Academic literature on the topic 'Neuroscience informed algorithm'
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Journal articles on the topic "Neuroscience informed algorithm"
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Baldassano, Steven, Drausin Wulsin, Hoameng Ung, Tyler Blevins, Mesha-Gay Brown, Emily Fox, and Brian Litt. "A novel seizure detection algorithm informed by hidden Markov model event states." Journal of Neural Engineering 13, no. 3 (April 21, 2016): 036011. http://dx.doi.org/10.1088/1741-2560/13/3/036011.
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Yazdani, Alireza, Lu Lu, Maziar Raissi, and George Em Karniadakis. "Systems biology informed deep learning for inferring parameters and hidden dynamics." PLOS Computational Biology 16, no. 11 (November 18, 2020): e1007575. http://dx.doi.org/10.1371/journal.pcbi.1007575.
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Mathematical models of biological reactions at the system-level lead to a set of ordinary differential equations with many unknown parameters that need to be inferred using relatively few experimental measurements. Having a reliable and robust algorithm for parameter inference and prediction of the hidden dynamics has been one of the core subjects in systems biology, and is the focus of this study. We have developed a new systems-biology-informed deep learning algorithm that incorporates the system of ordinary differential equations into the neural networks. Enforcing these equations effectively adds constraints to the optimization procedure that manifests itself as an imposed structure on the observational data. Using few scattered and noisy measurements, we are able to infer the dynamics of unobserved species, external forcing, and the unknown model parameters. We have successfully tested the algorithm for three different benchmark problems.
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Ford, Julian. "An Affective Cognitive Neuroscience-Based Approach to PTSD Psychotherapy: The TARGET Model." Journal of Cognitive Psychotherapy 29, no. 1 (2015): 68–91. http://dx.doi.org/10.1891/0889-8391.29.1.68.
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Adaptations or alternative versions of cognitive psychotherapy for posttraumatic stress disorder (PTSD) are needed because even the most efficacious cognitive or cognitive-behavioral psychotherapies for PTSD do not retain or achieve sustained clinically significant benefits for a majority of recipients. Cognitive affective neuroscience research is reviewed which suggests that it is not just memory (or memories) of traumatic events and related core beliefs about self, the world, and relationships that are altered in PTSD but also memory (and affective information) processing. A cognitive psychotherapy is described that was designed to systematically make explicit these otherwise implicit trauma-related alterations in cognitive emotion regulation and its application to the treatment of complex variants of PTSD—Trauma Affect Regulation: Guide for Education and Therapy (TARGET). TARGET provides therapists and clients with (a) a neurobiologically informed strengths-based meta-model of stress-related cognitive processing in the brain and how this is altered by PTSD and (b) a practical algorithm for restoring the executive functions that are necessary to make implicit trauma-related cognitions explicit (i.e., experiential awareness) and modifiable (i.e., planful refocusing). Results of randomized clinical trial studies and quasi-experimental effectiveness evaluations of TARGET with adolescents and adults are reviewed.
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Kazim, Emre, Adriano Soares Koshiyama, Airlie Hilliard, and Roseline Polle. "Systematizing Audit in Algorithmic Recruitment." Journal of Intelligence 9, no. 3 (September 17, 2021): 46. http://dx.doi.org/10.3390/jintelligence9030046.
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Business psychologists study and assess relevant individual differences, such as intelligence and personality, in the context of work. Such studies have informed the development of artificial intelligence systems (AI) designed to measure individual differences. This has been capitalized on by companies who have developed AI-driven recruitment solutions that include aggregation of appropriate candidates (Hiretual), interviewing through a chatbot (Paradox), video interview assessment (MyInterview), and CV-analysis (Textio), as well as estimation of psychometric characteristics through image-(Traitify) and game-based assessments (HireVue) and video interviews (Cammio). However, driven by concern that such high-impact technology must be used responsibly due to the potential for unfair hiring to result from the algorithms used by these tools, there is an active effort towards proving mechanisms of governance for such automation. In this article, we apply a systematic algorithm audit framework in the context of the ethically critical industry of algorithmic recruitment systems, exploring how audit assessments on AI-driven systems can be used to assure that such systems are being responsibly deployed in a fair and well-governed manner. We outline sources of risk for the use of algorithmic hiring tools, suggest the most appropriate opportunities for audits to take place, recommend ways to measure bias in algorithms, and discuss the transparency of algorithms.
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Hanlon, Colleen. "Uniting functional with structural connectivity to develop a more informed algorithm for TMS treatment in addiction: insight from TMS/fMRI, DTI, and electric field models." Brain Stimulation 16, no. 1 (January 2023): 202–3. http://dx.doi.org/10.1016/j.brs.2023.01.263.
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Xiao, Zhuo-Cheng, Kevin K. Lin, and Lai-Sang Young. "A data-informed mean-field approach to mapping of cortical parameter landscapes." PLOS Computational Biology 17, no. 12 (December 23, 2021): e1009718. http://dx.doi.org/10.1371/journal.pcbi.1009718.
