Goering and colleagues (2017) acknowledge closed-loop neural devices have the potential to undermine agency. Indeed, the authors observe that “the agent using the device may . . . sometimes doubt whether she is the author of her action, given that the device may operate in ways that are not transparent to her” (65). Still, the authors ultimately argue that closed-loop neural devices may be construed as supporting agency, especially when we view agency from a relational perspective. The reason? (...) We often manifest our agency in relation with others. For example, family and close friends sometimes step in to support us in setting and achieving our aims. In a similar way, then, the authors argue that “we can imagine [a closed-loop] device stepping in to support the user in achieving her aim” (67). Closed-loop neural devices, in other words, can be construed as supporting one’s agency in much the same way as other people can be seen as supporting one’s agency. The authors thus conclude that when we view agents from a relational perspective, closed-loop neural devices may be viewed as supporting rather than undermining agency. I largely agree with the authors’ conclusion. I also believe, however, that their discussion can be clarified by distinguishing three kinds of agency and viewing closed-loop neural devices from the perspective of each. By thinking carefully about agency, we can see more clearly the ways in which it is undermined by closed-loop neural devices, as well as the way in which these devices may support agency. (shrink)

In this article, we explore how deep brain stimulation (DBS) devices designed to “close the loop”—to automatically adjust stimulation levels based on computational algorithms—may risk taking the individual agent “out of the loop” of control in areas where (at least apparent) conscious control is a hallmark of our agency. This is of particular concern in the area of psychiatric disorders, where closed-loop DBS is attracting increasing attention as a therapy. Using a relational model of identity and agency, we consider whether (...) DBS designed for psychiatric regulation may require special attention to agency. To do this, we draw on philosophical work on relational identity and agency, connecting it with reports from people using first-generation DBS devices for depression and obsessive-compulsive disorder. We suggest a way to extend a notion of relational agency to encompass neural devices. (shrink)

One of the most compelling questions still unanswered in neuroscience is how consciousness arises. In this article, we examine visual processing, the parietal lobe, and contralateral neglect syndrome as a window into consciousness and how the brain functions as the mind and we introduce a mechanism for the processing of visual information and its role in consciousness. We propose that consciousness arises from integration of information from throughout the body and brain by the thalamus and that the thalamus reimages visual (...) and other sensory information from throughout the cortex in a default three-dimensional space in the mind. We further suggest that the thalamus generates a dynamic default three-dimensional space by integrating processed information from corticothalamic feedback loops, creating an infrastructure that may form the basis of our consciousness. Further experimental evidence is needed to examine and support this hypothesis, the role of the thalamus, and to further elucidate the mechanism of consciousness. (shrink)

Pointing to similarities between challenges encountered in today's neural coding and twentieth-century behaviorism, we draw attention to lessons learned from resolving the latter. In particular, Perceptual Control Theory posits behavior as a closed-loop control process with immediate and teleological causes. With two examples, we illustrate how these ideas may also address challenges facing current neural coding paradigms.

This paper focuses on neural learning from adaptive neural control for a class of flexible joint manipulator under the output tracking constraint. To facilitate the design, a new transformed function is introduced to convert the constrained tracking error into unconstrained error variable. Then, a novel adaptive neural dynamic surface control scheme is proposed by combining the neural universal approximation. The proposed control scheme not only decreases the dimension of neural inputs but also reduces the number (...) of neural approximators. Moreover, it can be verified that all the closed-loop signals are uniformly ultimately bounded and the constrained tracking error converges to a small neighborhood around zero in a finite time. Particularly, the reduction of the number of neural input variables simplifies the verification of persistent excitation condition for neural networks. Subsequently, the proposed ANC scheme is verified recursively to be capable of acquiring and storing knowledge of unknown system dynamics in constant neural weights. By reusing the stored knowledge, a neural learning controller is developed for better control performance. Simulation results on a single-link flexible joint manipulator and experiment results on Baxter robot are given to illustrate the effectiveness of the proposed scheme. (shrink)

Neural tube defects are one of the commonest human birth defects, with more than 0.5% of some populations affected. Mouse models are being used in an attempt to identify genes that could be involved in these malformations. Only two mouse mutations are known to lead to craniorachischisis, failure of closure of almost the entire neural tube. Two recent papers report that the gene for one of these, Loop‐tail, has now been identified and sequenced.1, 2 It has been given (...) the designation Ltap/Lpp1 and appears to function in floor plate formation. It will be of great interest to investigate the role of this gene in human neural tube defects. BioEssays 24:580–583, 2002. © 2002 Wiley Periodicals, Inc. (shrink)

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 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, Behaviorally Adaptive Training, and Combined Adaptive Training 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)

Three apparently unrelated developmental processes – mammalian myogenesis, the choice of neural fate and sex determination in Drosophila – are controlled by a common mechanism. Most of the genes governing these processes encode transcriptional factors that contain the helix‐loop‐helix (HLH) motif. This domain mediates the formation of homo‐ or heterodimers that specifically bind to DNA through a conserved basic region adjacent to the HLH motif. Dimers differ in their affinity for DNA and in their ability to activate transcription from (...) HLH binding‐site containing promoters. In addition, the activity of HLH proteins is inhibited by dimerization with another class of HLH proteins that lack a basic domain entirely or have an altered one. These structural properties provide a molecular mechanism to explain the synergistic and antagonistic functional relations among the HLH encoding genes that control several developmental pathways. (shrink)

