems of multiconstrained network intensive vehicle routing models. The pheromone in the path is changed by adjusting the volatile factors in the operation process adaptively. It effectively overcomes the tendency of the traditional ant colony algorithm to fall easily into the local optimal solution and slow convergence speed to search for (...) the global optimal solution. The multiconstrained network intensive vehicle routing algorithm based on adaptive ant colony algorithm in this paper refers to the interaction between groups. Adaptive transfer and pheromone update strategies are introduced based on the traditional ant colony algorithm to optimize the selection, update, and coordination mechanisms of the algorithm further. Thus, the search task of the objective function for a feasible solution is completed by the search ants. Through the division and collaboration of different kinds of ants, pheromone adaptive strategy is combined with polymorphic ant colony algorithm. It can effectively overcome some disadvantages, such as premature stagnation, and has a theoretical significance to the study of large-scale multiconstrained vehicle routing problems in complex traffic network systems. (shrink)

Deep learning techniques, which use a massive technology known as convolutional neural networks, have shown excellent results in a variety of areas, including image processing and interpretation. However, as the depth of these networks grows, so does the demand for a large amount of labeled data required to train these networks. In particular, the medical field suffers from a lack of images because the procedure for obtaining labeled medical images in the healthcare field is difficult, expensive, and requires specialized (...) expertise to add labels to images. Moreover, the process may be prone to errors and time-consuming. Current research has revealed transfer learning as a viable solution to this problem. Transfer learning allows us to transfer knowledge gained from a previous process to improve and tackle a new problem. This study aims to conduct a comprehensive survey of recent studies that dealt with solving this problem and the most important metrics used to evaluate these methods. In addition, this study identifies problems in transfer learning techniques and highlights the problems of the medical dataset and potential problems that can be addressed in future research. According to our review, many researchers use pre-trained models on the Imagenet dataset in many applications such as skin cancer, breast cancer, and diabetic retinopathy classification tasks. These techniques require further investigation of these models, due to training them on natural, non-medical images. In addition, many researchers use data augmentation techniques to expand their dataset and avoid overfitting. However, not enough studies have shown the effect of performance with or without data augmentation. Accuracy, recall, precision, F1 score, receiver operator characteristic curve, and area under the curve were the most widely used measures in these studies. Furthermore, we identified problems in the datasets for melanoma and breast cancer and suggested corresponding solutions. (shrink)

The growth of strawberry will be stressed by biological or abiotic factors, which will cause a great threat to the yield and quality of strawberry, in which various strawberry diseased. However, the traditional identification methods have high misjudgment rate and poor real-time performance. In today's era of increasing demand for strawberry yield and quality, it is obvious that the traditional strawberry disease identification methods mainly rely on personal experience and naked eye observation and cannot meet the needs of people for (...) strawberry disease identification and control. Therefore, it is necessary to find a more effective method to identify strawberry diseases efficiently and provide corresponding disease description and control methods. In this paper, based on the deep convolution neural network technology, the recognition of strawberry common diseases was studied, as well as a new method based on deep convolution neural network strawberry disease recognition algorithm, through the normal training of strawberry image feature representation in different scenes, and then through the application of transfer learning method, the strawberry disease image features are added to the training set, and finally the features are classified and recognized to achieve the goal of disease recognition. Moreover, attention mechanism and central damage function are introduced into the classical convolutional neural network to solve the problem that the information loss of key feature areas in the existing classification methods of convolutional neural network affects the classification effect, and further improves the accuracy of convolutional neural network in image classification. (shrink)

A major problem in developing an effective gene therapy for the nervous system lies in understanding the principles that maintain or turn off the expression of genes following their transfer into the CNS.

