Neuroadaptive modelling for generating images matching perceptual categories

Brain-computer interfaces enable active communication and execution of a pre-defined set of commands, such as typing a letter or moving a cursor. However, they have thus far not been able to infer more complex intentions or adapt more complex output based on brain signals. Here, we present neuroadaptive generative modelling, which uses a participant's brain signals as feedback to adapt a boundless generative model and generate new information matching the participant's intentions. We report an experiment validating the paradigm in generating images of human faces. In the experiment, participants were asked to specifically focus on perceptual categories, such as old or young people, while being presented with computer-generated, photorealistic faces with varying visual features. Their EEG signals associated with the images were then used as a feedback signal to update a model of the user's intentions, from which new images were generated using a generative adversarial network. A double-blind follow-up with the participant evaluating the output shows that neuroadaptive modelling can be utilised to produce images matching the perceptual category features. The approach demonstrates brain-based creative augmentation between computers and humans for producing new information matching the human operator's perceptual categories.Peer reviewe

The effect of Midazolam and Propranolol on fear memory reconsolidation in ethanol-withdrawn rats: Influence of D-cycloserine

Background: Withdrawal from chronic ethanol facilitates the formation of contextual fear memory and delays the onset to extinction, with its retrieval promoting an increase in ethanol consumption. Consequently, manipulations aimed to reduce these aversive memories, may be beneficial in the treatment of alcohol discontinuation symptoms. Related to this, pharmacological memory reconsolidation blockade has received greater attention due to its therapeutic potential. Methods: Here, we examined the effect of post-reactivation amnestic treatments such as Midazolam (MDZ, 3 mg/kg i.p) and Propranolol (PROP, 5 mg/kg i.p) on contextual fear memory reconsolidation in ethanol- withdrawn (ETOH) rats. Next, we examined whether the activation of N-methyl-D-aspartate (NMDA) receptors induced by d-cycloserine (DCS, 5 mg/kg i.p., a NMDA partial agonist) before memory reactivation can facilitate the disruptive effect of PROP and MDZ on fear memory in ETOH rats. Results: We observed a resistance to the disruptive effect of both MDZ and PROP following memory reactivation. Although intra-basolateral amygdala (BLA; 1.25 ug/side) and systemic PROP administration attenuated fear memory in DCS pre-treated ETOH rats, DCS/MDZ treatment did not affect memory in these animals. Finally, a decrease of both total and surface protein expression of the α1 GABAA receptor (GABAA-R) subunit in BLA was found in the ETOH rats. Conclusions: Ethanol withdrawal facilitated the formation of fear memory resistant to labilization post-reactivation. DCS administration promoted the disruptive effect of PROP on memory reconsolidation in ETOH rats. The resistance to MDZ's disruptive effect on fear memory reconsolidation may be, at least in part, associated with changes in the GABAA-R composition induced by chronic ethanol administration/withdrawal.Fil: Ortiz, Vanesa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Farmacología Experimental de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Farmacología Experimental de Córdoba; ArgentinaFil: Giachero, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Farmacología Experimental de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Farmacología Experimental de Córdoba; ArgentinaFil: Espejo, Pablo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Farmacología Experimental de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Farmacología Experimental de Córdoba; ArgentinaFil: Molina, Víctor Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Farmacología Experimental de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Farmacología Experimental de Córdoba; ArgentinaFil: Martijena, Irene Delia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Farmacología Experimental de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Farmacología Experimental de Córdoba; Argentin

Neuroadaptive LBS: towards human-, context-, and task-adaptive mobile geographic information displays to support spatial learning for pedestrian navigation

Well-designed, neuroadaptive mobile geographic information displays (namGIDs) could improve the lives of millions of mobile citizens of the mostly urban information society who daily need to make time critical and societally relevant decisions while navigating. What are the basic perceptual and neurocognitive processes with which individuals make movement decisions when guided by human- and context-adaptive namGIDs? How can we study this in an ecologically valid way, also outside of the highly controlled laboratory? We report first ideas and results from our unique neuroadaptive research agenda that brings us closer to answering this fundamental empirical question. We present our first implemented methodological solutions of novel ambulatory evaluation methods to study and improve Location-based System (LBS) displays, by critical examination of how perceptual, neurocognitive, psychophysiological, and display design factors might influence decision-making and spatial learning in pedestrian mobility across broad ranges of users and mobility contexts

Dopamine D4 receptor counteracts morphine-induced changes in M opioid receptor signaling in the striosomes of the rat caudate putamen.

Morphine is one of the most potent analgesic drugs used to relieve moderate to severe pain. After long-term use of morphine, neuroadaptive changes in the brain promotes tolerance, which result in a reduced sensitivity to most of its effects with attenuation of analgesic efficacy, and dependence, revealed by drug craving and physical or psychological manifestations of drug withdrawal. The mu opioid receptor (MOR) is critical, not only in mediating morphine analgesia, but also in addictive behaviors by the induction of a strong rewarding effect. We have previously shown that dopamine D4 receptor (D4R) stimulation counteracts morphine-induced activation of dopaminergic nigrostriatal pathway and accumulation of Fos family transcription factors in the caudate putamen (CPu). In the present work, we have studied the effect of D4R activation on MOR changes induced by morphine in the rat CPu on a continuous drug treatment paradigm, by analyzing MOR protein level, pharmacological profile, and functional coupling to G proteins. Furthermore, using conditioned place preference and withdrawal syndrome test, we have investigated the role of D4R activation on morphine-related behavioural effects. MOR immunoreactivity, agonist binding density and its coupling to G proteins are up-regulated in the striosomes by continuous morphine treatment. Interestingly, co-treatment of morphine with the dopamine D4 receptor (D4R) agonist PD168,077 fully counteracts these adaptive changes in MOR, in spite of the fact that continuous PD168,077 treatment increases the [3H]DAMGO Bmax values to the same degree as seen after continuous morphine treatment. In addition, the administration of the D4R agonist counteracts the rewarding effects of morphine, as well as the development of physical dependence. The present results give support for the existence of antagonistic functional D4R-MOR receptor-receptor interactions in the adaptive changes occurring in MOR of striosomes on continuous administration of morphine and preventing morphine-related behaviour.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Investigating the Impact of a Dual Musical Brain-Computer Interface on Interpersonal Synchrony: A Pilot Study

