Project description:Previous studies have shown that sensorimotor processing can often be described by Bayesian learning, in particular the integration of prior and feedback information depending on its degree of reliability. Here we test the hypothesis that the integration process itself can be tuned to the statistical structure of the environment. We exposed human participants to a reaching task in a three-dimensional virtual reality environment where we could displace the visual feedback of their hand position in a two dimensional plane. When introducing statistical structure between the two dimensions of the displacement, we found that over the course of several days participants adapted their feedback integration process in order to exploit this structure for performance improvement. In control experiments we found that this adaptation process critically depended on performance feedback and could not be induced by verbal instructions. Our results suggest that structural learning is an important meta-learning component of Bayesian sensorimotor integration.
Project description:The effect of sensory signals coming from skin and muscle afferents on the sensorimotor cortical networks is entitled as sensory-motor integration (SMI). SMI can be studied electrophysiologically by the motor cortex excitability changes in response to peripheral sensory stimulation. These changes include the periods of short afferent inhibition (SAI), afferent facilitation (AF), and late afferent inhibition (LAI). During the early period of motor skill acquisition, motor cortex excitability increases and changes occur in the area covered by the relevant zone of the motor cortex. In the late period, these give place to the morphological changes, such as synaptogenesis. SAI decreases during learning the motor skills, while LAI increases during motor activity. In this review, the role of SMI in the process of motor learning and transcranial magnetic stimulation techniques performed for studying SMI is summarized.
Project description:The cointegration of artificial neuronal and synaptic devices with homotypic materials and structures can greatly simplify the fabrication of neuromorphic hardware. We demonstrate experimental realization of vanadium dioxide (VO2) artificial neurons and synapses on the same substrate through selective area carrier doping. By locally configuring pairs of catalytic and inert electrodes that enable nanoscale control over carrier density, volatility or nonvolatility can be appropriately assigned to each two-terminal Mott memory device per lithographic design, and both neuron- and synapse-like devices are successfully integrated on a single chip. Feedforward excitation and inhibition neural motifs are demonstrated at hardware level, followed by simulation of network-level handwritten digit and fashion product recognition tasks with experimental characteristics. Spatially selective electron doping opens up previously unidentified avenues for integration of emerging correlated semiconductors in electronic device technologies.
Project description:Sensory information processing in robot skins currently rely on a centralized approach where signal transduction (on the body) is separated from centralized computation and decision-making, requiring the transfer of large amounts of data from periphery to central processors, at the cost of wiring, latency, fault tolerance and robustness. We envision a decentralized approach where intelligence is embedded in the sensing nodes, using a unique neuromorphic methodology to extract relevant information in robotic skins. Here we specifically address pain perception and the association of nociception with tactile perception to trigger the escape reflex in a sensorized robotic arm. The proposed system comprises self-healable materials and memtransistors as enabling technologies for the implementation of neuromorphic nociceptors, spiking local associative learning and communication. Configuring memtransistors as gated-threshold and -memristive switches, the demonstrated system features in-memory edge computing with minimal hardware circuitry and wiring, and enhanced fault tolerance and robustness.
Project description:Behavioural studies widely implicate sleep in memory consolidation in the learning of a broad range of behaviours. During sleep, brain regions are reactivated, and specific patterns of neural activity are replayed, consistent with patterns observed in previous waking behaviour. Birdsong learning is a paradigmatic model system for skill learning. Song development in juvenile zebra finches (Taeniopygia guttata) is characterized by sleep-dependent circadian fluctuations in singing behaviour, with immediate post-sleep deterioration in song structure followed by recovery later in the day. In sleeping adult birds, spontaneous bursting activity of forebrain premotor neurons in the robust nucleus of the arcopallium (RA) carries information about daytime singing. Here we show that, in juvenile zebra finches, playback during the day of an adult 'tutor' song induced profound and tutor-song-specific changes in bursting activity of RA neurons during the following night of sleep. The night-time neuronal changes preceded tutor-song-induced changes in singing, first observed the following day. Interruption of auditory feedback greatly reduced sleep bursting and prevented the tutor-song-specific neuronal remodelling. Thus, night-time neuronal activity is shaped by the interaction of the song model (sensory template) and auditory feedback, with changes in night-time activity preceding the onset of practice associated with vocal learning. We hypothesize that night-time bursting induces adaptive changes in premotor networks during sleep as part of vocal learning. By this hypothesis, adaptive changes driven by replay of sensory information at night and by evaluation of sensory feedback during the day interact to produce the complex circadian patterns seen early in vocal development.
