ORCID Profile
0000-0002-7009-3869
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Signal Processing | Simulation And Modelling | Biological Mathematics | Applied Mathematics | Simulation and Modelling | Sensory Systems | Neurosciences | Central Nervous System | Electrical and Electronic Engineering |
ANZSRC Socio-Economic Objective (SEO)
Biological sciences | Hearing, vision, speech and their disorders | Expanding Knowledge in the Information and Computing Sciences | Nervous system and disorders | Expanding Knowledge in the Biological Sciences | Expanding Knowledge in Psychology and Cognitive Sciences | Physical sciences
Related Links
Publications
Signal estimation via averaging of coarsely quantised signals
Publisher: IEEE
Date: 02-2007
Channel-noise-induced stochastic facilitation in an auditory brainstem neuron model
Publisher: American Physical Society (APS)
Date: 26-11-2013
Distribution and trajectories of floating and benthic marine macrolitter in the south-eastern North Sea
Publisher: Elsevier BV
Date: 06-2018
DOI: 10.1016/J.MARPOLBUL.2018.05.003
Abstract: In coastal waters the identification of sources, trajectories and deposition sites of marine litter is often h ered by the complex oceanography of shallow shelf seas. We conducted a multi-annual survey on litter at the sea surface and on the seafloor in the south-eastern North Sea. Bottom trawling was identified as a major source of marine litter. Oceanographic modelling revealed that the distribution of floating litter in the North Sea is largely determined by the site of origin of floating objects whereas the trajectories are strongly influenced by wind drag. Methods adopted from species distribution modelling indicated that resuspension of benthic litter and near-bottom transport processes strongly influence the distribution of litter on the seafloor. Major sink regions for floating marine litter were identified at the west coast of Denmark and in the Skagerrak. Our results may support the development of strategies to reduce the pollution of the North Sea.
Generalized noise resonance: Using noise for signal enhancement
Publisher: SPIE
Date: 25-05-2004
DOI: 10.1117/12.552953
Optimal sensor selection for noisy binary detection in stochastic pooling networks
Publisher: American Physical Society (APS)
Date: 12-08-2013
Stochastic resonance and data processing inequality
Publisher: Institution of Engineering and Technology (IET)
Date: 2003
DOI: 10.1049/EL:20030792
Analog to digital conversion using suprathreshold stochastic resonance
Publisher: SPIE
Date: 28-02-2005
DOI: 10.1117/12.582493
Optimal sigmoidal tuning curves for intensity encoding sensory neurons with quasi-Poisson variability
Publisher: Springer Science and Business Media LLC
Date: 07-2008
Acoustic scene classification using deep residual networks with late fusion of separated high and low frequency paths
Publisher: IEEE
Date: 05-2020
M-ary suprathreshold stochastic resonance: Generalization and scaling beyond binary threshold nonlinearities
Publisher: IOP Publishing
Date: 12-2014
Engineering intelligent electronic systems based on computational neuroscience [scanning the issue]
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2014
The role of stochasticity in an information-optimal neural population code
Publisher: IOP Publishing
Date: 12-2009
Is electrical noise useful?
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2011
Distributed bandpass filtering and signal demodulation in cortical network models
Publisher: Springer International Publishing
Date: 14-12-2014
An introductory review of information theory in the context of computational neuroscience
Publisher: Springer Science and Business Media LLC
Date: 07-2011
DOI: 10.1007/S00422-011-0451-9
Abstract: This article introduces several fundamental concepts in information theory from the perspective of their origins in engineering. Understanding such concepts is important in neuroscience for two reasons. Simply applying formulae from information theory without understanding the assumptions behind their definitions can lead to erroneous results and conclusions. Furthermore, this century will see a convergence of information theory and neuroscience information theory will expand its foundations to incorporate more comprehensively biological processes thereby helping reveal how neuronal networks achieve their remarkable information processing abilities.
