Sensorimotor representation learning for an "active self" in robots: A model survey

Safe human-robot interactions require robots to be able to learn how to behave appropriately in \sout{humans' world} \rev{spaces populated by people} and thus to cope with the challenges posed by our dynamic and unstructured environment, rather than being provided a rigid set of rules for operations. In humans, these capabilities are thought to be related to our ability to perceive our body in space, sensing the location of our limbs during movement, being aware of other objects and agents, and controlling our body parts to interact with them intentionally. Toward the next generation of robots with bio-inspired capacities, in this paper, we first review the developmental processes of underlying mechanisms of these abilities: The sensory representations of body schema, peripersonal space, and the active self in humans. Second, we provide a survey of robotics models of these sensory representations and robotics models of the self; and we compare these models with the human counterparts. Finally, we analyse what is missing from these robotics models and propose a theoretical computational framework, which aims to allow the emergence of the sense of self in artificial agents by developing sensory representations through self-exploration

Sensorimotor Representation Learning for an “Active Self” in Robots: A Model Survey

Safe human-robot interactions require robots to be able to learn how to behave appropriately in spaces populated by people and thus to cope with the challenges posed by our dynamic and unstructured environment, rather than being provided a rigid set of rules for operations. In humans, these capabilities are thought to be related to our ability to perceive our body in space, sensing the location of our limbs during movement, being aware of other objects and agents, and controlling our body parts to interact with them intentionally. Toward the next generation of robots with bio-inspired capacities, in this paper, we first review the developmental processes of underlying mechanisms of these abilities: The sensory representations of body schema, peripersonal space, and the active self in humans. Second, we provide a survey of robotics models of these sensory representations and robotics models of the self; and we compare these models with the human counterparts. Finally, we analyze what is missing from these robotics models and propose a theoretical computational framework, which aims to allow the emergence of the sense of self in artificial agents by developing sensory representations through self-exploration.Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Projekt DEALPeer Reviewe

A group-theoretic approach to formalizing bootstrapping problems

The bootstrapping problem consists in designing agents that learn a model of themselves and the world, and utilize it to achieve useful tasks. It is different from other learning problems as the agent starts with uninterpreted observations and commands, and with minimal prior information about the world. In this paper, we give a mathematical formalization of this aspect of the problem. We argue that the vague constraint of having "no prior information" can be recast as a precise algebraic condition on the agent: that its behavior is invariant to particular classes of nuisances on the world, which we show can be well represented by actions of groups (diffeomorphisms, permutations, linear transformations) on observations and commands. We then introduce the class of bilinear gradient dynamics sensors (BGDS) as a candidate for learning generic robotic sensorimotor cascades. We show how framing the problem as rejection of group nuisances allows a compact and modular analysis of typical preprocessing stages, such as learning the topology of the sensors. We demonstrate learning and using such models on real-world range-finder and camera data from publicly available datasets

Allocating structure to function: the strong links between neuroplasticity and natural selection

A central question in brain evolution is how species-typical behaviors, and the neural function-structure mappings supporting them, can be acquired and inherited. Advocates of brain modularity, in its different incarnations across scientific subfields, argue that natural selection must target domain-dedicated, separately modifiable neural subsystems, resulting in genetically-specified functional modules. In such modular systems, specification of neuron number and functional connectivity are necessarily linked. Mounting evidence, however, from allometric, developmental, comparative, systems-physiological, neuroimaging and neurological studies suggests that brain elements are used and reused in multiple functional systems. This variable allocation can be seen in short-term neuromodulation, in neuroplasticity over the lifespan and in response to damage. We argue that the same processes are evident in brain evolution. Natural selection must preserve behavioral functions that may co-locate in variable amounts with other functions. In genetics, the uses and problems of pleiotropy, the re-use of genes in multiple networks have been much discussed, but this issue has been sidestepped in neural systems by the invocation of modules. Here we highlight the interaction between evolutionary and developmental mechanisms to produce distributed and overlapping functional architectures in the brain. These adaptive mechanisms must be robust to perturbations that might disrupt critical information processing and action selection, but must also recognize useful new sources of information arising from internal genetic or environmental variability, when those appear. These contrasting properties of robustness and evolvability have been discussed for the basic organization of body plan and fundamental cell physiology. Here we extend them to the evolution and development, evo-devo, of brain structure

