Ideas In Neuroscience Research Articles

Bayesian encoding and decoding as distinct perspectives on neural coding.

The Bayesian brain hypothesis is one of the most influential ideas in neuroscience. However, unstated differences in how Bayesian ideas are operationalized make it difficult to draw general conclusions about how Bayesian computations map onto neural circuits. Here, we identify one such unstated difference: some theories ask how neural circuits could recover information about the world from sensory neural activity (Bayesian decoding), whereas others ask how neural circuits could implement inference in an internal model (Bayesian encoding). These two approaches require profoundly different assumptions and lead to different interpretations of empirical data. We contrast them in terms of motivations, empirical support and relationship to neural data. We also use a simple model to argue that encoding and decoding models are complementary rather than competing. Appreciating the distinction between Bayesian encoding and Bayesian decoding will help to organize future work and enable stronger empirical tests about the nature of inference in the brain.

Reinforcement learning-based AI assistant and VR play therapy game for children with Down syndrome bound to wheelchairs

<abstract> <p>Some of the most significant computational ideas in neuroscience for learning behavior in response to reward and penalty are reinforcement learning algorithms. This technique can be used to train an artificial intelligent (AI) agent to serve as a virtual assistant and a helper. The goal of this study is to determine whether combining a reinforcement learning-based Virtual AI assistant with play therapy. It can benefit wheelchair-bound youngsters with Down syndrome. This study aims to employ play therapy methods and Reinforcement Learning (RL) agents to aid children with Down syndrome and help them enhance their abilities like physical and mental skills by playing games with them. This Agent is designed to be smart enough to analyze each patient's lack of ability and provide a specific set of challenges in the game to improve that ability. Increasing the game's difficulty can help players develop these skills. The agent should be able to assess each player's skill gap and tailor the game to them accordingly. The agent's job is not to make the patient victorious but to boost their morale and skill sets in areas like physical activities, intelligence, and social interaction. The primary objective is to improve the player's physical activities such as muscle reflexes, motor controls and hand-eye coordination. Here, the study concentrates on the employment of several distinct techniques for training various models. This research focuses on comparing the reinforcement learning algorithms like the Deep Q-Learning Network, QR-DQN, A3C and PPO-Actor Critic. This study demonstrates that when compared to other reinforcement algorithms, the performance of the AI helper agent is at its highest when it is trained with PPO-Actor Critic and A3C. The goal is to see if children with Down syndrome who are wheelchair-bound can benefit by combining reinforcement learning with play therapy to increase their mobility.</p> </abstract>

Prediction-error neurons in circuits with multiple neuron types: Formation, refinement, and functional implications

SignificanceAn influential idea in neuroscience is that neural circuits do not only passively process sensory information but rather actively compare them with predictions thereof. A core element of this comparison is prediction-error neurons, the activity of which only changes upon mismatches between actual and predicted sensory stimuli. While it has been shown that these prediction-error neurons come in different variants, it is largely unresolved how they are simultaneously formed and shaped by highly interconnected neural networks. By using a computational model, we study the circuit-level mechanisms that give rise to different variants of prediction-error neurons. Our results shed light on the formation, refinement, and robustness of prediction-error circuits, an important step toward a better understanding of predictive processing.

Neuroscience Concepts Changed Teachers' Views of Pedagogy and Students.

Advances in neuroscience reveal how individual brains change as learning occurs. Translating this neuroscience into practice has largely been unidirectional, from researchers to teachers. However, how teachers view and incorporate neuroscience ideas in their classroom practices remains relatively unexplored. Previously fourteen non-science teachers participated in a 3-week three credit graduate course focusing on foundational ideas in neuroscience. The current work was undertaken to gain insight into if and how individual teachers choose to later apply the proposed set of educational neuroscience concepts (ENCs) in their classrooms. This qualitative follow-up study examined commonalities in how teachers of diverse ages and subjects utilized their new neuroscience understandings. To this end, a year after the course, all participants assessed their perceived usefulness of the ENCs in a survey. Six of those teachers permitted classroom observations and participated in interviews that focused on how the ENCs may have influenced their lesson planning and teaching. The survey revealed that irrespective of subject areas or grade levels taught, teachers found the ENCs useful as organizing principles for their pedagogy now and in the future. Overall teachers estimated that the ENCs’ influence on lesson design had increased from 51% prior to the course to an estimated 90% for future lessons. A cross-case analysis of classroom observations and interviews revealed how teachers used ENCs to inform their pedagogical decisions, organize actions in their classroom, influence their understanding of students, and respond to individual contexts. Teachers recognized the importance of student agency for engaging them in the learning process. The ENCs also offered teachers explanations that affirmed known practices or helped justify exploring untried techniques. The foundational neuroscience concepts offered a small group of teachers a lens to reconsider, re-envision and re-design their lessons. Some teachers applied these ideas more broadly or frequently than others. This case study provided insights into how teachers can directly apply neuroscience knowledge to their practice and views of students.