Changing and often class-dependent non-stationarities of signals are a big challenge in the transfer of common findings in cognitive workload estimation using Electroencephalography (EEG) from laboratory experiments to realistic scenarios or other experiments. Additionally, it often remains an open question whether actual cognitive workload reflected by brain signals was the main contribution to the estimation or discriminative and class-dependent muscle and eye activity, which can be secondary effects of changing workload levels. Within this study, we investigated a novel approach (...) to spatial filtering based on beamforming adapted to changing settings. We compare it to no spatial filtering and Common Spatial Patterns (CSP). We used a realistic maneuvering task, as well as an auditoryn-backsecondary task on a tugboat simulator as two different conditions to induce workload changes on professional tugboat captains. Apart from the typical within condition classification, we investigated the ability of the different classification methods to transfer between then-backcondition and the maneuvering task. The results show a clear advantage of the proposed approach over the others in the challenging transfer setting. While no filtering leads to lowest within-condition normalized classification loss on average in two scenarios (22 and 10%), our approach using adaptive beamforming (30 and 18%) performs comparably to CSP (33 and 15%). Importantly, in the transfer from one to another setting, no filtering and CSP lead to performance around chance level (45 to 53%), while our approach in contrast is the only one capable of classifying in all other scenarios (34 and 35%) with a significant difference from chance level. The changing signal composition over the scenarios leads to a need to adapt the spatial filtering in order to be transferable. With our approach, the transfer is successful due to filtering being optimized for the extraction of neural components and additional investigation of their scalp patterns revealed mainly neural origin. Interesting findings are that rather the patterns slightly change between conditions. We conclude that the approaches with low normalized loss depend on eye and muscle activity which is successful for classification within conditions, but fail in the classifier transfer since eye and muscle contributions are highly condition-specific. (shrink)

The goal of philosophy of information is to understand what information is, how it operates, and how to put it to work. But unlike ‘information’ in the technical sense of information theory, what we are interested in is meaningful information. To understand the nature and dynamics of information in this sense we have to understand meaning. What we offer here are simple computational models that show emergence of meaning and information transfer in randomized arrays of neural nets. These (...) we take to be formal instantiations of a tradition of theories of meaning as use. What they offer, we propose, is a glimpse into the origin and dynamics of at least simple forms of meaning and information transfer as properties inherent in behavioral coordination across a community. (shrink)

Chaos in nervous system is a fascinating but controversial field of investigation. To approach the role of chaos in the real brain, we theoretically and numerically investigate the occurrence of chaos inartificial neural networks. Most of the time, recurrent networks (with feedbacks) are fully connected. This architecture being not biologically plausible, the occurrence of chaos is studied here for a randomly diluted architecture. By normalizing the variance of synaptic weights, we produce a bifurcation parameter, dependent on this variance and (...) on the slope of the transfer function, that allows a sustained activity and the occurrence of chaos when reaching a critical value. Even for weak connectivity and small size, we find numerical results in accordance with the theoretical ones previously established for fully connected infinite sized networks. The route towards chaos is numerically checked to be a quasi-periodic one, whatever the type of the first bifurcation is. Our results suggest that such high-dimensional networks behave like low-dimensional dynamical systems. (shrink)

The goal of philosophy of information is to understand what information is, how it operates, and how to put it to work. But unlike ‘information’ in the technical sense of information theory, what we are interested in is meaningful information. To understand the nature and dynamics of information in this sense we have to understand meaning. What we offer here are simple computational models that show emergence of meaning and information transfer in randomized arrays of neural nets. These (...) we take to be formal instantiations of a tradition of theories of meaning as use. What they offer, we propose, is a glimpse into the origin and dynamics of at least simple forms of meaning and information transfer as properties inherent in behavioral coordination across a community. (shrink)

Without a proper restriction on mappings, virtually any system could be seen as implementing any computation. That would not allow characterization of systems in terms of implemented computations and is not compatible with a computationalist philosophy of mind. Information-based criteria for independence of substates within structured states are proposed as a solution. Objections to the use of requirements for transitions in counterfactual states are addressed, in part using the partial-brain argument as a general counterargument to neural replacement arguments.

