Improving Language Model Predictions via Prompts Enriched with Knowledge Graphs

Despite advances in deep learning and knowledge graphs (KGs), using language models for natural language understanding and question answering remains a challenging task. Pre-trained language models (PLMs) have shown to be able to leverage contextual information, to complete cloze prompts, next sentence completion and question answering tasks in various domains. Unlike structured data querying in e.g. KGs, mapping an input question to data that may or may not be stored by the language model is not a simple task. Recent studies have highlighted the improvements that can be made to the quality of information retrieved from PLMs by adding auxiliary data to otherwise naive prompts. In this paper, we explore the effects of enriching prompts with additional contextual information leveraged from the Wikidata KG on language model performance. Specifically, we compare the performance of naive vs. KG-engineered cloze prompts for entity genre classification in the movie domain. Selecting a broad range of commonly available Wikidata properties, we show that enrichment of cloze-style prompts with Wikidata information can result in a significantly higher recall for the investigated BERT and RoBERTa large PLMs. However, it is also apparent that the optimum level of data enrichment differs between models

Mobile phone based mini-spectrometer for rapid screening of skin cancer

We demonstrate a highly sensitive mobile phone based spectrometer that has potential to detect cancerous skin lesions in a rapid, non-invasive manner. Earlier reports of low cost spectrometers utilize the camera of the mobile phone to image the field after moving through a diffraction grating. These approaches are inherently limited by the closed nature of mobile phone image sensors and built in optical elements. The system presented uses a novel integrated grating and sensor that is compact, accurate and calibrated. Resolutions of about 10 nm can be achieved. Additionally, UV and visible LED excitation sources are built into the device. Data collection and analysis is simplified using the wireless interfaces and logical control on the smart phone. Furthermore, by utilizing an external sensor, the mobile phone camera can be used in conjunction with spectral measurements. We are exploring ways to use this device to measure endogenous fluorescence of skin in order to distinguish cancerous from non-cancerous lesions with a mobile phone based dermatoscope

Genome-wide meta-analyses reveal novel loci for verbal short-term memory and learning

Understanding the genomic basis of memory processes may help in combating neurodegenerative disorders. Hence, we examined the associations of common genetic variants with verbal short-term memory and verbal learning in adults without dementia or stroke (N = 53,637). We identified novel loci in the intronic region of CDH18, and at 13q21 and 3p21.1, as well as an expected signal in the APOE/APOC1/TOMM40 region. These results replicated in an independent sample. Functional and bioinformatic analyses supported many of these loci and further implicated POC1. We showed that polygenic score for verbal learning associated with brain activation in right parieto-occipital region during working memory task. Finally, we showed genetic correlations of these memory traits with several neurocognitive and health outcomes. Our findings suggest a role of several genomic loci in verbal memory processes.Peer reviewe

TOWARDS PAIN CONTROL BY MODELING THE INTERACTIONS IN A MAMMALIAN NERVE FIBER

Objectives. Electrical stimulation of nerve fibers is used to treat various diseases. De- spite efforts to model the effects of stimulation, its underlying mechanisms remain unclear. This is because current mechanistic models just quantify the effects that the electrical field produces near the fiber and do not capture interactions between stimulus-initiated action potentials (APs) and underlying physiological activity initiated APs. Physiological activity induced APs, noxious or innocuous, travel along the fibers and may, for example, collide with a stimulus AP, and thus may never be relayed to the brain. In this study, we aim to quantify the effects of stimulation frequency on these interactions. Methods. We construct three computational models of a nerve fiber of varying degrees of complexity (probabilistic, reduced and mechanistic) each receiving two in- puts: the underlying physiological activity at one end of the fiber, and the external stimulus applied to the middle of the fiber. We then define conduction reliability, C, as the percentage of physiological APs that make it to the other end of the nerve fiber. We apply the two inputs to the fiber at various frequencies and analyse conduction reliability. Results. We find that at input frequencies (100 Hz) inputs result in intra-signal loss of excitability (stimulus/physiological AP induces refractory period and fiber does not respond to next stimulus/physiological pulse). The interaction statistics produced by reduced versus mechanistic models are linearly related. On the other hand, the probabilistic model captures relay properties for low input frequencies (< 10 Hz) but then differs from reduced and mechanistic models if either input has a larger frequency. This is because the probabilistic model only accounts for only (i) inter signal loss of excitability and (ii) collisions between stimulus-initiated action potentials (APs) and underlying physiological activity initiated APs. Conclusions. Modelling the interactions in a nerve fiber opens up opportunities to- wards understanding mechanisms of electrical stimulation therapies