Toward Robust Functional Neuroimaging Genetics of Cognition.

A commonly held assumption in cognitive neuroscience is that, because measures of human brain function are closer to underlying biology than distal indices of behavior/cognition, they hold more promise for uncovering genetic pathways. Supporting this view is an influential fMRI-based study of sentence reading/listening by Pinel et al. (2012), who reported that common DNA variants in specific candidate genes were associated with altered neural activation in language-related regions of healthy individuals that carried them. In particular, different single-nucleotide polymorphisms (SNPs) of FOXP2 correlated with variation in task-based activation in left inferior frontal and precentral gyri, whereas a SNP at the KIAA0319/TTRAP/THEM2 locus was associated with variable functional asymmetry of the superior temporal sulcus. Here, we directly test each claim using a closely matched neuroimaging genetics approach in independent cohorts comprising 427 participants, four times larger than the original study of 94 participants. Despite demonstrating power to detect associations with substantially smaller effect sizes than those of the original report, we do not replicate any of the reported associations. Moreover, formal Bayesian analyses reveal substantial to strong evidence in support of the null hypothesis (no effect). We highlight key aspects of the original investigation, common to functional neuroimaging genetics studies, which could have yielded elevated false-positive rates. Genetic accounts of individual differences in cognitive functional neuroimaging are likely to be as complex as behavioral/cognitive tests, involving many common genetic variants, each of tiny effect. Reliable identification of true biological signals requires large sample sizes, power calculations, and validation in independent cohorts with equivalent paradigms.SIGNIFICANCE STATEMENT A pervasive idea in neuroscience is that neuroimaging-based measures of brain function, being closer to underlying neurobiology, are more amenable for uncovering links to genetics. This is a core assumption of prominent studies that associate common DNA variants with altered activations in task-based fMRI, despite using samples (10-100 people) that lack power for detecting the tiny effect sizes typical of genetically complex traits. Here, we test central findings from one of the most influential prior studies. Using matching paradigms and substantially larger samples, coupled to power calculations and formal Bayesian statistics, our data strongly refute the original findings. We demonstrate that neuroimaging genetics with task-based fMRI should be subject to the same rigorous standards as studies of other complex traits.

Neural Signal to Violations of Abstract Rules Using Speech-Like Stimuli.

As the evidence of predictive processes playing a role in a wide variety of cognitive domains increases, the brain as a predictive machine becomes a central idea in neuroscience. In auditory processing, a considerable amount of progress has been made using variations of the Oddball design, but most of the existing work seems restricted to predictions based on physical features or conditional rules linking successive stimuli. To characterize the predictive capacity of the brain to abstract rules, we present here two experiments that use speech-like stimuli to overcome limitations and avoid common confounds. Pseudowords were presented in isolation, intermixed with infrequent deviants that contained unexpected phoneme sequences. As hypothesized, the occurrence of unexpected sequences of phonemes reliably elicited an early prediction error signal. These prediction error signals do not seemed to be modulated by attentional manipulations due to different task instructions, suggesting that the predictions are deployed even when the task at hand does not volitionally involve error detection. In contrast, the amount of syllables congruent with a standard pseudoword presented before the point of deviance exerted a strong modulation. Prediction error’s amplitude doubled when two congruent syllables were presented instead of one, despite keeping local transitional probabilities constant. This suggests that auditory predictions can be built integrating information beyond the immediate past. In sum, the results presented here further contribute to the understanding of the predictive capabilities of the human auditory system when facing complex stimuli and abstract rules.

Passions, consciousness, and the Rosetta Stone: Spinoza and embodied, extended, and affective cognition

Baruch Spinoza is often cited as a forerunner of current ideas in neuroscience and neurobiology and is seen as an early champion of embodied cognition. This article aims to specify in what way Spinoza’s ideas are useful to current research on the mind-body problem. Rather than seeking coincidences here and there between Spinoza’s positions and current findings, the article proposes that Spinoza provides a broad ontological framework that can guide research. This idea is fleshed out by contrasting Spinoza’s ideas on emotions, the treatment of harmful passions, and the mind-body relationship with those of Descartes; by showing how Spinoza’s positions are importantly different from those of current neuroscience; and by showing how Spinoza proposes a solution to the problem of consciousness that makes use of his ontological framework, which suggests an heuristic in which mind and body can be treated as hermeneutical keys to each other.

The Neuroscience of Growth Mindset and Intrinsic Motivation.