Maximising information transfer through nonlinear noisy devices
Publisher: SPIE
Date: 11-2002
DOI: 10.1117/12.469415
Enabling 'question answering' in the MBAT vector symbolic architecture by exploiting orthogonal random matrices
Publisher: IEEE
Date: 06-2014
DOI: 10.1109/ICSC.2014.38
A unified account of perceptual layering and surface appearance in terms of gamut relativity
Publisher: Public Library of Science (PLoS)
Date: 17-11-2014
Using style-transfer to understand material classification for robotic sorting of recycled beverage containers
Publisher: IEEE
Date: 12-2019
Communication of uncoded sensor measurements through nanoscale binary-node stochastic pooling networks
Publisher: Elsevier BV
Date: 09-2010
Optimal quantization and suprathreshold stochastic resonance
Publisher: SPIE
Date: 23-05-2005
DOI: 10.1117/12.609542
A deep convolutional neural network for segmentation of whole-slide pathology images identifies novel tumour cell-perivascular niche interactions that are associated with poor survival in glioblastoma
Publisher: Springer Science and Business Media LLC
Date: 29-04-2021
DOI: 10.1038/S41416-021-01394-X
Abstract: Glioblastoma is the most aggressive type of brain cancer with high-levels of intra- and inter-tumour heterogeneity that contribute to its rapid growth and invasion within the brain. However, a spatial characterisation of gene signatures and the cell types expressing these in different tumour locations is still lacking. We have used a deep convolutional neural network (DCNN) as a semantic segmentation model to segment seven different tumour regions including leading edge (LE), infiltrating tumour (IT), cellular tumour (CT), cellular tumour microvascular proliferation (CTmvp), cellular tumour pseudopalisading region around necrosis (CTpan), cellular tumour perinecrotic zones (CTpnz) and cellular tumour necrosis (CTne) in digitised glioblastoma histopathological slides from The Cancer Genome Atlas (TCGA). Correlation analysis between segmentation results from tumour images together with matched RNA expression data was performed to identify genetic signatures that are specific to different tumour regions. We found that spatially resolved gene signatures were strongly correlated with survival in patients with defined genetic mutations. Further in silico cell ontology analysis along with single-cell RNA sequencing data from resected glioblastoma tissue s les showed that these tumour regions had different gene signatures, whose expression was driven by different cell types in the regional tumour microenvironment. Our results further pointed to a key role for interactions between microglia ericytes/monocytes and tumour cells that occur in the IT and CTmvp regions, which may contribute to poor patient survival. This work identified key histopathological features that correlate with patient survival and detected spatially associated genetic signatures that contribute to tumour-stroma interactions and which should be investigated as new targets in glioblastoma. The source codes and datasets used are available in GitHub: min20/GBM_WSSM .
Regularized training of the extreme learning machine using the conjugate gradient method
Publisher: IEEE
Date: 05-2017
Characterization of young and old adult brains: An EEG functional connectivity analysis
Publisher: Cold Spring Harbor Laboratory
Date: 13-12-2018
DOI: 10.1101/495564
Abstract: Brain connectivity studies have reported that functional networks change with older age. We aim to (1) investigate whether electroencephalography (EEG) data can be used to distinguish between in idual functional networks of young and old adults and (2) identify the functional connections that contribute to this classification. Two eyes-open resting-state EEG recording sessions with 64 electrodes for each of 22 younger adults (19-37 years) and 22 older adults (63-85 years) were conducted. For each session, imaginary coherence matrices in theta, alpha, beta and gamma bands were computed. A range of machine learning classification methods were utilized to distinguish younger and older adult brains. A support vector machine (SVM) classifier was 94% accurate in classifying the brains by age group. We report decreased functional connectivity with older age in theta, alpha and gamma bands, and increased connectivity with older age in beta band. Most connections involving frontal, temporal, and parietal electrodes, and approximately two-thirds of connections involving occipital electrodes, showed decreased connectivity with older age. Just over half of the connections involving central electrodes showed increased connectivity with older age. Functional connections showing decreased strength with older age had significantly longer electrode-to-electrode distance than those that increased with older age. Most of the connections used by the classifier to distinguish participants by age group belonged to the alpha band. Findings suggest a decrease in connectivity in key networks and frequency bands associated with attention and awareness, and an increase in connectivity of the sensorimotor functional networks with ageing during a resting state.