Giulio Tononi's integrated information theory (IIT) proposes explaining consciousness by directly identifying it with integrated information. We examine the construct validity of IIT's measure of consciousness,phi(Φ), by analyzing its formal properties, its relation to key aspects of consciousness, and its co-variation with relevant empirical circumstances. Our analysis shows that IIT's identification of consciousness with the causal efficacy with which differentiated networks accomplish global information transfer (which is what Φ in fact measures) is mistaken. This misidentification has the consequence of (...) requiring the attribution of consciousness to a range of natural systems and artifacts that include, but are not limited to, large-scale electrical power grids, gene-regulation networks, some electronic circuit boards, and social networks. Instead of treating this consequence of the theory as a disconfirmation, IIT embraces it. By regarding these systems as bearers of consciousnessex hypothesi, IIT is led toward the orbit of panpsychist ideation. This departure from science as we know it can be avoided by recognizing the functional misattribution at the heart of IIT's identity claim. We show, for example, what function is actually performed, at least in the human case, by the cortical combination of differentiation with integration that IIT identifies with consciousness. Finally, we examine what lessons may be drawn from IIT's failure to provide a credible account of consciousness for progress in the very active field of research concerned with exploring the phenomenon from formal and neural points of view. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Emergent Spacetime, the Megastructure Problem, and the Metaphysics of the SelfSusan Schneider (bio)The aim of this article is to introduce new thoughts on some pressing topics relating to my book, Artificial You, ranging from the fundamental nature of reality to quantum theory and emergence in large language models (LLM) like GPT-4. Since Artificial You was published, the innovations in the domain of AI chatbots like GPT-4 have been rapid-fire, (...) and at times dizzying. The thoughtful discussion in this symposium offers an important opportunity for cross-cultural engagement on these issues and more. I am grateful for the opportunity to learn from the insights of these authors and editors. [End Page 314]The order of my comments will be as follows: in the context of considering Christian Coseru's intriguing response (section 1), I begin by discussing the nature of the self, which I use as a springboard into larger, vexing issues about the nature of fundamental reality, given work on emergent spacetime (ES) (section 2). I offer a "Prototime Interpretation" of quantum mechanics in which spacetime emerges from a prototemporal arena, and use this idea to explore the nature of the self and mind.Using the assumption of ES, I devise an approach to metaphysically fundamental objects (which I call "quantum objects") based on quantum entanglement and the bundle theory of substance. I illustrate how the self or mind, insofar as it is a substance that supervenes on quantum objects, could be partly or fully determined by the properties of the quantum objects on which it supervenes. Intriguingly, this would mean that the self or mind is not located in any singular point in space at a given time. I also explain how the Prototime Interpretation of quantum mechanics is compatible with change and causation at the fundamental level.In section 3 I turn to Inês Hipólito's interesting article, discussing the import of not underestimating AI autonomy in light of the increasingly sophisticated large-language models (LLMs).1 I raise the "AI Megastructure Problem," a new problem of AI control arising in the context of the incipient AGI systems that are now populating the AI ecosystem. The problem, which arose from a collaboration with Kyle Kilian, considers the possibility of emergent properties from the interaction of the LLMs and other AI's in the AI ecosystem (Schneider and Kilian 2023). Then, turning to Noam Cook's intriguing discussion of Japanese philosophy, I consider the pressing issue of testing for machine consciousness, devising a modified version of my AI consciousness test (ACT), originally developed with astrophysicist Edwin Turner. I add an "interpretability component" to the ACT to deal with the case of LLMs (section 4).I. Patternism and the Biological Process ViewChristian Coseru's intriguing piece focuses on the chapters on personal identity in Artificial You, proposing that when it comes to the nature of the self, one should adopt "a biologically informed process-ontology perspective on personal identity," which "means recognizing that organic identity is different in kind: it is an identity of form rather than of matter."His position is akin to the patternist views that I argue against in Artificial You in the sense that it embraces the idea that form, rather than the particular matter that comprises one's brain and body, is key to one's survival over time. But there are important differences as well. For transhumanists tend to believe that the self can survive radical changes in one's substrate, transitioning from thinking with a biological brain to computing with a network of microchips, for instance. Coseru departs from this, writing: [End Page 315]As organisms, persons endure by undergoing constant change on a fundamentally cellular level, including the change in the neural pathways that realize the cognitive processes of thinking and expressing these very thoughts right now.Furthermore:[I]f persons are processes rather than things, then they cannot be cognitively enhanced by augmenting, replacing or transferring their functioning parts (e.g., brain cells).I will call this position the "Biological Process View" (BPV). I have several questions about it. First, how does this position solve one of the key problems I raised for... (shrink)