This paper proposes an adaptive neural network control approach for a direct-current system with full state constraints. To guarantee that state constraints always remain in the asymmetric time-varying constraint regions, the asymmetric time-varying Barrier Lyapunov Function is employed to structure an adaptive NN controller. As we all know that the constant constraint is only a special case of the time-varying constraint, hence, the proposed control method is more general for dealing with constraint problem as compared with the existing works (...) on DC systems. As far as we know, this system is the first studied situations with time-varying constraints. Using Lyapunov analysis, all signals in the closed-loop system are proved to be bounded and the constraints are not violated. In this paper, the effectiveness of the control method is demonstrated by simulation results. (shrink)

This paper presents an adaptive neural tracking control approach for a two-joint robotic manipulator with unknown time-varying delays. In order to work out the effect of unknown time-varying delays on the two-joint robotic manipulator, the appropriate Lyapunov–Krasovskii functionals and separation technology are chosen to settle this matter. The neural networks work as an approximator that has the advantage of estimating the unknown function in the system. In this paper, Lyapunov stability analysis can prove that all signals of the (...) closed-loop system are semiglobal uniformly ultimately bounded and the tracking error can converge to a compact neighborhood with respect to zero. The simulation consequences demonstrate the availability of the feedforward control approach. (shrink)

Neural organization: Structure, function, and dynamics shows how theory and experiment can supplement each other in an integrated, evolving account of the brain's structure, function, and dynamics. (1) Structure: Studies of brain function and dynamics build on and contribute to an understanding of many brain regions, the neural circuits that constitute them, and their spatial relations. We emphasize Szentágothai's modular architectonics principle, but also stress the importance of the microcomplexes of cerebellar circuitry and the lamellae of hippocampus. (2) (...) Function: Control of eye movements, reaching and grasping, cognitive maps, and the roles of vision receive a functional decomposition in terms of schemas. Hypotheses as to how each schema is implemented through the interaction of specific brain regions provide the basis for modeling the overall function by neural networks constrained by neural data. Synthetic PET integrates modeling of primate circuitry with data from human brain imaging. (3) Dynamics: Dynamic system theory analyzes spatiotemporal neural phenomena, such as oscillatory and chaotic activity in both single neurons and (often synchronized) neural networks, the self-organizing development and plasticity of ordered neural structures, and learning and memory phenomena associated with synaptic modification. Rhythm generation involves multiple levels of analysis, from intrinsic cellular processes to loops involving multiple brain regions. A variety of rhythms are related to memory functions. The Précis presents a multifaceted case study of the hippocampus. We conclude with the claim that language and other cognitive processes can be fruitfully studied within the framework of neural organization that the authors have charted with John Szentágothai. Key Words: cognitive maps; computational neuroscience; dynamics; hippocampus; memory; modular architectonics; neural modeling; neural organization; neural plasticity; rhythmogenesis; Szentágothai. (shrink)

In this study, an adaptive output feedback fault tolerant control scheme is proposed for a class of multi-input and multioutput nonlinear systems with multiple constraints. The neural network is adopted to handle the unknown nonlinearity by means of its superior approximation capability. Based on it, the state observer is designed to estimate the unmeasured states, and the nonlinear disturbance observer is constructed to tackle the external disturbances. In addition, the Nussbaum function is utilized to cope with the actuator faults, (...) which are coupled with the unknown control directions. Combining with the Lyapunov theory, a NN-based output feedback FTC law is developed for the MIMO nonlinear systems, and the boundedness of all closed-loop system error signals is proved. Simulation results on the unmanned helicopter are performed to demonstrate the effectiveness of the proposed controller. (shrink)

The computer distance teaching system teaches through the network, and there is no entrance threshold. Any student who is willing to study can log in to the network computer distance teaching system for study at any free time. Neural network has a strong self-learning ability and is an important part of artificial intelligence research. Based on this study, a neural network-embedded architecture based on shared memory and bus structure is proposed. By looking for an alternative method of exp (...) function to improve the speed of radial basis function algorithm, and then by analyzing the judgment conditions in the main loop during the algorithm process, these judgment conditions are modified conditionally to reduce the calculation scale, which can double the speed of the algorithm. Finally, this article verifies the function, performance, and interface of the computer distance education system. (shrink)

The ability to improve in speed and accuracy as a result of repeating some task is an important hallmark of intelligent biological systems. Although gradual behavioral improvements from practice have been modeled in spiking neural networks, few such models have attempted to explain cognitive development of a task as complex as addition. In this work, we model the progression from a counting-based strategy for addition to a recall-based strategy. The model consists of two networks working in parallel: a slower (...) basal ganglia loop and a faster cortical network. The slow network methodically computes the count from one digit given another, corresponding to the addition of two digits, whereas the fast network gradually “memorizes” the output from the slow network. The faster network eventually learns how to add the same digits that initially drove the behavior of the slower network. Performance of this model is demonstrated by simulating a fully spiking neural network that includes basal ganglia, thalamus, and various cortical areas. Consequently, the model incorporates various neuroanatomical data, in terms of brain areas used for calculation and makes psychologically testable predictions related to frequency of rehearsal. Furthermore, the model replicates developmental progression through addition strategies in terms of reaction times and accuracy, and naturally explains observed symptoms of dyscalculia. (shrink)