Whole brain Probabilistic Generative Model toward Realizing Cognitive Architecture for Developmental Robots

Building a humanlike integrative artificial cognitive system, that is, an artificial general intelligence, is one of the goals in artificial intelligence and developmental robotics. Furthermore, a computational model that enables an artificial cognitive system to achieve cognitive development will be an excellent reference for brain and cognitive science. This paper describes the development of a cognitive architecture using probabilistic generative models (PGMs) to fully mirror the human cognitive system. The integrative model is called a whole-brain PGM (WB-PGM). It is both brain-inspired and PGMbased. In this paper, the process of building the WB-PGM and learning from the human brain to build cognitive architectures is described.Comment: 55 pages, 8 figures, submitted to Neural Network

Spectral estimation for random processes with stationary increments

In studying a stationary random process on R, the covariance function is commonlyused to characterize the second-order spatial dependency. Through the inversionof Fourier transformation, its corresponding spectral density has been widely usedto describe the periodical components and frequencies. When the process is with stationarydth increments, that is, when the resulting process after undertaken dth orderof di erences is stationary, the notion of structure function is put forward. Throughthe inversion formula, the spectrum can be represented by the structure function.In this dissertation, we rst investigate the properties of the proposed Method ofMoments structure function estimator, through which we obtain the spectral densityfunction estimation of the underlying process. In particular, when the process is intrinsicallystationary, which is also a process is with stationary increments of order 1,we derive the spectral density functions for commonly used variogram models. Furthermore,our proposed estimation method is applied to estimate the spectral densityof power variogram models. All of the above results are supplemented via simulationsand a real data analysis. Our results show that the proposed estimation method performswell in recovering the true spectral density function on various processes withstationary increments we considered.[This abstract has been edited to remove characters that will not display in this system. Please see the PDF for the full abstract.]]]> 2018 Spectral theory (Mathematics) Estimation theory Stochastic processes Stationary processes English http://libres.uncg.edu/ir/uncg/f/Chen_uncg_0154D_12424.pdf oai:libres.uncg.edu/23131 2019-03-04T14:06:04Z UNCG The effects of instrumental music instruction on the neurophysiological responses and adaptive behaviors of children with autism spectrum disorder Chinn Cannon, Michelle L. NC DOCKS at The University of North Carolina at Greensboro <![CDATA[Autism spectrum disorder, also referred to as autism, is a complex and heterogeneous neurodevelopmental condition characterized by deficits in social communication, delayed or absent language development, and restricted or repetitive behaviors. Finding an appropriate, effective, and affordable intervention that targets these differences may increase access of children with autism to treatment that improves their quality of life, independence, and productivity, while reducing lifetime care costs. The premise of this exploratory study was that music instruction may serve as an appropriate, effective, and affordable intervention for children with autism. Previous researchers noted that children with autism have both an affinity for and ability in music, while neuroscientists demonstrated increased cortical growth and neural network responses among musicians. At the onset of the current study, no published research studies were found that explicitly examined effects of musical training on both neural activity and adaptive behaviors of children with autism. The purpose of this exploratory research study was to investigate the effects of instrumental music instruction on neurophysiological responses and adaptive behaviors of children with autism. Fourteen children with autism participated in the current study. During a 20-week period, a control group (n = 7) received 30 minutes of non-music intervention per week, and an experimental group (n = 7) received 30 minutes of music intervention (i.e., violin instruction) per week. Before and after the intervention period, neurophysiological and adaptive behavioral data were collected from control and experimental groups. The 14 participants of the study were assigned randomly to either the control (i.e., non-music intervention group), or the experimental (i.e., music intervention group). Eleven children completed the behavioral segment of this study, five in the control group and six in the experimental group. As compared to the non-music intervention group, experimental participants displayed significant gains in Expressive Communication (p =.018). Increases in Interpersonal Socialization by the music intervention group also approached significance (p = .057). The researcher found a moderately large effect size for Expressive Communication (r = .694), and for Interpersonal Socialization (r = .589), accounting for approximately 40% and 35% of the variances of the two adaptive behaviors before and after music intervention, respectively. Eight children completed the neurophysiological segment of this study, three in the control group and five in the experimental group. Results revealed several trends in the differences between the control and experimental intervention groups' postintervention neurophysiological responses. While changes were not observed among the non-music group's pre- and post-intervention cortical activity, changes were observed among the experimental group's cortical activation in areas associated with social and language learning. These findings supported the premise that instrumental music study may serve as an appropriate, effective, and affordable intervention, targeting the hallmark behaviors of autism and potentially associated cortical areas

