1,110 research outputs found
Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks
Biological plastic neural networks are systems of extraordinary computational
capabilities shaped by evolution, development, and lifetime learning. The
interplay of these elements leads to the emergence of adaptive behavior and
intelligence. Inspired by such intricate natural phenomena, Evolved Plastic
Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed
plastic neural networks with a large variety of dynamics, architectures, and
plasticity rules: these artificial systems are composed of inputs, outputs, and
plastic components that change in response to experiences in an environment.
These systems may autonomously discover novel adaptive algorithms, and lead to
hypotheses on the emergence of biological adaptation. EPANNs have seen
considerable progress over the last two decades. Current scientific and
technological advances in artificial neural networks are now setting the
conditions for radically new approaches and results. In particular, the
limitations of hand-designed networks could be overcome by more flexible and
innovative solutions. This paper brings together a variety of inspiring ideas
that define the field of EPANNs. The main methods and results are reviewed.
Finally, new opportunities and developments are presented
Embodied Robot Models for Interdisciplinary Emotion Research
Due to their complex nature, emotions cannot be properly understood from the perspective of a single discipline. In this paper, I discuss how the use of robots as models is beneficial for interdisciplinary emotion research. Addressing this issue through the lens of my own research, I focus on a critical analysis of embodied robots models of different aspects of emotion, relate them to theories in psychology and neuroscience, and provide representative examples. I discuss concrete ways in which embodied robot models can be used to carry out interdisciplinary emotion research, assessing their contributions: as hypothetical models, and as operational models of specific emotional phenomena, of general emotion principles, and of specific emotion ``dimensions''. I conclude by discussing the advantages of using embodied robot models over other models.Peer reviewe
Synaptic motor adaptation: A three-factor learning rule for adaptive robotic control in spiking neural networks
Legged robots operating in real-world environments must possess the ability
to rapidly adapt to unexpected conditions, such as changing terrains and
varying payloads. This paper introduces the Synaptic Motor Adaptation (SMA)
algorithm, a novel approach to achieving real-time online adaptation in
quadruped robots through the utilization of neuroscience-derived rules of
synaptic plasticity with three-factor learning. To facilitate rapid adaptation,
we meta-optimize a three-factor learning rule via gradient descent to adapt to
uncertainty by approximating an embedding produced by privileged information
using only locally accessible onboard sensing data. Our algorithm performs
similarly to state-of-the-art motor adaptation algorithms and presents a clear
path toward achieving adaptive robotics with neuromorphic hardware
A Hormone-Driven Epigenetic Mechanism for Adaptation in Autonomous Robots
Different epigenetic mechanisms provide biological organisms with the ability to adjust their physiology and/or morphology and adapt to a wide range of challenges posed by their environments. In particular, one type of epigenetic process, in which hormone concentrations are linked to the regulation of hormone receptors, has been shown to have implications for behavioral development. In this paper, taking inspiration from these biological processes, we investigate whether an epigenetic model based on the concept of hormonal regulation of receptors can provide a similarly robust and general adaptive mechanism for autonomous robots. We have implemented our model using a Koala robot, and tested it in a series of experiments in six different environments with varying challenges to negotiate. Our results, including the emergence of varied behaviors that permit the robot to exploit its current environment, demonstrate the potential of our epigenetic model as a general mechanism for adaptation in autonomous robots.Peer reviewe
Функциональные системы и функциональные блоки мозга после Лурия, с Лурия: анатомические аспекты
Original manuscript received February 4, 2020.Revised manuscript accepted March 18, 2020.This paper describes the anatomical aspects of a functional brain model that develops A. R. Luria’s ideas. Five functional brain units are described on the basis of ontogenetic, anatomical, histological, functional, and clinical studies: preferential or primordial (unit I), limbic (unit II), cortical (unit III), basal ganglia (unit IV), and cerebellar (unit V). This review allows two large integrated and interrelated functional complexes to be distinguished: a primordial-limbic complex (units I and II) and a supralimbic one (units, III, IV and V). There is consensus that there exists a clear interplay among the cortex, the basal ganglia, and the cerebellum. Three main simplified parallel cortico-basal ganglia systems have been recognized: limbic, associative, and sensorimotor. Certain structures (e. g. neuromodulatory systems, hypothalamus, and paralimbic cortex) form functional links among units. Future studies are required to develop and improve the proposed model.В данной статье развиваются идеи А. Р. Лурия, касающиеся анатомических аспектов функциональной модели мозга. На основании онтогенетических, анатомических, гистологических, функциональных и клинических исследований описаны пять функциональных блоков мозга: преимущественные или первичные (блок I), лимбические (блок II), корковые (блок III), базальные ганглии (блок IV) и мозжечок (блок V). Этот обзор позволяет выделить два крупных интегрированных и взаимосвязанных функциональных комплекса: примордиально-лимбический комплекс (блоки I, II) и супралимбический комплекс (блоки III, IV, V). Существует консенсус, который представляет собой четкое взаимодействие между корой головного мозга, базальными ганглиями и мозжечком. Различают три основные упрощенные параллельные системы кортико-базальных ганглиев: лимбическую, ассоциативную и сенсомоторную. Некоторые структуры (например, нейромодулирующие системы, гипоталамус и паралимбическая кора) образуют функциональные связи между блоками. Для разработки и улучшения предлагаемой модели необходимы дальнейшие исследования
Dysconnection in schizophrenia: from abnormal synaptic plasticity to failures of self-monitoring
Over the last 2 decades, a large number of neurophysiological and neuroimaging studies of patients with schizophrenia have furnished in vivo evidence for dysconnectivity, ie, abnormal functional integration of brain processes. While the evidence for dysconnectivity in schizophrenia is strong, its etiology, pathophysiological mechanisms, and significance for clinical symptoms are unclear. First, dysconnectivity could result from aberrant wiring of connections during development, from aberrant synaptic plasticity, or from both. Second, it is not clear how schizophrenic symptoms can be understood mechanistically as a consequence of dysconnectivity. Third, if dysconnectivity is the primary pathophysiology, and not just an epiphenomenon, then it should provide a mechanistic explanation for known empirical facts about schizophrenia. This article addresses these 3 issues in the framework of the dysconnection hypothesis. This theory postulates that the core pathology in schizophrenia resides in aberrant N-methyl-D-aspartate receptor (NMDAR)–mediated synaptic plasticity due to abnormal regulation of NMDARs by neuromodulatory transmitters like dopamine, serotonin, or acetylcholine. We argue that this neurobiological mechanism can explain failures of self-monitoring, leading to a mechanistic explanation for first-rank symptoms as pathognomonic features of schizophrenia, and may provide a basis for future diagnostic classifications with physiologically defined patient subgroups. Finally, we test the explanatory power of our theory against a list of empirical facts about schizophrenia
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