A dynamic learning method is developed for an uncertain n-link robot with unknown system dynamics, achieving predefined performance attributes on the link angular position and velocity tracking errors. For a known nonsingular initial robotic condition, performance functions and unconstrained transformation errors are employed to prevent the violation of the full-state tracking error constraints. By combining two independent Lyapunov functions and radial basis function neural network approximator, a novel and simple adaptive neural control scheme is proposed for the dynamics (...) of the unconstrained transformation errors, which guarantees uniformly ultimate boundedness of all the signals in the closed-loop system. In the steady-state control process, RBF NNs are verified to satisfy the partial persistent excitation condition. Subsequently, an appropriate state transformation is adopted to achieve the accurate convergence of neural weight estimates. The corresponding experienced knowledge on unknown robotic dynamics is stored in NNs with constant neural weight values. Using the stored knowledge, a static neural learning controller is developed to improve the full-state tracking performance. A comparative simulation study on a 2-link robot illustrates the effectiveness of the proposed scheme. (shrink)

Prism adaptation is a form of visuomotor training that produces both sensorimotor and cognitive aftereffects depending on the direction of the visual displacement. Recently, a neural framework explaining both types of PA-induced aftereffects has been proposed, but direct evidence for it is lacking. We employed Structural Equation Modeling, a form of effective connectivity analysis, to establish directionality among connected nodes of the brain network thought to subserve PA. The findings reveal two distinct network branches: a loop involving connections from (...) the parietal cortices to the right parahippocampal gyrus, and a branch linking the lateral premotor cortex to the parahippocampal gyrus via the cerebellum. Like the sensorimotor aftereffects, the first branch exhibited qualitatively different modulations for left versus right PA, and critically, changes in these connections were correlated with the magnitude of the sensorimotor aftereffects. Like the cognitive aftereffects, changes in the second branch were qualitatively similar for left and right PA, with greater change for left PA and a trend correlation with cognitive aftereffects. These results provide direct evidence that PA is supported by two functionally distinct subnetworks, a parietal–temporal network responsible for sensorimotor aftereffects and a fronto-cerebellar network responsible for cognitive aftereffects. (shrink)

Neural engineering technologies such as implanted deep brain stimulators and brain-computer interfaces represent exciting and potentially transformative tools for improving human health and well-being. Yet their current use and future prospects raise a variety of ethical and philosophical concerns. Devices that alter brain function invite us to think deeply about a range of ethical concerns—identity, normality, authority, responsibility, privacy, and justice. If a device is stimulating my brain while I decide upon an action, am I still the author of (...) the action? Does a device make the interiority of my experience accessible to others? Will the device change the way I think of myself and others think of me? Such fundamental questions arise even when a device is designed for only a relatively circumscribed purpose, such as restoring functioning via a smart prosthetic. We are part of a National Science Foundation-funded Engineering Research Center tasked with investigating philosophical and social implications of neural engineering research and technologies. Neural devices already in clinical use, such as deep brain stimulators for Parkinson's disease or essential tremor, have spurred healthy debate about such implications. Devices currently under development—such as the BrainGate System of implanted brain sensors coupled to robotics in persons with paralysis, exoskeletons for augmented movement, transcranial do-it-yourself stimulators, closed-loop brain stimulating systems, or even brain-to-brain interfacing—promise to extend and deepen these debates. At our center, brain-computer interfaces are the principal focus of work. Even acknowledging that the clinical translation of neural devices and seamless integration by end users may still largely reside in the future, the potential these devices hold calls for careful early analysis. The launching of the Brain Research through Advancing Innovative Neurotechnologies Initiative in April 2013 provides further impetus for this work. (shrink)

Estimation of discharge flowing through rivers is an important aspect of water resource planning and management. The most common way to address this concern is to develop stage-discharge relationships at various river sections. Various computational techniques have been applied to develop discharge ratings and improve the accuracy of estimated discharges. In this regard, the present study explores the application of the novel hybrid multigene genetic programming-generalized reduced gradient technique for estimating river discharges for steady as well as unsteady flows. It (...) also compares the MGGP-GRG performance with those of the commonly used optimization techniques. As a result, the rating curves of eight different rivers were developed using the conventional method, evolutionary algorithm, the modified honey bee mating optimization algorithm, artificial neural network, MGGP, and the hybrid MGGP-GRG technique. The comparison was conducted on the basis of several widely used performance evaluation criteria. It was observed that no model outperformed others for all datasets and metrics considered, which demonstrates that the best method may be different from one case to another one. Nevertheless, the ranking analysis indicates that the hybrid MGGP-GRG model overall performs the best in developing stage-discharge relationships for both single-value and loop rating curves. For instance, the hybrid MGGP-GRG technique improved sum of square of errors obtained by the conventional method between 4.5% and 99% for six out of eight datasets. Furthermore, EA, the MHBMO algorithm, and artificial intelligence models performed satisfactorily in some of the cases, while the idea of combining MGGP with GRG reveals that this hybrid method improved the performance of MGGP in this specific application. Unlike the black box nature of ANN, MGGP offers explicit equations for stream rating curves, which may be counted as one of the advantages of this AI model. (shrink)

Several theories of consciousness first described about a decade ago, including the conscious electromagnetic information (CEMI) field theory, claimed that the substrate of consciousness is the brain’s electromagnetic (EM) field. These theories were prompted by the observation, in many diverse systems, that synchronous neuronal firing, which generates coherent EM fields, was a strong correlate of attention, awareness, and consciousness. However, when these theories were first described there was no direct evidence that synchronous firing was actually functional, rather than an epiphenomenon (...) of brain function. Additionally, any EM field-based consciousness would be a ‘ghost in the machine’ unless the brain’s endogenous EM field is also able to influence neuron firing. Once again, when these theories were first described, there was only indirect evidence that the brain’s EM field influenced neuron firing patterns in the brain. In this paper I describe recent experimental evidence which demonstrate that synchronous neuronal firing does indeed have a functional role in the brain; and also that the brain’s endogenous EM field is involved in recruiting neurons to synchronously firing networks. The new data point to a new and unappreciated form of neural communication in the brain that is likely to have significance for all theories of consciousness. I describe an extension of the CEMI field theory that incorporates these recent experimental findings and integrates the theory with the ‘communication through coherence’ hypothesis. (shrink)