Our actions can be triggered by intentions, incentives or intrinsic values. Recent neuroscientific research has yielded some results about the growth mindset and intrinsic motivation. With the advances in neuroscience and motivational studies, there is a global need to utilize this information to inform educational practice and research. Yet, little is known about the neuroscientific interplay between growth mindset and intrinsic motivation. This paper attempts to draw on the theories of growth mindset and intrinsic motivation, together with contemporary ideas in neuroscience, outline the potential for neuroscientific research in education. It aims to shed light on the relationship between growth mindset and intrinsic motivation in terms of supporting a growth mindset to facilitate intrinsic motivation through neural responses. Recent empirical research from the educational neuroscience perspective that provides insights into the interplay between growth mindset and intrinsic motivation will also be discussed.

Functional Plasticity in Somatosensory Cortex Supports Motor Learning by Observing

An influential idea in neuroscience is that the sensory-motor system is activated when observing the actions of others [1, 2]. This idea has recently been extended to motor learning, in which observation results in sensory-motor plasticity and behavioral changes in both motor and somatosensory domains [3-9]. However, it is unclear how the brain maps visual information onto motor circuits for learning. Here we test the idea that the somatosensory system, and specifically primary somatosensory cortex (S1), plays a role in motor learning by observing. In experiment 1, we applied stimulation to the median nerve to occupy the somatosensory system with unrelated inputs while participants observed a tutor learning to reach in a force field. Stimulation disrupted motor learning by observing in a limb-specific manner. Stimulation delivered to the right arm (the same arm used by the tutor) disrupted learning, whereas left arm stimulation did not. This is consistent with the idea that a somatosensory representation of the observed effector must be available during observation for learning to occur. In experiment 2, we assessed S1 cortical processing before and after observation by measuring somatosensory evoked potentials (SEPs) associated with median nerve stimulation. SEP amplitudes increased only for participants who observed learning. Moreover, SEPs increased more for participants who exhibited greater motor learning following observation. Taken together, these findings support the idea that motor learning by observing relies on functional plasticity in S1. We propose that visual signals about the movements of others are mapped onto motor circuits for learning via the somatosensory system.

The optimum context for learning; drawing on neuroscience to inform best practice in the classroom

Aims:As our understanding of neuroscience and the developing brain continues to grow, there is a worldwide move to use this information to inform educational practice. This paper attempts to draw together several convergent but complementary ideas in neuroscience to produce a model outlining the optimum context for a child to learn in a classroom.Method:A general outline of brain development is presented, highlighting key areas of brain functioning and cell connection. The positive cycle of learning shows how brain circuits are formed which require the child to be engaged in ‘doing’ a task.Findings:Two key situations which mitigate against a child entering the positive cycle of learning are highlighted: where the child is stressed, and where they are not at their achievable challenge level. Both situations might emphasise activity in the midbrain, which is believed to reduce connections to the forebrain (primarily considered the ‘thinking brain’, which is essential for learning). The importance of a student’s beliefs and mindset are also described which are strong contributors to learning behaviour. Finally, the importance of maintaining a developmental perspective within education is outlined, given what is known about stages of brain development and in particular given the significant changes that take place during adolescence in terms of brain reorganisation. A final model is proposed to guide teacher’s behaviour as a model to be empirically tested in the future.Limitations:Although an emerging area, this field is in its infancy. Many of the studies included in the model have yet to be replicated and the degree to which a cohesive model such as this can offer a testable means for approaching individual needs is unknown. Merging paradigms from different fields such neuroscience, attachment research and education is a fairly recent endeavour and will require further empirical investigation in a collaborative manner.

Effects of brain lesions on moral agency: ethical dilemmas in investigating moral behavior.

Understanding how the "brain produces behavior" is a guiding idea in neuroscience. It is thus of no surprise that establishing an interrelation between brain pathology and antisocial behavior has a long history in brain research. However, interrelating the brain with moral agency--the ability to act in reference to right and wrong--is tricky with respect to therapy and rehabilitation of patients affected by brain lesions. In this contribution, we outline the complexity of the relationship between the brain and moral behavior, and we discuss ethical issues of the neuroscience of ethics and of its clinical consequences. First, we introduce a theory of moral agency and apply it to the issue of behavioral changes caused by brain lesions. Second, we present a typology of brain lesions both with respect to their cause, their temporal development, and the potential for neural plasticity allowing for rehabilitation. We exemplify this scheme with case studies and outline major knowledge gaps that are relevant for clinical practice. Third, we analyze ethical pitfalls when trying to understand the brain-morality relation. In this way, our contribution addresses both researchers in neuroscience of ethics and clinicians who treat patients affected by brain lesions to better understand the complex ethical questions, which are raised by research and therapy of brain lesion patients.

Billionaire Restores Funding for Novel Biomedical Network

Casino magnate Sheldon Adelson is putting up roughly $20 million to $25 million this year to reenergize his medical foundation, which supports collaborative research into bold ideas in neuroscience and cancer. And Adelson hints that number may grow.