Project description:Stretchable and flexible multifunctional electronic components, including sensors and actuators, have received increasing attention in robotics, electronics, wearable, and healthcare applications. Despite advances, it has remained challenging to design analogs of many electronic components to be highly stretchable, to be efficient to fabricate, and to provide control over electronic performance. Here, we describe highly elastic sensors and interconnects formed from thin, twisted conductive microtubules. These devices consist of twisted assemblies of thin, highly stretchable (>400%) elastomer tubules filled with liquid conductor (eutectic gallium indium, EGaIn), and fabricated using a simple roller coating process. As we demonstrate, these devices can operate as multimodal sensors for strain, rotation, contact force, or contact location. We also show that, through twisting, it is possible to control their mechanical performance and electronic sensitivity. In extensive experiments, we have evaluated the capabilities of these devices, and have prototyped an array of applications in several domains of stretchable and wearable electronics. These devices provide a novel, low cost solution for high performance stretchable electronics with broad applications in industry, healthcare, and consumer electronics, to emerging product categories of high potential economic and societal significance.
Project description:The integration of deep learning and theories of reinforcement learning (RL) is a promising avenue to explore novel hypotheses on reward-based learning and decision-making in humans and other animals. Here, we trained deep RL agents and mice in the same sensorimotor task with high-dimensional state and action space and studied representation learning in their respective neural networks. Evaluation of thousands of neural network models with extensive hyperparameter search revealed that learning-dependent enrichment of state-value and policy representations of the task-performance-optimized deep RL agent closely resembled neural activity of the posterior parietal cortex (PPC). These representations were critical for the task performance in both systems. PPC neurons also exhibited representations of the internally defined subgoal, a feature of deep RL algorithms postulated to improve sample efficiency. Such striking resemblance between the artificial and biological networks and their functional convergence in sensorimotor integration offers new opportunities to better understand respective intelligent systems.
Project description:High-performance organic neuromorphic devices with miniaturized device size and computing capability are essential elements for developing brain-inspired humanoid intelligence technique. However, due to the structural inhomogeneity of most organic materials, downscaling of such devices to nanoscale and their high-density integration into compact matrices with reliable device performance remain challenging at the moment. Herein, based on the design of a semicrystalline polymer PBFCL10 with ordered structure to regulate dense and uniform formation of conductive nanofilaments, we realize an organic synapse with the smallest device dimension of 50 nm and highest integration size of 1 Kb reported thus far. The as-fabricated PBFCL10 synapses can switch between 32 conductance states linearly with a high cycle-to-cycle uniformity of 98.89% and device-to-device uniformity of 99.71%, which are the best results of organic devices. A mixed-signal neuromorphic hardware system based on the organic neuromatrix and FPGA controller is implemented to execute spiking-plasticity-related algorithm for decision-making tasks.
Project description:Electronics allowing for visible light to pass through are attractive, where a key challenge is to make the core functional units transparent. Here, it is shown that transparent electronics can be constructed by epitaxial growth of metal-organic frameworks (MOFs) on single-layer graphene (SLG) to give a desirable transparency of 95.7% to 550 nm visible light and an electrical conductivity of 4.0 × 104 S m-1. Through lattice and symmetry match, collective alignment of MOF pores and dense packing of MOFs vertically on SLG are achieved, as directly visualized by electron microscopy. These MOF-on-SLG constructs are capable of room-temperature recognition of gas molecules at the ppb level with a linear range from 10 to 108 ppb, providing real-time gas monitoring function in transparent electronics. The corresponding devices can be fabricated on flexible substrates with large size, 3 × 5 cm, and afford continuous folding for more than 200 times without losing conductivity or transparency.
Project description:Information processing in the nervous system during sensorimotor tasks with inherent uncertainty has been shown to be consistent with Bayesian integration. Bayes optimal decision-makers are, however, risk-neutral in the sense that they weigh all possibilities based on prior expectation and sensory evidence when they choose the action with highest expected value. In contrast, risk-sensitive decision-makers are sensitive to model uncertainty and bias their decision-making processes when they do inference over unobserved variables. In particular, they allow deviations from their probabilistic model in cases where this model makes imprecise predictions. Here we test for risk-sensitivity in a sensorimotor integration task where subjects exhibit Bayesian information integration when they infer the position of a target from noisy sensory feedback. When introducing a cost associated with subjects' response, we found that subjects exhibited a characteristic bias towards low cost responses when their uncertainty was high. This result is in accordance with risk-sensitive decision-making processes that allow for deviations from Bayes optimal decision-making in the face of uncertainty. Our results suggest that both Bayesian integration and risk-sensitivity are important factors to understand sensorimotor integration in a quantitative fashion.