This article investigates an adaptive neural network control algorithm for marine surface vessels with time-varying output constraints and unknown external disturbances. The nonlinear state-dependent transformation is introduced to eliminate the feasibility conditions of virtual controller. Moreover, the barrier Lyapunov function is used to achieve time-varying output constraints. As an important approximation tool, the NN is employed to approximate uncertain and continuous functions. Subsequently, the disturbance observer is structured to observe time-varying constraints and unknown external disturbances. The novel strategy can (...) guarantee that all signals in the closed-loop system are semiglobally uniformly ultimately bounded. Finally, the simulation results verify the benefit of the proposed method. (shrink)

The aim of this paper and poster is to discuss some of the problems occurring in attempts to visualize neurobiological mechanisms. According to the thesis, neurobiological, static schemas should not only depict the order of connections among individual neural structures accordingly, but they have also the potential to present more detailed information about relations within mechanisms, such as patterns of dynamic interactions, their intensity, arrangement and the size of specific components, etc. First I will introduce the problems of visualization (...) in neuroscience, then I am going to discuss these problems with an example of the cortico-subcortical loops (C-SL) mechanism. Finally, I will show my solutions to some visualization problems and discuss the original schemas presented in the poster. (shrink)

Neural organization: Structure, function, and dynamics shows how theory and experiment can supplement each other in an integrated, evolving account of the brain's structure, function, and dynamics. Structure: Studies of brain function and dynamics build on and contribute to an understanding of many brain regions, the neural circuits that constitute them, and their spatial relations. We emphasize Szentágothai's modular architectonics principle, but also stress the importance of the microcomplexes of cerebellar circuitry and the lamellae of hippocampus. Function: Control (...) of eye movements, reaching and grasping, cognitive maps, and the roles of vision receive a functional decomposition in terms of schemas. Hypotheses as to how each schema is implemented through the interaction of specific brain regions provide the basis for modeling the overall function by neural networks constrained by neural data. Synthetic PET integrates modeling of primate circuitry with data from human brain imaging. Dynamics: Dynamic system theory analyzes spatiotemporal neural phenomena, such as oscillatory and chaotic activity in both single neurons and neural networks, the self-organizing development and plasticity of ordered neural structures, and learning and memory phenomena associated with synaptic modification. Rhythm generation involves multiple levels of analysis, from intrinsic cellular processes to loops involving multiple brain regions. A variety of rhythms are related to memory functions. The Précis presents a multifaceted case study of the hippocampus. We conclude with the claim that language and other cognitive processes can be fruitfully studied within the framework of neural organization that the authors have charted with John Szentágothai. (shrink)

Recording and manipulating neuronal ensemble activity is a key requirement in advanced neuromodulatory and behavior studies. Devices capable of both recording and manipulating neuronal activity brain-computer interfaces should ideally operate un-tethered and allow chronic longitudinal manipulations in the freely moving animal. In this study, we designed a new intracortical BCI feasible of telemetric recording and stimulating local gray and white matter of visual neural circuit after irradiation exposure. To increase the translational reliance, we put forward a Göttingen minipig model. (...) The animal was stereotactically irradiated at the level of the visual cortex upon defining the target by a fused cerebral MRI and CT scan. A fully implantable neural telemetry system consisting of a 64 channel intracortical multielectrode array, a telemetry capsule, and an inductive rechargeable battery was then implanted into the visual cortex to record and manipulate local field potentials, and multi-unit activity. We achieved a 3-month stability of the functionality of the un-tethered BCI in terms of telemetric radio-communication, inductive battery charging, and device biocompatibility for 3 months. Finally, we could reliably record the local signature of sub- and suprathreshold neuronal activity in the visual cortex with high bandwidth without complications. The ability to wireless induction charging combined with the entirely implantable design, the rather high recording bandwidth, and the ability to record and stimulate simultaneously put forward a wireless BCI capable of long-term un-tethered real-time communication for causal preclinical circuit-based closed-loop interventions. (shrink)

Helix–loop–helix (HLH) genes encode for transcription factors affecting a whole variety of developmental programs, including neurogenesis. At least seven functional classes (denoted I to VII) of HLH genes exist,1 with subclass members exhibiting homo‐ and heterodimerisation for proper DNA binding and transcriptional regulation of downstream target genes. In the developing nervous system, members of class II, V and VI have been most extensively studied concerning their roles in neural programming. In contrast, the function of class I proteins (such as (...) E12 and E47) is poorly defined and the orthodox view relegates them to general dimerisation duties that are necessary for the activity of the other classes. However, closer scrutiny of the spatiotemporal expression patterns of class I factors, combined with recent biochemical evidence, would suggest that class I proteins possess specific functions during early neural differentiation. This essay supports this possibility, in addition to putting forward the hypothesis that, outside their general dimerisation activity, class I genes have independent roles in regulating neurogenesis. BioEssays 25:709–716, 2003. © 2003 Wiley Periodicals, Inc. (shrink)