Modeling electrical stimulation of mammalian nerve fibers: A mechanistic versus probabilistic approach

peer reviewedElectrical neurostimulation is increasingly used over neuropharmacology to treat various diseases. Despite efforts to model the effects of electrical stimulation, its underlying mechanisms remain unclear. This is because current mechanistic models just quantify the effects that the electrical field produces near the fiber and do not capture interactions between stimulus-initiated action potentials (APs) and underlying physiological activity initiated APs. In this study, we aim to quantify and compare these interactions. We construct two computational models of a nerve fiber of varying degrees of complexity (probabilistic versus mechanistic) each receiving two inputs: the underlying physiological activity at one end of the fiber, and the external stimulus applied to the middle of the fiber. We then define reliability, R, as the percentage of physiological APs that make it to the other end of the nerve fiber. We apply the two inputs to the fiber at various frequencies and analyze reliability. We find that the probabilistic model captures relay properties for low input frequencies (<; 10 Hz) but then differs from the mechanistic model if either input has a larger frequency. This is because the probabilistic model only accounts for only (i) inter signal loss of excitability and (ii) collisions between stimulus-initiated action potentials (APs) and underlying physiological activity initiated APs. This first step towards modeling the interactions in a nerve fiber opens up opportunities towards understanding mechanisms of electrical stimulation therapies

Modeling the interactions between stimulation and physiologically induced APs in a mammalian nerve fiber: dependence on frequency and fiber diameter

Electrical stimulation of nerve fibers is used as a therapeutic tool to treat neurophysiological disorders. Despite efforts to model the effects of stimulation, its underlying mechanisms remain unclear. Current mechanistic models quantify the effects that the electrical field produces near the fiber but do not capture interactions between action potentials (APs) initiated by stimulus and APs initiated by underlying physiological activity. In this study, we aim to quantify the effects of stimulation frequency and fiber diameter on AP interactions involving collisions and loss of excitability. We constructed a mechanistic model of a myelinated nerve fiber receiving two inputs: the underlying physiological activity at the terminal end of the fiber, and an external stimulus applied to the middle of the fiber. We define conduction reliability as the percentage of physiological APs that make it to the somatic end of the nerve fiber. At low input frequencies, conduction reliability is greater than 95% and decreases with increasing frequency due to an increase in AP interactions. Conduction reliability is less sensitive to fiber diameter and only decreases slightly with increasing fiber diameter. Finally, both the number and type of AP interactions significantly vary with both input frequencies and fiber diameter. Modeling the interactions between APs initiated by stimulus and APs initiated by underlying physiological activity in a nerve fiber opens opportunities towards understanding mechanisms of electrical stimulation therapies

Selective relay of afferent sensory-induced action potentials from peripheral nerve to brain and the effects of electrical stimulation

peer reviewedElectrical stimulation of peripheral nerve fibers and dorsal column fibers is used to treat acute and chronic pain. Recent studies have shown that sensitized A-fibers maybe involved in the relay of pain information. These nerve fibers also carry sensory-induced action potentials (APs), such as proprioception, mechanoreception, etc. Electrical stimulation of these nerve fibers can result in interactions between sensory-induced APs and stimulation-induced APs. For example, the sensory-induced APs can collide with stimulus APs, and thus may never be relayed to the brain. In this study, we aimed to quantify the effects of stimulation frequency on these interactions. Specifically, we focused on the goal of stimulation to simultaneously (i) block noxious sensory signals while (ii) relaying innocuous sensory signals from the periphery to the brain via a myelinated nerve fiber. We defined a performance metric called the ``selective relay $(SR)$ '' measure. Specifically, we constructed a tractable model of a nerve fiber that receives two inputs: the underlying sensory activity at the bottom of the fiber (noxious or innocuous), and the external stimulus applied to the middle of the fiber. We then defined relay reliability, $R$, as the percentage of sensory APs that make it to the top of the fiber. $SR$ is then a product of relaying innocuous sensory information while blocking noxious pain stimuli, i.e., $SR=R_ s e n(1-R_ p a i n)$. We applied the two inputs to the fiber at various frequencies and analyzed relay reliability and then we studied selective relay assuming noxious and innocuous stimuli produce APs with distinct frequencies. We found that frequency stimulation between 50-100Hz effectively blocks relay of low-frequency pain signals, allowing mid-to-high frequency sensory signals to transmit to the brain