Today, artificial intelligence is actively mastering natural languages, becoming an interlocutor and partner of human in various aspects of activity. However, the symbolic approach, which implies the transfer of rules and logic, has failed, the number of rules and exceptions of the language does not allow its formalization, so modern «deep learning» of artificial neural networks involves an independent search for patterns in extensive databases. During training, artificial intelligence puts a word into a sentence so that the syntagmatic (...) relationships are as close as possible to those of the target word in the base, taking into account both the semantic relationships of words and the relationships between words in the sequence of presentation. The «language» of information technologies is digital. During natural language processing, words are represented in vector form as a sequence of numbers. The idea of representing words mathematically is familiar to people and is usually associated with logical consistency. Visualization of the position of words in a multidimensional space created by artificial intelligence demonstrates a number of patterns, obvious semantic and syntactic relationships, but the essence of other relationships between words is not obvious. The mathematical representation of words, created by artificial intelligence, can allow you to look at the language from a new non-human point of view. (shrink)

People unfamiliar with Chinese characters show poorer visual working memory (VWM) performance for Chinese characters than do literates in Chinese. In a series of experiments, we investigated the reasons for this expertise advantage. Experiments 1 and 2 showed that the advantage of Chinese literates does not transfer to novel material. Experts had similar resolution as novices for material outside of their field of expertise, and the memory of novices and experts did not differ when detecting a big change, e.g., (...) when a character’s color was changed. Memorizing appears to function as a rather abstract representation of word forms because memory for characters’ fonts was poor independently of expertise (Experiment 3), though still visual. Distractors that were highly similar conceptually did not increase memory errors, but visually similar distractors impaired memory (Experiment 4). We hypothesized that literates in Chinese represent characters in VWM as tokens of visual word forms made available by long-term memory. In Experiment 5, we provided novices with visual word form knowledge. Participants subsequently performed a change detection task with trained and novel characters in a functional magnetic resonance experiment. We analyzed set size- and training-dependent effects in the intraparietal sulcus (IPS) and the visual word form area. VWM for trained characters was better than for novel characters. Neural activity increased with set size and at a slower rate for trained than for novel characters. All conditions approached the same maximum, but novel characters reached the maximum at a smaller set size than trained characters. The time course of the bold response depended on set size and knowledge status. Starting from the same initial maximum, neural activity at small set sizes returned to baseline more quickly for trained characters than for novel characters. Additionally, high performers showed generally more neural activity in the IPS than low performers. We conclude that experts’ better performance in working memory (WM) is caused by the availability of visual long-term representations (word form types) that allow a sparse representation of the perceived stimuli and make even small changes big because they cause a type change that is easily detected. (shrink)

A funny thing happened to Cartesian dualism on the way to the twenty-first century. After three hundred-odd years the irreconcilable dualism between `mind' and `matter' is still with us but, especially since the 1950s it has undergone a startling change. Matter has gotten fatter while mind is hard to find. I refer in particular to the domain of thought which has been transferred from res cogitans to res extensa in the guise of the computational brain. For Descartes, the body was (...) an automaton motivated by purely material forces to which the thinking mind was completely inessential . Now, however, the thinking mind has become the neural processor which is part of the same automaton. The only vestige of res cogitans which remains is sentience , i.e. conscious awareness. Thought is represented in sentience by what might be called `thought experience', i.e. the wispy but cognitively irrelevant stuff that takes place behind our foreheads. The rest of consciousness is made up of such things as sights, sounds, smells, tastes, elations, depressions, agonies and ecstasies -- our qualia. Some cognitive scientists deny we have qualia; they say we only seem to have them. But even those who admit we have them are increasingly inclined to follow their famous French predecessor and declare consciousness irrelevant to physical information processing. The latest doctrine of this persuasion is conscious inessentialism which is defined by Owen Flanagan as the view that `for any intelligent activity i, performed in any cognitive domain d, even if we do i with conscious accompaniments, i can in principle be done without these conscious accompaniments'. (shrink)