Increasing evidence suggests that it is not only the case that brain-based cognitive and emotional processes affect decision-making, but also that decision-making, actions and habits influence in turn the very structure and function of the brain by way of neural plasticity. This indicates that the interplay between brain and agency is made up of a complex feedback loop of reciprocal causality. The assumption that the causal relationship is one way –brain to behavior– results in unsatisfactory neuroscientific analyses of agency. (...) I contend that taking into account reciprocal causality for the purposes of human enhancement is not only a matter of theoretical consistency, but also carries with it important practical implications. Top-down cognitive enhancement –through mind training techniques such as meditation– may have advantages over biomedical enhancement. (shrink)

Theories of superior collicular and hippocampal function have remarkable similarities. Both structures have been repeatedly implicated in spatial and attentional behaviour and in inhibitory control of locomotion. Moreover, they share certain electrophysiological properties in their single unit responses and in the synchronous appearance and disappearance of slow wave activity. Both are phylogenetically old and the colliculus projects strongly to brainstem nuclei instrumental in the generation of theta rhythm in the hippocampal EECOn the other hand, close inspection of behavioural and electrophysiological (...) data reveals disparities. In particular, hippocampal processing mainly concerns stimulus ambiguity, contextual significance, and spatial relations or other subtle, higher order characteristics. This requires the use of largely preprocessed sensory information and mediation of poststimulus investigation. Although collicular activity must also be integrated with that of “higher” centres, its primary role in attention is more “peripheral” and specific in controlling orienting/localisation via eye and body movements toward egocentrically labelled spatial positions. In addition, the colliculus may exert a nonspecific influence in alerting higher centres to the imminence of information potentially worthy of focal attention. Nevertheless, it is noteworthy that collicular and hippocampal lesions produce deficits on similar tasks, although the type of deficit is usually different in each case. Functional overlap between hippocampus and colliculus is virtually certain vis-à-vis stimulus sampling, for example in the acquisition of information via vibrissal movements and visual scanning. In addition, insofar as stimulus significance is a factor in collicular orienting mechanisms, the hippocampus — cingulate – cortex — colliculus pathway may play a significant role, modulating collicular responsiveness and thus ensuring an attentional strategy appropriate to current requirements. A tentative “reciprocal loop” model is proposed which bridges physiological and behavioural levels of analysis and which would account for the observed degree and nature of functional overlap between the superior colliculus and hippocampus. (shrink)

I have argued elsewhere that imagery and represention are best explained as the result of operations of neurally implemented emulators of an agent's body and environment. In this article I extend the theory of emulation to address perceptual processing as well. The key notion will be that of an emulator of an agent's egocentric behavioral space. This emulator, when run off-line, produces mental imagery, including transformations such as visual image rotations. However, while on-line, it is used to process information from (...) sensory systems, resulting in perception (in this regard, the theory is similar to that proposed by Kosslyn (1994)). This emulator is what provides the theory in theory-laden perception. I close by arguing briefly that the spatial character of perception is to be explained as the contribution of the egocentric behavioral space emulator. (shrink)

The article deals with the algorithm of encrypting graphic information using transformation matrixes. It presents the actions that can be done with the image. The article also gives algorithms for forming matrixes that are created with the use of random processes. Examples of matrixes and encryption results are shown. Calculations of the analysis of combinations and conclusions to them are carried out. The article shows the possibilities and advantages of this image encryption algorithm. The proposed algorithm will allow to transmit (...) encrypted information through open communication channels. The algorithm uses all possible actions that can be done with the image, namely: image resizing, adding redundant elements to the image, changing the location of the pixels, changing the brightness of the image pixels, inverting the image pixels, changing the colours of the images pixels. A randomized approach to matrix formation will make it impossible to use neural systems. The algorithm of the implementation of writing encryption code is rather simple. It is not possible to decrypt the image without a key as the number of possible combinations is extremely large. This algorithm can be used in the military, intelligence, information security. Encryption does not require significant computing or RAM resources. This algorithm can be implemented in WEB-technology and in mobile applications, it has no recursive functions and loop in loop. The algorithm hides the image size, which in turn adds extra protection. Redundant elements will not allow neural networks to compare pixels. Changing colors, changing brightness, inversion by random processes will not allow neural networks to find the decryption function. The article considers an image encryption algorithm that will give a possibility to generate a key. The key will be a set of two-dimensional arrays formed in a randomized manner. A randomized approach to matrix formation will make it impossible to use neural systems. The algorithm uses all possible actions that can be done with the image, namely: Image resizing. Adding redundant elements to the image. Changing the location of the pixels. Changing the brightness of the image pixels. Inverting the image pixels. Changing the colours of the images pixels. The pixel positioning matrix gives us! combinations. The pixel brightness change matrix gives us 8 *! combinations. The pixel inversion matrix gives us 8 *! combinations. The pixel colour change matrix gives us 6 *! Combinations. The total number of combinations N is equal to: It is not possible to decrypt the image without a key as the number of possible combinations is extremely large. The algorithm of the implementation of writing encryption code is rather simple. This algorithm can be used in the military, intelligence, information security. The advantage of this algorithm is the fact that the encrypted image can be transmitted through open chann. (shrink)

In their paper, “Closed-Loop Neuromodulation and Self-Perception in Clinical Treatment of Refractory Epilepsy,” Haeusermann et al. (2023) note that although responsive neurostimulation (RNS) data v...