High resolution optimal quantization for stochastic pooling networks
Publisher: SPIE
Date: 27-12-2007
DOI: 10.1117/12.695984
Natural moment-to-moment signal variability and stochastic facilitation
Publisher: Springer Science and Business Media LLC
Date: 07-09-2011
DOI: 10.1038/NRN3061-C2
Signal acquisition via polarization modulation in single photon sources
Publisher: American Physical Society (APS)
Date: 08-12-2009
Optimal information transmission in nonlinear arrays through suprathreshold stochastic resonance
Publisher: Elsevier BV
Date: 03-2006
What is stochastic resonance? Definitions, misconceptions, debates, and its relevance to biology
Publisher: Public Library of Science (PLoS)
Date: 29-05-2009
An Analytical Optimisation Framework for Airport Terminal Capacity Expansion
Publisher: Hindawi Limited
Date: 10-2020
DOI: 10.1155/2020/2976281
Abstract: This article considers how to allocate additional physical resources within airport terminals. An optimization model was developed to determine where additional resources should be placed to minimise passenger waiting times. The objective function is stochastic and can only be evaluated using discrete event simulation. As this model is stochastic and nonlinear, a Simulated Annealing (SA) metaheuristic was implemented and tested. The SA algorithm repeatedly perturbs a resource allocation solution using one of two methods. The first method is creating new solution randomly in each iteration, and the second method is local search that is mimicked by any move of the current solution of x solution chosen randomly in its neighborhood. Numerical testing shows that the random approach is best, and solutions that are 12.11% better can be obtained.
A neurobiologically plausible vector symbolic architecture
Publisher: IEEE
Date: 06-2014
DOI: 10.1109/ICSC.2014.40
Phase changes in neuronal postsynaptic spiking due to short term plasticity
Publisher: Public Library of Science (PLoS)
Date: 22-09-2017
A Kuramoto coupling of quasi-cycle oscillators with application to neural networks
Publisher: American Scientific Publishers
Date: 04-2016
Degradation of Performance in Reinforcement Learning with State Measurement Uncertainty
Publisher: IEEE
Date: 11-2019
Maximally informative stimuli and tuning curves for sigmoidal rate-coding neurons and populations
Publisher: American Physical Society (APS)
Date: 08-2008
Single-Bit-per-Weight Deep Convolutional Neural Networks without Batch-Normalization Layers for Embedded Systems
Publisher: IEEE
Date: 07-2019
Modeling Electrode Place Discrimination in Cochlear Implant Stimulation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2017
M-ary suprathreshold stochastic resonance in multilevel threshold systems with signal-dependent noise
Publisher: Elsevier BV
Date: 08-2017
Understanding Data Augmentation for Classification: When to Warp?
Publisher: IEEE
Date: 11-2016
Signal compression in biological sensory systems: Information theoretic performance limits
Publisher: SPIE
Date: 21-12-2008
DOI: 10.1117/12.759225
Enhanced image classification with a fast-learning shallow convolutional neural network
Publisher: IEEE
Date: 07-2015
The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey
Publisher: MDPI AG
Date: 12-11-2020
DOI: 10.3390/JPM10040224
Abstract: In recent years, improved deep learning techniques have been applied to biomedical image processing for the classification and segmentation of different tumors based on magnetic resonance imaging (MRI) and histopathological imaging (H& E) clinical information. Deep Convolutional Neural Networks (DCNNs) architectures include tens to hundreds of processing layers that can extract multiple levels of features in image-based data, which would be otherwise very difficult and time-consuming to be recognized and extracted by experts for classification of tumors into different tumor types, as well as segmentation of tumor images. This article summarizes the latest studies of deep learning techniques applied to three different kinds of brain cancer medical images (histology, magnetic resonance, and computed tomography) and highlights current challenges in the field for the broader applicability of DCNN in personalized brain cancer care by focusing on two main applications of DCNNs: classification and segmentation of brain cancer tumors images.