This study looked into how effective a Musical Brain-Computer Interface (MBCI) can be in providing feedback about synchrony between two people. Using a double EEG setup, we compared two types of musical feedback; one that adapted in real-time based on the inter-brain synchrony between participants (Neuroadaptive condition), and another music that was randomly generated (Random condition). We evaluated how these two conditions were perceived by 8 dyads (n = 16) and whether the generated music could influence the perceived connection and EEG synchrony between them. The findings indicated that Neuroadaptive musical feedback could potentially boost synchrony levels between people compared to Random feedback, as seen by a significant increase in EEG phase-locking values. Additionally, the real-time measurement of synchrony was successfully validated and musical neurofeedback was generally well-received by the participants. However, more research is needed for conclusive results due to the small sample size. This study is a stepping stone towards creating music that can audibly reflect the level of synchrony between individuals.Comment: 6 pages, 4 figure

Neuroadaptive incentivization in healthcare using Blockchain and IoT

Financially incentivizing health-related behaviors can improve health record outcomes and reduce healthcare costs. Blockchain and IoT technologies can be used to develop safe and transparent incentive schemes in healthcare. IoT devices, such as body sensor networks and wearable sensors, etc. connect the physical and digital world making it easier to collect useful health-related data for further analysis. There are, however, many security and privacy issues with the use of IoT. Some of these IoT security issues can be alleviated using Blockchain technology. Incorporating neuroadaptive technology can result in more personalized and effective therapies using machine learning algorithms and real-time feedback. The research investigates the possibilities of neuroadaptive incentivization in healthcare using Blockchain and IoT on patient health records. The core idea is to incentivize patients to keep their health parameters within standard range thereby reducing the load on healthcare system. In summary, we have presented a proof of concept for neuroadaptive incentivization in healthcare using Blockchain and IoT and discuss various applications and implementation challenges

doi:10.1155/2008/868425 Research Article Neural Network Adaptive Control for Discrete-Time Nonlinear Nonnegative Dynamical Systems

Nonnegative and compartmental dynamical system models are derived from mass and energy balance considerations that involve dynamic states whose values are nonnegative. These models are widespread in engineering and life sciences, and they typically involve the exchange of nonnegative quantities between subsystems or compartments, wherein each compartment is assumed to be kinetically homogeneous. In this paper, we develop a neuroadaptive control framework for adaptive set-point regulation of discrete-time nonlinear uncertain nonnegative and compartmental systems. The proposed framework is Lyapunov-based and guarantees ultimate boundedness of the error signals corresponding to the physical system states and the neural network weighting gains. In addition, the neuroadaptive controller guarantees that the physical system states remain in the nonnegative orthant of the state space for nonnegative initial conditions. Copyright q 2008 Wassim M. Haddad et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1

Dissociating frontoparietal brain networks with neuroadaptive Bayesian optimization.

Understanding the unique contributions of frontoparietal networks (FPN) in cognition is challenging because they overlap spatially and are co-activated by diverse tasks. Characterizing these networks therefore involves studying their activation across many different cognitive tasks, which previously was only possible with meta-analyses. Here, we use neuroadaptive Bayesian optimization, an approach combining real-time analysis of functional neuroimaging data with machine-learning, to discover cognitive tasks that segregate ventral and dorsal FPN activity. We identify and subsequently refine two cognitive tasks, Deductive Reasoning and Tower of London, which maximally dissociate the dorsal from ventral FPN. We subsequently investigate these two FPNs in the context of a wider range of FPNs and demonstrate the importance of studying the whole activity profile across tasks to uniquely differentiate any FPN. Our findings deviate from previous meta-analyses and hypothesized functional labels for these FPNs. Taken together the results form the starting point for a neurobiologically-derived cognitive taxonomy

Neuroadaptive technology enables implicit cursor control based on medial prefrontal cortex activity

The effectiveness of today's human-machine interaction is limited by a communication bottleneck as operators are required to translate high-level concepts into a machine-mandated sequence of instructions. In contrast, we demonstrate effective, goal-oriented control of a computer system without any form of explicit communication from the human operator. Instead, the system generated the necessary input itself, based on real-time analysis of brain activity. Specific brain responses were evoked by violating the operators' expectations to varying degrees. The evoked brain activity demonstrated detectable differences reflecting congruency with or deviations from the operators' expectations. Real-time analysis of this activity was used to build a user model of those expectations, thus representing the optimal (expected) state as perceived by the operator. Based on this model, which was continuously updated, the computer automatically adapted itself to the expectations of its operator. Further analyses showed this evoked activity to originate from the medial prefrontal cortex and to exhibit a linear correspondence to the degree of expectation violation. These findings extend our understanding of human predictive coding and provide evidence that the information used to generate the user model is task-specific and reflects goal congruency. This paper demonstrates a form of interaction without any explicit input by the operator, enabling computer systems to become neuroadaptive, that is, to automatically adapt to specific aspects of their operator'smindset. Neuroadaptive technology significantlywidens the communication bottleneck and has the potential to fundamentally change the way we interact with technology