Effects of psychological stressors and [delta]�-THC acutely and chronically on zebra finch song behavior and dendritic spine density

This dissertation investigated song performance and dendritic spine density, following acute restraint stress in adult zebra finches and, following chronic mild stress and CB1 receptor partial agonist [delta]�-tetrahydrocannabinol ( [delta]�-THC) treatments (3 mg/kg) during sensorimotor development or adulthood. CB1 receptor agonists and stressors have mechanistic overlap: a stressor activates glucocorticoid corticosterone release in the hypothalamic-pituitary-adrenal axis, and endocannabinoids anandamide (AEA) and 2-arachidonoylglycerol (2-AG) are CB1 receptor agonists which operate as an endogenous stress buffer system that turns off the response. The endocannabinoid system is prominent during late postnatal development and may modulate important synaptic fine-tuning. Chronic CB1 receptor agonist treatment or stressors during this developmental stage may disrupt appropriate endocannabinoid signaling mediating brain development. Male zebra finches possess a developmental, sensorimotor critical period for learning a song in a mechanism similar to language acquisition in humans. Initially in sensorimotor development, zebra finches possess a surplus of dendritic spines, which are the anatomical basis of the post-synaptic site with excitatory input and may represent morphological building blocks of learning and memory. Over time, a net elimination occurs as part of the developmental learning process. In this dissertation, acute restraint stress (30 minutes) in adults rapidly increased plasma corticosterone levels, altered performance of spectral and temporal acoustic features, and stimulated dendritic spine and c-Fos immunolabeled nuclei density in higher-order acoustic region NCM. [delta]�-THC, the principal psychoactive component of marijuana, inhibits perceptual sensory processing, and [delta]�-THC pretreatment antagonized the effects of stress on c-Fos density in NCM in a CB1 receptor inverse agonist/antagonist SR141716 (6 mg/kg)-reversible manner. The acute effects differed from chronic effects. In adult groups, chronic mild stress or [delta]�-THC treatments alone did not alter corticosterone levels, song acoustic features, or dendritic spine density in NCM or basal ganglia/striatal region Area X. Both chronic stress and [delta]�-THC treatments during sensorimotor song development resulted in effects persistent into adulthood, with reduced syllable entropy and dendritic spine density in Area X. These effects suggest an interference with typical developmental song learning and brain development. Adolescent brain development may be vulnerable to long-term consequences following chronic exposure to CB1 receptor agonists or stressors, and their effects likely differ than exposure during adulthood. This distinction is important to the elucidation of mechanisms and outcomes of marijuana and psychological disorders, such as depression

The malleable brain: plasticity of neural circuits and behavior: A review from students to students