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Constraining the many biological parameters that govern cortical dynamics is computationally and conceptually difficult because of the curse of dimensionality. This paper addresses these challenges by proposing (1) a novel data-informed mean-field (MF) approach to efficiently map the parameter space of network models; and (2) an organizing principle for studying parameter space that enables the extraction biologically meaningful relations from this high-dimensional data. We illustrate these ideas using a large-scale network model of the Macaque primary visual cortex. Of the 10-20 model parameters, we identify 7 that are especially poorly constrained, and use the MF algorithm in (1) to discover the firing rate contours in this 7D parameter cube. Defining a “biologically plausible” region to consist of parameters that exhibit spontaneous Excitatory and Inhibitory firing rates compatible with experimental values, we find that this region is a slightly thickened codimension-1 submanifold. An implication of this finding is that while plausible regimes depend sensitively on parameters, they are also robust and flexible provided one compensates appropriately when parameters are varied. Our organizing principle for conceptualizing parameter dependence is to focus on certain 2D parameter planes that govern lateral inhibition: Intersecting these planes with the biologically plausible region leads to very simple geometric structures which, when suitably scaled, have a universal character independent of where the intersections are taken. In addition to elucidating the geometry of the plausible region, this invariance suggests useful approximate scaling relations. Our study offers, for the first time, a complete characterization of the set of all biologically plausible parameters for a detailed cortical model, which has been out of reach due to the high dimensionality of parameter space.
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Messaritaki, Eirini, Sonya Foley, Simona Schiavi, Lorenzo Magazzini, Bethany Routley, Derek K. Jones, and Krish D. Singh. "Predicting MEG resting-state functional connectivity from microstructural information." Network Neuroscience 5, no. 2 (2021): 477–504. http://dx.doi.org/10.1162/netn_a_00187.
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Abstract Understanding how human brain microstructure influences functional connectivity is an important endeavor. In this work, magnetic resonance imaging data from 90 healthy participants were used to calculate structural connectivity matrices using the streamline count, fractional anisotropy, radial diffusivity, and a myelin measure (derived from multicomponent relaxometry) to assign connection strength. Unweighted binarized structural connectivity matrices were also constructed. Magnetoencephalography resting-state data from those participants were used to calculate functional connectivity matrices, via correlations of the Hilbert envelopes of beamformer time series in the delta, theta, alpha, and beta frequency bands. Nonnegative matrix factorization was performed to identify the components of the functional connectivity. Shortest path length and search-information analyses of the structural connectomes were used to predict functional connectivity patterns for each participant. The microstructure-informed algorithms predicted the components of the functional connectivity more accurately than they predicted the total functional connectivity. This provides a methodology to understand functional mechanisms better. The shortest path length algorithm exhibited the highest prediction accuracy. Of the weights of the structural connectivity matrices, the streamline count and the myelin measure gave the most accurate predictions, while the fractional anisotropy performed poorly. Overall, different structural metrics paint very different pictures of the structural connectome and its relationship to functional connectivity.
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Padhi, Dushmanta Kumar, Neelamadhab Padhy, Akash Kumar Bhoi, Jana Shafi, and Seid Hassen Yesuf. "An Intelligent Fusion Model with Portfolio Selection and Machine Learning for Stock Market Prediction." Computational Intelligence and Neuroscience 2022 (June 23, 2022): 1–18. http://dx.doi.org/10.1155/2022/7588303.
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Developing reliable equity market models allows investors to make more informed decisions. A trading model can reduce the risks associated with investment and allow traders to choose the best-paying stocks. However, stock market analysis is complicated with batch processing techniques since stock prices are highly correlated. In recent years, advances in machine learning have given us a lot of chances to use forecasting theory and risk optimization together. The study postulates a unique two-stage framework. First, the mean-variance approach is utilized to select probable stocks (portfolio construction), thereby minimizing investment risk. Second, we present an online machine learning technique, a combination of “perceptron” and “passive-aggressive algorithm,” to predict future stock price movements for the upcoming period. We have calculated the classification reports, AUC score, accuracy, and Hamming loss for the proposed framework in the real-world datasets of 20 health sector indices for four different geographical reasons for the performance evaluation. Lastly, we conduct a numerical comparison of our method’s outcomes to those generated via conventional solutions by previous studies. Our aftermath reveals that learning-based ensemble strategies with portfolio selection are effective in comparison.
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Dimitriadis, George, Joana P. Neto, and Adam R. Kampff. "t-SNE Visualization of Large-Scale Neural Recordings." Neural Computation 30, no. 7 (July 2018): 1750–74. http://dx.doi.org/10.1162/neco_a_01097.