Voltage-sensitive dye imaging of mouse neocortex during a whisker detection task

Sensorimotor processing occurs in a highly distributed manner in the mammalian neocortex. The spatiotemporal dynamics of electrical activity in the dorsal mouse neocortex can be imaged using voltage-sensitive dyes (VSDs) with near-millisecond temporal resolution and similar to 100-mu m spatial resolution. Here, we trained mice to lick a water reward spout after a 1-ms deflection of the C2 whisker, and we imaged cortical dynamics during task execution with VSD RH1691. Responses to whisker deflection were highly dynamic and spatially highly distributed, exhibiting high variability from trial to trial in amplitude and spatiotemporal dynamics. We differentiated trials based on licking and whisking behavior. Hit trials, in which the mouse licked after the whisker stimulus, were accompanied by overall greater depolarization compared to miss trials, with the strongest hit versus miss differences being found in frontal cortex. Prestimulus whisking decreased behavioral performance by increasing the fraction of miss trials, and these miss trials had attenuated cortical sensorimotor responses. Our data suggest that the spatiotemporal dynamics of depolarization in mouse sensorimotor cortex evoked by a single brief whisker deflection are subject to important behavioral modulation during the execution of a simple, learned, goal-directed sensorimotor transformation. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License

Studying the interactions in a mammalian nerve fiber: a functional modeling approach

peer reviewedModern therapeutic interventions are increasingly favoring electrical stimulation to treat neurophysiological dis-orders. These therapies are associated with suboptimal efficacy since most neurostimulation devices operate in an open-loop manner (i.e., stimulation settings like frequency, amplitude are preprogrammed). A closed-loop system can dynamically adjust stimulation parameters and may provide efficient therapies. Computational models used to design these systems vary in complexity which can adversely affect their real-time performance. In this study, we compare two models of varying degrees of complexity. We constructed two computational models of a myelinated nerve fiber (functional versus mechanistic) each receiving two inputs: the underlying physiological activity at one end of the fiber, and the external stimulus applied to the middle of the fiber. We then defined relay reliability as the percentage of physiological action potentials that make it to the other end of the nerve fiber. We applied the two inputs to the fiber at various frequencies and analyze reliability. We found that the functional model and the mechanistic model have similar reliability properties, but the functional model significantly decreases the computational complexity and simulation run time. This modeling effort is the first step towards understanding and designing closed loop, real-time neurostimulation devices

Améliorer les prédictions des modèles de language avec les prompts enrichies par les Graphes de Connaissances ⋆

International audienceDespite advances in deep learning and knowledge graphs (KGs), using language models for natural language understanding and question answering remains a challenging task. Pre-trained language models (PLMs) have shown to be able to leverage contextual information, to complete cloze prompts, next sentence completion and question answering tasks in various domains. Unlike structured data querying in e.g. KGs, mapping an input question to data that may or may not be stored by the language model is not a simple task. Recent studies have highlighted the improvements that can be made to the quality of information retrieved from PLMs by adding auxiliary data to otherwise naive prompts. In this paper, we explore the effects of enriching prompts with additional contextual information leveraged from the Wikidata KG on language model performance. Specifically, we compare the performance of naive vs. KG-engineered cloze prompts for entity genre classification in the movie domain. Selecting a broad range of commonly available Wikidata properties, we show that enrichment of cloze-style prompts with Wikidata information can result in a significantly higher recall for the investigated BERT and RoBERTa large PLMs. However, it is also apparent that the optimum level of data enrichment differs between models