How to use noise to reduce complexity in quantization
Publisher: SPIE
Date: 28-12-2006
DOI: 10.1117/12.638476
Optimal quantization for energy-efficient information transfer in a population of neuron-like devices
Publisher: SPIE
Date: 25-05-2004
DOI: 10.1117/12.546934
Framework for Airport Outbound Passenger Flow Modelling
Publisher: Elsevier BV
Date: 2017
Automated Adjustment of PPE Masks Using IoT Sensor Fusion
Publisher: MDPI AG
Date: 03-02-2023
DOI: 10.3390/S23031711
Abstract: The COVID-19 pandemic has led to a dramatic increase in the use of PPE by the general public as well as health professionals. Scientists and health organizations have developed measures to protect people and minimize the catastrophic outcomes of COVID, including social distancing, frequent and periodic sanitizing, vaccinations, protective coverings, and face masks. During this time, the usage of protective face masks has increased dramatically. A mask only provides full safety to the user if it is a proper fit on their face. The aim of this paper is to automatically analyze and improve the fit of a face mask using IoT sensors. This paper describes the creation of a 3D-printed smart face mask that uses sensors to determine the current mask fit and then automatically tightens mask straps. This is evaluated using adjustment response time and the quality of fit achieved using the automatic adjustment approach with a range of sensor types.
Neural mechanisms for analog to digital conversion
Publisher: SPIE
Date: 29-03-2004
DOI: 10.1117/12.523165
SNDR enhancement in noisy sinusoidal signals by non-linear processing elements
Publisher: SPIE
Date: 07-06-2007
DOI: 10.1117/12.724688
Optimal stimulus and noise distributions for information transmission via suprathreshold stochastic resonance
Publisher: American Physical Society (APS)
Date: 06-06-2007
The data processing inequality and stochastic resonance
Publisher: SPIE
Date: 08-05-2003
DOI: 10.1117/12.496992
Optimal weighted suprathreshold stochastic resonance with multigroup saturating sensors
Publisher: Elsevier BV
Date: 09-2016
The benefits of noise in neural systems: Bridging theory and experiment
Publisher: Springer Science and Business Media LLC
Date: 20-06-2011
DOI: 10.1038/NRN3061
Abstract: Although typically assumed to degrade performance, random fluctuations, or noise, can sometimes improve information processing in non-linear systems. One such form of 'stochastic facilitation', stochastic resonance, has been observed to enhance processing both in theoretical models of neural systems and in experimental neuroscience. However, the two approaches have yet to be fully reconciled. Understanding the erse roles of noise in neural computation will require the design of experiments based on new theory and models, into which biologically appropriate experimental detail feeds back at various levels of abstraction.
Motif-role-fingerprints: the building-blocks of motifs, clustering-coefficients and transitivities in directed networks.
Publisher: Public Library of Science (PLoS)
Date: 08-12-2014
A model of neurobiologically plausible least-squares learning in visual cortex
Publisher: IEEE
Date: 07-2018
Thai handwritten recognition on text block-based from thai archive manuscripts
Publisher: IEEE
Date: 09-2019
Transmit pulse shaping for molecular communications
Publisher: IEEE
Date: 04-2014
A model of the effects of authority on consensus formation in adaptive networks: Impact on network topology and robustness
Publisher: Elsevier BV
Date: 02-2013
Ion channel noise can explain firing correlation in auditory nerves
Publisher: Springer Science and Business Media LLC
Date: 02-08-2016
DOI: 10.1007/S10827-016-0613-9
Abstract: Neural spike trains are commonly characterized as a Poisson point process. However, the Poisson assumption is a poor model for spiking in auditory nerve fibres because it is known that interspike intervals display positive correlation over long time scales and negative correlation over shorter time scales. We have therefore developed a biophysical model based on the well-known Meddis model of the peripheral auditory system, to produce simulated auditory nerve fibre spiking statistics that more closely match the firing correlations observed in empirical data. We achieve this by introducing biophysically realistic ion channel noise to an inner hair cell membrane potential model that includes fractal fast potassium channels and deterministic slow potassium channels. We succeed in producing simulated spike train statistics that match empirically observed firing correlations. Our model thus replicates macro-scale stochastic spiking statistics in the auditory nerve fibres due to modeling stochasticity at the micro-scale of potassium channels.
Sensor selection for distributed detection via multiaccess channels
Publisher: IEEE
Date: 02-2009
Effect of network topology in opinion formation models
Publisher: Springer Berlin Heidelberg
Date: 2011
Performance of a hierarchical temporal memory network in noisy sequence learning
Publisher: IEEE
Date: 12-2013
Information capacity of stochastic pooling networks is achieved by discrete inputs
Publisher: American Physical Society (APS)
Date: 06-04-2009
Mathematical analysis and algorithms for efficiently and accurately implementing stochastic simulations of short-term synaptic depression and facilitation
Publisher: Frontiers Media SA
Date: 2013
Modeling the influence of short term depression in vesicle release and stochastic calcium channel gating on auditory nerve spontaneous firing statistics.