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Electrophysiology is entering the era of big data. Multiple probes, each with hundreds to thousands of individual electrodes, are now capable of simultaneously recording from many brain regions. The major challenge confronting these new technologies is transforming the raw data into physiologically meaningful signals, that is, single unit spikes. Sorting the spike events of individual neurons from a spatiotemporally dense sampling of the extracellular electric field is a problem that has attracted much attention (Rey, Pedreira, & Quian Quiroga, 2015 ; Rossant et al., 2016 ) but is still far from solved. Current methods still rely on human input and thus become unfeasible as the size of the data sets grows exponentially. Here we introduce the [Formula: see text]-student stochastic neighbor embedding (t-SNE) dimensionality reduction method (Van der Maaten & Hinton, 2008 ) as a visualization tool in the spike sorting process. t-SNE embeds the [Formula: see text]-dimensional extracellular spikes ([Formula: see text] = number of features by which each spike is decomposed) into a low- (usually two-) dimensional space. We show that such embeddings, even starting from different feature spaces, form obvious clusters of spikes that can be easily visualized and manually delineated with a high degree of precision. We propose that these clusters represent single units and test this assertion by applying our algorithm on labeled data sets from both hybrid (Rossant et al., 2016 ) and paired juxtacellular/extracellular recordings (Neto et al., 2016 ). We have released a graphical user interface (GUI) written in Python as a tool for the manual clustering of the t-SNE embedded spikes and as a tool for an informed overview and fast manual curation of results from different clustering algorithms. Furthermore, the generated visualizations offer evidence in favor of the use of probes with higher density and smaller electrodes. They also graphically demonstrate the diverse nature of the sorting problem when spikes are recorded with different methods and arise from regions with different background spiking statistics.
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Tice, Alexander K., David Žihala, Tomáš Pánek, Robert E. Jones, Eric D. Salomaki, Serafim Nenarokov, Fabien Burki, et al. "PhyloFisher: A phylogenomic package for resolving eukaryotic relationships." PLOS Biology 19, no. 8 (August 6, 2021): e3001365. http://dx.doi.org/10.1371/journal.pbio.3001365.
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Phylogenomic analyses of hundreds of protein-coding genes aimed at resolving phylogenetic relationships is now a common practice. However, no software currently exists that includes tools for dataset construction and subsequent analysis with diverse validation strategies to assess robustness. Furthermore, there are no publicly available high-quality curated databases designed to assess deep (>100 million years) relationships in the tree of eukaryotes. To address these issues, we developed an easy-to-use software package, PhyloFisher (https://github.com/TheBrownLab/PhyloFisher), written in Python 3. PhyloFisher includes a manually curated database of 240 protein-coding genes from 304 eukaryotic taxa covering known eukaryotic diversity, a novel tool for ortholog selection, and utilities that will perform diverse analyses required by state-of-the-art phylogenomic investigations. Through phylogenetic reconstructions of the tree of eukaryotes and of the Saccharomycetaceae clade of budding yeasts, we demonstrate the utility of the PhyloFisher workflow and the provided starting database to address phylogenetic questions across a large range of evolutionary time points for diverse groups of organisms. We also demonstrate that undetected paralogy can remain in phylogenomic “single-copy orthogroup” datasets constructed using widely accepted methods such as all vs. all BLAST searches followed by Markov Cluster Algorithm (MCL) clustering and application of automated tree pruning algorithms. Finally, we show how the PhyloFisher workflow helps detect inadvertent paralog inclusions, allowing the user to make more informed decisions regarding orthology assignments, leading to a more accurate final dataset.
Book chapters on the topic "Neuroscience informed algorithm"
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Macruz, Andrea, Ernesto Bueno, Gustavo G. Palma, Jaime Vega, Ricardo A. Palmieri, and Tan Chen Wu. "Measuring Human Perception of Biophilically-Driven Design with Facial Micro-expressions Analysis and EEG Biosensor." In Proceedings of the 2021 DigitalFUTURES, 231–41. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5983-6_22.
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AbstractThis paper investigates the role technology and neuroscience play in aiding the design process and making meaningful connections between people and nature. Using two workshops as a vehicle, the team introduced advanced technologies and Quantified Self practices that allowed people to use neural data and pattern recognition as feedback for the design process. The objective is to find clues to natural elements of human perception that can inform the design to meet goals for well-being. A pattern network of geometric shapes that achieve a higher level of monitored meditation levels and point toward a positive emotional valence is proposed. By referencing biological forms found in nature, the workshops utilized an algorithmic process that explored how nature can influence architecture. To measure the impact, the team used FaceOSC for capture and an Artificial Neural Network for micro-expression recognition, and a MindWave sensor manufactured by NeuroSky, which documented the human response further. The methodology allowed us to establish a boundary logic, ranking geometric shapes that suggested positive emotions and a higher level of monitored meditation levels. The results pointed us to a deeper level of understanding relative to geometric shapes in design. They indicate a new way to predict how well-being factors can clarify and rationalize a more intuitive design process inspired by nature.