Language is the original technological prosthesis mediating all transfer of human cognition. The relationships between language, communication and cognition have long been the subject of scientific, philosophic and linguistic inquiry. However, it is through contemporary advancements in neuroscience that we now have unprecedented access to the inner workings of the human brain. Particularly, consumer grade neural scanning technologies like the Muse headset allow non-scientists to view, manipulate and draw conclusions from data generated by their own neural processes. (...) Hence, artists Minka Stoyanova and Lisa Park SoYoung have created an artistic experimental system that utilizes these technologies to address the possibility of communication beyond the confines of linguistic signification. Performing Hypo-Linguistics uses Muse brain sensors to create an abstract audio-visual communication system and feedback loop. Through the visualization and sonification of their respective brain data, these artists perform communication that is both outside of conscious control and beyond sign-based systems of linguistic representation. In this article, we discuss the philosophic and scientific groundwork that supports the creation of this system; our processes of sonification and visualization of the Muse-generated brain data (including the modes by which we actively avoided signification and expected technological aesthetics of data representation); and the outcomes of the performances or the realized interaction between the performers. As deep-learning algorithms become increasingly able to teach computers to mimic human language, it becomes ever more apparent that language is not, in fact, the purview of the human, but only one technological mode invented to facilitate the transfer of information. As with all systems of communication, noise is introduced in this process of transfer. This artistic research seeks to highlight that reality while considering the potentials for alternative forms of technologically mediated communication beyond linguistic signification and below the level of consciousness. (shrink)

Innate emotional bases of ethics have been proposed by authors in evolutionary psychology, following Darwin and his sources in eighteenth-century moral philosophy. Philosophers often tend to view such theories as irrelevant to, or even as tending to undermine, the project of moral philosophy. But the importance of emotions to early moral learning gives them a role to play in determining the content of morality. I argue, first, that research on neural circuits indicates that the basic elements or components of (...) emotions need not be limited to what psychologists think of as basic emotions. But in that case, innate mechanisms of social transfer of emotion, such as infants’ tendency to facial imitation, gaze-following, and emotional contagion or empathy, provide a source of plasticity in developing the basic elements that lets emotions incorporate cultural influence from early on. This leaves room later for cognitive components of adult human emotions and hence for the further role of language in conveying cultural influence. We can thus see how moral judgment might depend on innate emotional capacities that are both modifiable by culture and capable of registering objective values. I use Rawls’s treatment of the development of moral sentiments to illustrate the kind of supportive role that emotions can play in a principle-based account – though my own account involves modifications I go on to indicate. (shrink)

Brain states, which correlate with specific motor, cognitive, and emotional states, may be monitored with noninvasive techniques such as electroencephalography and magnetoencephalography that measure macroscopic cortical activity manifested as oscillatory network dynamics. These rhythmic cortical signatures provide insight into the neuronal activity used to identify pathological cortical function in numerous neurological and psychiatric conditions. Sensory and transcranial stimulation, entraining the brain with specific brain rhythms, can effectively induce desired brain states correlated with such cortical rhythms. Because brain states have distinct (...) neural correlates, it may be possible to induce a desired brain state by replicating these neural correlates through stimulation. To do so, we propose recording brain waves from a “donor” in a particular brain state using EEG/MEG to extract cortical signatures of the brain state. These cortical signatures would then be inverted and used to entrain the brain of a “recipient” via sensory or transcranial stimulation. We propose that brain states may thus be transferred between people by acquiring an associated cortical signature from a donor, which, following processing, may be applied to a recipient through sensory or transcranial stimulation. This technique may provide a novel and effective neuromodulation approach to the noninvasive, non-pharmacological treatment of a variety of psychiatric and neurological disorders for which current treatments are mostly limited to pharmacotherapeutic interventions. (shrink)

Extensive research demonstrated that firms may pursue several advantages in hiring individuals with the set of emotional, social, and cognitive (ESC) competencies that are most critical for business success. Therefore, the role of education for competency development is becoming paramount. Prior studies have questioned the traditional methods, grounded in the lecture format, as a way to effectively develop ESC competencies. Alternatively, they propose experiential learning techniques that involve participants in dedicated courses or activities. Despite the insights provided by these studies, (...) they do not take into account a comprehensive set of learning methods and their combined effect on the individual's competency portfolio within educational programs that aim to transfer primarily professional skills. Our study aims to fill these gaps by investigating the impact of the interplay between different learning methods on ESC competencies through a sample of students enrolled in the first year of a master's degree program. After providing a classification of three learning methods [traditional learning (TL), individual experiential learning (IEL), and social experiential learning (SEL)], the study delves into their combined influence on ESC competencies, adopting the Artificial Neural Network. Contrary to prior studies, our results provide counterintuitive evidence, suggesting that TL needs to be implemented together, on the one hand, with IEL to achieve a significant effect on emotional competencies and, on the other hand, with SEL to have an impact on social competencies. Moreover, IEL plays a prominent role in stimulating cognitive competencies. Our research contributes to educational literature by providing new insights on the effective combination of learning methods that can be adopted into programs that transfer technical knowledge and skills to promote behavioral competencies. (shrink)