Literacy is an essential skill. Learning to read is a requirement for becoming a self-providing human being. However, while spoken language is acquired naturally with exposure to language without explicit instruction, reading and writing need to be taught explicitly. Decades of research have shown that well-structured teaching of phonological awareness, letter knowledge, and letter-to-sound mapping is crucial in building solid foundations for the acquisition of reading. During the COVID-19 pandemic, children worldwide did not have access to consistent and structured teaching (...) and are, as a consequence, predicted to be behind in the development of their reading skills. Subsequent evidence confirms this prediction. With the best evidence-based practice in mind, we developed an online version of a well-structured early literacy training program for 5-year-old children. This 2-week online Reading Camp program is designed for pre-K children. It incorporates critical components of the fundamental skills essential to learning to read and is taught online in an interactive, multi-sensory, and peer-learning environment. We measure the participants’ literacy skills and other related skills before and after participating in the online Reading Camp and compare the results to no-treatment controls. Results show that children who participated in the online Reading Camp improved significantly on all parameters in relation to controls. Our results demonstrate that a well-structured evidence-based reading instruction program, even if online and short-term, benefits 5-year-old children in learning to read. With the potential to scale up this online program, the evidence presented here, alongside previous evidence for the efficacy of the in-person program, indicates that the online Reading Camp program is effective and can be used to tackle a variety of questions regarding structural and functional plasticity in the early stages of reading acquisition. (shrink)

The human mind is essentially a metamind. Autonomy, knowledge, preference, acceptance, consciousness, and the content of thought all incorporate metamental ascent to a higher level beyond first level belief and desire. The primary function or role of metamental ascent is conflict resolution and higher order evaluation. An infinite regress of metamental ascent is avoided by a mental loop of keystone states which refer back to themselves yielding autonomy and knowledge without paradox. The metamental loop is, moreover, compatible with materialism, even (...) eliminative materiahsm. Vector activation in the brain averages neural impulses, and conflict resolution in the brain, like interpersonal conflict resolution, is obtained from weighted averaging. The resolution of the conflict is a fixed point vector of integrative weights which yields itself back forming a neural loop. Thus, neurophysiology recapitulates metamentality. (shrink)

The human mind is essentially a metamind. Autonomy, knowledge, preference, acceptance, consciousness, and the content of thought all incorporate metamental ascent to a higher level beyond first level belief and desire. The primary function or role of metamental ascent is conflict resolution and higher order evaluation. An infinite regress of metamental ascent is avoided by a mental loop of keystone states which refer back to themselves yielding autonomy and knowledge without paradox. The metamental loop is, moreover, compatible with materialism, even (...) eliminative materiahsm. Vector activation in the brain averages neural impulses, and conflict resolution in the brain, like interpersonal conflict resolution, is obtained from weighted averaging. The resolution of the conflict is a fixed point vector of integrative weights which yields itself back forming a neural loop. Thus, neurophysiology recapitulates metamentality. (shrink)

The Global Workspace Theory and Information Integration Theory are two of the most currently accepted consciousness models; however, these models do not address many aspects of conscious experience. We compare these models to our previously proposed consciousness model in which the thalamus fills-in processed sensory information from corticothalamic feedback loops within a proposed 3D default space, resulting in the recreation of the internal and external worlds within the mind. This 3D default space is composed of all cells of the (...) body, which communicate via gap junctions and electrical potentials to create this unified space. We use 3D llustrations to explain how both visual and non-visual sensory information may be filled-in within this dynamic space, creating a unified seamless conscious experience. This neural sensory memory space is likely generated by baseline neural oscillatory activity from the default mode network, other salient networks, brainstem, and reticular activating system. (shrink)

An increasing number of research teams are investigating the efficacy of brain-computer interface (BCI)-mediated interventions for promoting motor recovery following stroke. A growing body of evidence suggests that of the various BCI designs, most effective are those that deliver functional electrical stimulation (FES) of upper extremity (UE) muscles contingent on movement intent. More specifically, BCI-FES interventions utilize algorithms that isolate motor signals—user-generated intent-to-move neural activity recorded from cerebral cortical motor areas—to drive electrical stimulation of individual muscles or muscle synergies. (...) BCI-FES interventions aim to recover sensorimotor function of an impaired extremity by facilitating and/or inducing long-term motor learning-related neuroplastic changes in appropriate control circuitry. We developed a non-invasive, electroencephalogram (EEG)-based BCI-FES system that delivers closed-loop neural activity-triggered electrical stimulation of targeted distal muscles while providing the user with multimodal sensory feedback. This BCI-FES system consists of three components: (1) EEG acquisition and signal processing to extract real-time volitional and task-dependent neural command signals from cerebral cortical motor areas, (2) FES of muscles of the impaired hand contingent on the motor cortical neural command signals, and (3) multimodal sensory feedback associated with performance of the behavioral task, including visual information, linked activation of somatosensory afferents through intact sensorimotor circuits, and electro-tactile stimulation of the tongue. In this report, we describe device parameters and intervention protocols of our BCI-FES system which, combined with standard physical rehabilitation approaches, has proven efficacious in treating UE motor impairment in stroke survivors, regardless of level of impairment and chronicity. (shrink)

Studies have shown that both serotonin and glutamate receptor systems play a crucial role in the mechanisms underlying drug-induced synesthesia. The specific nature of these mechanisms, however, continues to remain elusive. Here we propose two distinct hypotheses for how synesthesia triggered by hallucinogens in the serotonin-agonist family may occur. One hypothesis is that the drug-induced destabilization of thalamic projections via GABAergic neuronal circuits from sensory areas leads to a disruption of low-level, spontaneous integration of multisensory stimuli. This sort of integration (...) regularly occurs when spatial and temporal attributes match. Destabilization of feedback loops, however, can result in incongruent experiences or binding of random thalamus activation with sensory input in a particular sensory modality. The second hypothesis builds on embodied cognition, cases in which visual images of external stimuli activate task-related neural regions. On this proposal, binding processes that do not normally generate awareness become accessible to consciousness as a result of decreased attentional discrimination among incoming stimuli. (shrink)