Comments on Aur’s “From Neuroelectrodynamics to Thinking Machines”

In [1] Dorian Aur heatedly criticizes what he calls the ‘‘current neurophysiological doctrine,’’ which relies on the measurement of neural events on a millisecond time scale, that is, spikes or action potential. Aur’s intention is no other than to terminate one of the most fundamental ideas in neuroscience since the pioneering work of Edgard Adrian in the 20’s, the functional relevance of these nerve impulses as carriers of information. In Aur’s view, the spike timing and other related forms of neural coding expressed in terms of temporal observables are no more than epiphenomena. The principles of neural computation must be found in the spatial distribution of electrical processes that occur during the action potential. Thus, ‘‘current mainstream provides a weak understanding of computations performed in the brain,’’ because it ignores the ‘‘hidden information’’ embedded in the complex microscopic interactions inside the cell, during the millisecond time frame of a spike. Thus, ‘‘intrinsic computational processes’’ are decided at a much slower time scale and smaller space scale than is commonly assumed in neurophysiology. Neuroelectrodynamics (NED), a new theoretical framework that borrows from Hamiltonian mechanics, Thermodynamics, Quantum Physics and non-Turing computation, is surmised as the ‘‘change in paradigm required’’ to understand ‘‘brain language.’’ This review highlights the methodological pitfalls and conceptual errors introduced in the model suggested by Aur. First, it is shown that the mathematical equations proposed are not adequate for the studied system, that is, the brain, and second, a discussion on the aftermath of the dismissal of spike trains as carriers of relevant information, as stated by Aur, is sketched. First, with regard to the methodological aspects, Aur makes a claim for ‘‘adequate techniques’’ in order to understand ‘‘the neuron’s language.’’ For Aur, the diversity found in actual recording of action potential propagation in nerve cells needs to be explained in terms of the spatial distribution of electrical charges inside the neuron. The spike directivity vector is presented as the tool put on place to reveal the hidden information laying in the intracellular interactions inside the cell. Thus, while mainstream neurophysiology assumes that it is the timing of the spike that matters, Aur announces a new approach to understand neural computation, up to now unperceived by neurophysiologists, in which meaningful patterns are built upon spike directivity vectors that quantify transient charge density taking place during action potential. The methodological implications of Aur’s approach are of a devastating complexity, owing to the stratospheric dimensionality of the neuron models needed to capture the dynamics of ions, molecules and proteins inside every single cell. It is hard to imagine how one may come to grips with the dynamics of such a gargantuan system. There are millions of proteins inside each neuron! Surprisingly enough, Aur’s bet is Hamiltonian mechanics, which is mainly geometry in phase space [2]. Although Aur’s modeling choice is entirely legitimate, the actual model proposed does not apply neither aims at the physical reality for which claims to be conceived, the brain. It goes without saying that a model is always a simplified description of some features of a system, for example point neuron models are simplifications unable to simulate J. Gomez-Ramirez (&) Autonomous Systems Laboratory, Universidad Politecnica de Madrid, Jose Gutierrez Abascal, 2, 28006 Madrid, Spain e-mail: jd.gomez@upm.es

The best rehabilitation programs in the world

The best rehabilitation programs in the world

Roboroach, or, The Extended Phenotype Meets Cognitive Science

I could summarise Being There1 unkindly, but not too inaccurately, as Language of thought: bad. Everything else: good. For the book is a fast and approving tour of a menagerie of alternatives to the classic computational theory of the mind. Some of the usual suspects appear-neural nets, connectionism-but they are not interrogated as deeply as the new members of the gang. For example, Clark walks us through some interesting new ideas in neuroscience. But none of the gang are pressed very hard. Clark a lot of interesting ideas but sketches is the right word. This is a book for the already sympathetic. For none of the examples are developed in the detail that would be required to convince a sceptic. A second issue concerns integration. Clark develops a number of themes as the book unwinds, but by the conclusion I was still uncertain about the extent to which the ideas in Being There are intended to be a package deal. In his 1982, Richard Dawkins developed a new version of the phenotypegenotype distinction. As Dawkins conceives it, evolution is the result of the intersection of two processes. Lineages of replicators-typically genescompete; it is these that succeed or fail in evolution. Gene success, though, is usually indirect. Genes contribute to the construction of vehicles, typically organisms. Vehicles are the agents of ecology; ecological processes are composed of the interaction of vehicles with one another and the abiotic environment. Some vehicles succeed better than others, and genes that help build better vehicles replicate more. So genes are selected in virtue of their phenotypic effects on vehicles. But the effects in question need not be expressed in the body in which the gene is situated. Genes have extended phenotypes. Some of their jointly constructed adaptations are aspects of the organisms in which they ride, and through which they replicate. An animal's speed, sensory acuity, and resistance to environmental insult are all standard bodily adaptations of this kind. But some are not. Some are adaptations-at-a-