: The transplantation of adult human neural stem cells into prenatal non-humans offers an avenue for studying human neural cell development without direct use of human embryos. However, such experiments raise significant ethical concerns about mixing human and nonhuman materials in ways that could result in the development of human-nonhuman chimeras. This paper examines four arguments against such research, the moral taboo, species integrity, "unnaturalness," and human dignity arguments, and finds the last plausible. It argues that the (...) class='Hi'>transfer of human brain or retinal stem cells to nonhuman embryos would not result in the development of human-nonhuman chimeras that denigrate human dignity, provided such stem cells are dissociated. The article provides guidelines that set ethical boundaries for conducting such research that are consonant with the requirements of human dignity. (shrink)

How to automatically detect intracranial aneurysms from Three-Dimension Time of Flight Magnetic Resonance Angiography images is a typical 3D image classification problem. Currently, the commonly used method is the Maximum Intensity Projection- based way. It transfers 3D classification into 2D case by projecting the 3D patch into 2D planes along different directions on the basis of voxel’s intensity. After then, the 2D Convolutional Neural Network is established to do classification. It has been shown that the MIP-based method can reduce (...) the demands for the samples and increase the computation efficiency. Meanwhile, the accuracy is comparable with that of 3D image classification. Inspired by the strategy of MIP, we want to further reduce the demands for samples and accelerate the training by transferring the 2D image classification into 1D case, i.e., we want to generate the 1D vectors from the MIP images and then establish a 1D CNN to do intracranial aneurysm detection and classification for 3D TOF MRA image. Specifically, our method first extracts a series of patches as the Region of Interests along the blood vessels from the original 3D TOF MRA 3D image. The corresponding MIP images of each ROI will be obtained through maximum intensity projecting. Then, we generate a series of 1D vectors by accumulating each MIP image along different directions. Meanwhile, a 1D CNN is established to detect aneurysms, in which, the input is the obtained 1D vectors and the output is the binary classification result denoting whether there are intracranial aneurysms in the considered patch. Generally, compared with 2D- and 3D-CNN, the 1D CNN-based way greatly accelerates the training and shows stronger robustness in the case of fewer samples. The efficiency of the proposed method outperforms the 2D CNN about 10 times in CPU training. Yet, their accuracies are close. (shrink)

Dementia, a prevalent disorder of the brain, has negative effects on individuals and society. This paper concerns using Spontaneous Speech Challenge of Interspeech 2020 to classify Alzheimer's dementia. We used VGGish, a deep, pretrained, Tensorflow model as an audio feature extractor, and Scikit-learn classifiers to detect signs of dementia in speech. Three classifiers were 59.1% accurate, which was 3% above the best-performing baseline models trained on the acoustic features used in the challenge. We also proposed DemCNN, a new PyTorch raw (...) waveform-based convolutional neural network model that was 63.6% accurate, 7% more accurate then the best-performing baseline linear discriminant analysis model. We discovered that audio transfer learning with a pretrained VGGish feature extractor performs better than the baseline approach using automatically extracted acoustic features. Our DepCNN exhibits good generalization capabilities. Both methods presented in this paper offer progress toward new, innovative, and more effective computer-based screening of dementia through spontaneous speech. (shrink)

The present paper addresses the formal perspective of information with the focus on ‘untrue’ information presented as dangerous. Grounded in perspectivism, the epistemic subject is understood as decisive in informational transfer. In this context, ethics should focus on how the epistemic subject receives information. Regarding wide-spread information, the notions of danger and safety, the latter being a reaction to the former, essentially result from the fear mechanism of affective neural systems in higher mammals. The practice of attaining safety (...) by eliminating danger is analyzed through the Nietzschean metaphor of the Apollonian and the Dionysian and the concept of ressentiment. Focusing on the individual, the paper presents a critique of systemized authoritative management of speech content, intensified by the emergence of social media. It is asserted that, by reacting to danger through the systematization of prevention and regulation of speech, it not only affirms fear but also decreases the faculties of an individual to deal with danger and fear itself. The paper argues that the only way to overcome danger and fear is through the individual’s exposure to it, which allows for the strengthening of one’s faculties, thus fulfilling one’s potential in freedom. (shrink)