Feedback control in neural systems is ubiquitous. Here we study the mathematics of nonlinear feedback control. We compare models in which the input is multiplied by a dynamic gain (multiplicative control) with models in which the input is divided by a dynamic attenuation (divisive control). The gain signal (resp. the attenuation signal) is obtained through a concatenation of an instantaneous nonlinearity and a linear low-pass filter operating on the output of the feedback loop. For input steps, the dynamics of (...) gain and attenuation can be very different, depending on the mathematical form of the nonlinearity and the ordering of the nonlinearity and the filtering in the feedback loop. Further, the dynamics of feedback control can be strongly asymmetrical for increment versus decrement steps of the input. Nevertheless, for each of the models studied, the nonlinearity in the feedback loop can be chosen such that immediately after an input step, the dynamics of feedback control is symmetric with respect to increments versus decrements. Finally, we study the dynamics of the output of the control loops and find conditions under which overshoots and undershoots of the output relative to the steady-state output occur when the models are stimulated with low-pass filtered steps. For small steps at the input, overshoots and undershoots of the output do not occur when the filtering in the control path is faster than the low-pass filtering at the input. For large steps at the input, however, results depend on the model, and for some of the models, multiple overshoots and undershoots can occur even with a fast control path. (shrink)

We describe a neural monitor of environmental and physiological resources that informs effort expenditure. Depending on resources and environmental stability, serotonergic and dopaminergic neuromodulations favor different behavioral controls that are organized in corticostriatal loops. This broader perspective produces some suggestions and questions that may not be covered by the foraging approach to vigor of Shadmehr and Ahmed (2020).

In mental action there is no motor output to be controlled and no sensory input vector that could be manipulated by bodily movement. It is therefore unclear whether this specific target phenomenon can be accommodated under the predictive processing framework at all, or if the concept of “active inference” can be adapted to this highly relevant explanatory domain. This contribution puts the phenomenon of mental action into explicit focus by introducing a set of novel conceptual instruments and developing a first (...) positive model, concentrating on epistemic mental actions and epistemic self-control. Action initiation is a functionally adequate form of self-deception; mental actions are a specific form of predictive control of effective connectivity, accompanied and possibly even functionally mediated by a conscious “epistemic agent model”. The overall process is aimed at increasing the epistemic value of pre-existing states in the conscious self-model, without causally looping through sensory sheets or using the non-neural body as an instrument for active inference. (shrink)

The emulation theory of representation is developed and explored as a framework that can revealingly synthesize a wide variety of representational functions of the brain. The framework is based on constructs from control theory (forward models) and signal processing (Kalman filters). The idea is that in addition to simply engaging with the body and environment, the brain constructs neural circuits that act as models of the body and environment. During overt sensorimotor engagement, these models are driven by efference copies (...) in parallel with the body and environment, in order to provide expectations of the sensory feedback, and to enhance and process sensory information. These models can also be run off-line in order to produce imagery, estimate outcomes of different actions, and evaluate and develop motor plans. The framework is initially developed within the context of motor control, where it has been shown that inner models running in parallel with the body can reduce the effects of feedback delay problems. The same mechanisms can account for motor imagery as the off-line driving of the emulator via efference copies. The framework is extended to account for visual imagery as the off-line driving of an emulator of the motor-visual loop. I also show how such systems can provide for amodal spatial imagery. Perception, including visual perception, results from such models being used to form expectations of, and to interpret, sensory input. I close by briefly outlining other cognitive functions that might also be synthesized within this framework, including reasoning, theory of mind phenomena, and language. Key Words: efference copies; emulation theory of representation; forward models; Kalman filters; motor control; motor imagery; perception; visual imagery. (shrink)

We discuss cases where subjects seem to enjoy conscious experience when the relevant first-order perceptual representations are either missing or too weak to account for the experience. Though these cases are originally considered to be theoretical possibilities that may be problematical for the higher-order view of consciousness, careful considerations of actual empirical examples suggest that this strategy may backfire; these cases may cause more trouble for first-order theories instead. Specifically, these cases suggest that (I) recurrent feedback loops to V1 (...) are most likely not the neural correlate of first-order representations for conscious experience, (II) first-order views seem to have a problem accounting for the phenomenology in these cases, and either (III) a version of the ambitious higher-order approach is superior in that it is the simplest theory that can account for all results at face value, or (IV) a view where phenomenology is jointly determined by both first-order and higher-order states. In our view (III) and (IV) are both live options and the decision between them may ultimately be an empirical question that cannot yet be decided. (shrink)

Hofstadter [1979, 2007] offered a novel Gödelian proposal which purported to reconcile the apparently contradictory theses that (1) we can talk, in a non-trivial way, of mental causation being a real phenomenon and that (2) mental activity is ultimately grounded in low-level rule-governed neural processes. In this paper, we critically investigate Hofstadter’s analogical appeals to Gödel’s [1931] First Incompleteness Theorem, whose “diagonal” proof supposedly contains the key ideas required for understanding both consciousness and mental causation. We maintain that bringing (...) sophisticated results from Mathematical Logic into play cannot furnish insights which would otherwise be unavailable. Lastly, we conclude that there are simply too many weighty details left unfilled in Hofstadter’s proposal. These really need to be fleshed out before we can even hope to say that our understanding of classical mind-body problems has been advanced through metamathematical parallels with Gödel’s work. (shrink)