One of the most intriguing features of the brain is its ability to be malleable, allowing it to adapt continually to changes in the environment. Specific neuronal activity patterns drive long-lasting increases or decreases in the strength of synaptic connections, referred to as long-term potentiation (LTP) and long-term depression (LTD) respectively. Such phenomena have been described in a variety of model organisms, which are used to study molecular, structural, and functional aspects of synaptic plasticity. This review originated from the first International Society for Neurochemistry (ISN) and Journal of Neurochemistry (JNC) Flagship School held in Alpbach, Austria (Sep 2016), and will use its curriculum and discussions as a framework to review some of the current knowledge in the field of synaptic plasticity. First, we describe the role of plasticity during development and the persistent changes of neural circuitry occurring when sensory input is altered during critical developmental stages. We then outline the signaling cascades resulting in the synthesis of new plasticity-related proteins, which ultimately enable sustained changes in synaptic strength. Going beyond the traditional understanding of synaptic plasticity conceptualized by LTP and LTD, we discuss system-wide modifications and recently unveiled homeostatic mechanisms, such as synaptic scaling. Finally, we describe the neural circuits and synaptic plasticity mechanisms driving associative memory and motor learning. Evidence summarized in this review provides a current view of synaptic plasticity in its various forms, offers new insights into the underlying mechanisms and behavioral relevance, and provides directions for future research in the field of synaptic plasticity.Fil: Schaefer, Natascha. University of Wuerzburg; AlemaniaFil: Rotermund, Carola. University of Tuebingen; AlemaniaFil: Blumrich, Eva Maria. Universitat Bremen; AlemaniaFil: Lourenco, Mychael V.. Universidade Federal do Rio de Janeiro; BrasilFil: Joshi, Pooja. Robert Debre Hospital; FranciaFil: Hegemann, Regina U.. University of Otago; Nueva ZelandaFil: Jamwal, Sumit. ISF College of Pharmacy; IndiaFil: Ali, Nilufar. Augusta University; Estados UnidosFil: García Romero, Ezra Michelet. Universidad Veracruzana; MéxicoFil: Sharma, Sorabh. Birla Institute of Technology and Science; IndiaFil: Ghosh, Shampa. Indian Council of Medical Research; IndiaFil: Sinha, Jitendra K.. Indian Council of Medical Research; IndiaFil: Loke, Hannah. Hudson Institute of Medical Research; AustraliaFil: Jain, Vishal. Defence Institute of Physiology and Allied Sciences; IndiaFil: Lepeta, Katarzyna. Polish Academy of Sciences; ArgentinaFil: Salamian, Ahmad. Polish Academy of Sciences; ArgentinaFil: Sharma, Mahima. Polish Academy of Sciences; ArgentinaFil: Golpich, Mojtaba. University Kebangsaan Malaysia Medical Centre; MalasiaFil: Nawrotek, Katarzyna. University Of Lodz; ArgentinaFil: Paid, Ramesh K.. Indian Institute of Chemical Biology; IndiaFil: Shahidzadeh, Sheila M.. Syracuse University; Estados UnidosFil: Piermartiri, Tetsade. Universidade Federal de Santa Catarina; BrasilFil: Amini, Elham. University Kebangsaan Malaysia Medical Centre; MalasiaFil: Pastor, Verónica. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia ; ArgentinaFil: Wilson, Yvette. University of Melbourne; AustraliaFil: Adeniyi, Philip A.. Afe Babalola University; NigeriaFil: Datusalia, Ashok K.. National Brain Research Centre; IndiaFil: Vafadari, Benham. Polish Academy of Sciences; ArgentinaFil: Saini, Vedangana. University of Nebraska; Estados UnidosFil: Suárez Pozos, Edna. Instituto Politécnico Nacional; MéxicoFil: Kushwah, Neetu. Defence Institute of Physiology and Allied Sciences; IndiaFil: Fontanet, Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia ; ArgentinaFil: Turner, Anthony J.. University of Leeds; Reino Unid