An action-oriented perspective changes the role of an individual from a passive observer to an actively engaged agent interacting in a closed loop with the world as well as with others. Cognition exists to serve action within a landscape that contains both. This chapter surveys this landscape and addresses the status of the pragmatic turn. Its potential influence on science and the study of cognition are considered (including perception, social cognition, social interaction, sensorimotor entrainment, and language acquisition) and its impact (...) on how neuroscience is studied is also investigated (with the notion that brains do not passively build models, but instead support the guidance of action). A review of its implications in robotics and engineering includes a discussion of the application of enactive control principles to couple action and perception in robotics as well as the conceptualization of system design in a more holistic, less modular manner. Practical applications that can impact the human condition are reviewed (e.g., educational applications, treatment possibilities for developmental and psychopathological disorders, the development of neural prostheses). All of this foreshadows the potential societal implications of the pragmatic turn. The chapter concludes that an action-oriented approach emphasizes a continuum of interaction between technical aspects of cognitive systems and robotics, biology, psychology, the social sciences, and the humanities, where the individual is part of a grounded cultural system. (shrink)

The scientific investigation of consciousness generates new findings at a rapid pace. We argue that we need a novel theoretical framework, which we call the ALARM theory of consciousness, in order to account for all central observations. According to this theory, we need to distinguish two levels of consciousness, namely basic arousal and general alertness. Basic arousal functions as a specific alarm system, keeping a biological organism alive under sudden intense threats, and general alertness enables flexible learning and behavioural strategies. (...) This two-level theory of consciousness helps us to account for (i) recent discoveries of subcortical brain activities with a central role of thalamic processes, (ii) observations of differences in the behavioural repertoire of non-human animals indicating two types of conscious experiences. Furthermore, the framework enables us (iii) to unify the neural evidence for the relevance of subcortical processes, on the one hand, and of cortico-cortical loops, on the other, and finally (iv) to clarify the evolutionary and actual functional role of conscious experiences. (shrink)

Cognitive neuroscience has been driven by the idea that by reductionist analysis of mechanisms within a solitary brain one can best understand how the human mind is constituted and what its nature is. The brain thus came to appear as the creator of the mind and the experienced world. In contrast, the paper argues for an ecological view of mind and brain as both being embedded in the relation of the living organism and its environment. This approach is crucially dependent (...) on a developmental perspective: the brain is conceived as a plastic system of open loops that are formed in the process of life and closed to full functional cycles in every interaction with the environment. Each time a new disposition of coherent neural activity is formed through repeated experience, structures of the mind are imprinted onto the brain. The brain becomes a mediating organ or a window to the mind, for it is structured by the mind itself. (shrink)

Advanced biological brains are by nature open-ended opportunistic controllers. Such controllers compute, pretty much on a moment-to-moment basis, what problem-solving resources are readily available and recruit them into temporary problem-solving wholes. Neural plasticity, exaggerated in our own species, makes it possible for such resources to become factored deep into both our cognitive and physical problem-solving routines. One way to think about this is to depict the biological brain as a master of what I shall dub ‘ecological control’. Ecological control (...) is the kind of top-level control that does not micro-manage every detail, but rather encourages substantial devolvement of power and responsibility. This kind of control allows much of our skill at walking to reside in the linkages and elastic properties of muscles and tendons. And it allows much of our prowess at thought and reason to depend upon the robust and reliable operation, often in dense brain-involving loops, of a variety of non-biological problem-solving resources spread throughout our social and technological surround. Are the complex distributed systems that result in some sense ‘out of control’, beyond the reach of useful governance? I shall argue that they are not, although understanding them requires us to re-think some key ideas about control and the nature of the self. To make this case, I shall first examine some strategies for efficient, external opportunity exploiting control in simple systems. I shall then argue that many of the same lessons apply to the case of higher-level human problem-solving. (shrink)

'Brainjacking’ refers to the exercise of unauthorized control of another’s electronic brain implant. Whilst the possibility of hacking a Brain–Computer Interface (BCI) has already been proven in both experimental and real-life settings, there is reason to believe that it will soon be possible to interfere with the software settings of the Implanted Pulse Generators (IPGs) that play a central role in Deep Brain Stimulation (DBS) systems. Whilst brainjacking raises ethical concerns pertaining to privacy and physical or psychological harm, we claim (...) that the possibility of brainjacking DBS raises particularly profound concerns about individual autonomy, since the possibility of hacking such devices raises the prospect of third parties exerting influence over the neural circuits underpinning the subject’s cognitive, emotional and motivational states. However, although it seems natural to assume that brainjacking represents a profound threat to individual autonomy, we suggest that the implications of brainjacking for individual autonomy are complicated by the fact that technologies targeted by brainjacking often serve to enhance certain aspects of the user’s autonomy. The difficulty of ascertaining the implications of brainjacking DBS for individual autonomy is exacerbated by the varied understandings of autonomy in the neuroethical and philosophical literature. In this paper, we seek to bring some conceptual clarity to this area by mapping out some of the prominent views concerning the different dimension of autonomous agency, and the implications of brainjacking DBS for each dimension. Drawing on three hypothetical case studies, we show that there could plausibly be some circumstances in which brainjacking could potentially be carried out in ways that could serve to enhance certain dimensions of the target’s autonomy. Our analysis raises further questions about the power, scope, and necessity of obtaining prior consent in seeking to protect patient autonomy when directly interfering with their neural states, in particular in the context of self-regulating closed-loop stimulation devices. (shrink)