Publisher: Frontiers Media SA
Date: 23-12-2014
End-to-end phoneme recognition using models from semantic image segmentation
Publisher: IEEE
Date: 07-2020
Optimal coding of a random stimulus by a population of parallel neuron models
Publisher: SPIE
Date: 07-06-2007
DOI: 10.1117/12.724618
The Impact of Pan-Sharpening and Spectral Resolution on Vineyard Segmentation through Machine Learning
Publisher: MDPI AG
Date: 13-03-2020
DOI: 10.3390/RS12060934
Abstract: Precision viticulture benefits from the accurate detection of vineyard vegetation from remote sensing, without a priori knowledge of vine locations. Vineyard detection enables efficient, and potentially automated, derivation of spatial measures such as length and area of crop, and hence required volumes of water, fertilizer, and other resources. Machine learning techniques have provided significant advancements in recent years in the areas of image segmentation, classification, and object detection, with neural networks shown to perform well in the detection of vineyards and other crops. However, what has not been extensively quantitatively examined is the extent to which the initial choice of input imagery impacts detection/segmentation accuracy. Here, we use a standard deep convolutional neural network (CNN) to detect and segment vineyards across Australia using DigitalGlobe Worldview-2 images at ∼50 cm (panchromatic) and ∼2 m (multispectral) spatial resolution. A quantitative assessment of the variation in model performance with input parameters during model training is presented from a remote sensing perspective, with combinations of panchromatic, multispectral, pan-sharpened multispectral, and the spectral Normalised Difference Vegetation Index (NDVI) considered. The impact of image acquisition parameters—namely, the off-nadir angle and solar elevation angle—on the quality of pan-sharpening is also assessed. The results are synthesised into a ‘recipe’ for optimising the accuracy of vineyard segmentation, which can provide a guide to others aiming to implement or improve automated crop detection and classification.
Stochastic information transfer from cochlear implant electrodes to auditory nerve fibers
Publisher: American Physical Society (APS)
Date: 29-08-2014
How can “Super Corals” facilitate global coral reef survival under rapid environmental and climatic change?
Publisher: Wiley
Date: 27-04-2018
DOI: 10.1111/GCB.14153
Abstract: Coral reefs are in a state of rapid global decline via environmental and climate change, and efforts have intensified to identify or engineer coral populations with increased resilience. Concurrent with these efforts has been increasing use of the popularized term "Super Coral" in both popular media and scientific literature without a unifying definition. However, how this subjective term is currently applied has the potential to mislead inference over factors contributing to coral survivorship, and the future trajectory of coral reef form and functioning. Here, we discuss that the information required to support a single definition does not exist, and in fact may never be appropriate, i.e. "How Super is Super"? Instead, we advocate caution of this term, and suggest a workflow that enables contextualization and clarification of superiority to ensure that inferred or asserted survivorship is appropriate into future reef projections. This is crucial to robustly unlock how "Super Corals" can be integrated into the suite of management options required to facilitate coral survival under rapid environmental and climate change.
Editorial: Neuronal Stochastic Variability: Influences on Spiking Dynamics and Network Activity
Publisher: Frontiers Media SA
Date: 21-04-2016
Information theoretic inference of the optimal number of electrodes for future cochlear implants using a spiral cochlea model
Publisher: IEEE
Date: 08-2012
Systemic testing on bradley-terry model against nonlinear ranking hierarchy
Publisher: Public Library of Science (PLoS)
Date: 22-12-2014
Reliable communication and sensing via parallel redundancy in noisy digital receivers
Publisher: IEEE
Date: 2008
Methods for generating complex networks with selected structural properties for simulations: A review and tutorial for neuroscientists
Publisher: Frontiers Media SA
Date: 2011
Deep extreme learning machines: Supervised autoencoding architecture for classification
Publisher: Elsevier BV
Date: 2016
Suprathreshold stochastic resonance
Publisher: Scholarpedia
Date: 2009
Dynamics of gamma bursts in local field potentials
Publisher: MIT Press - Journals
Date: 2015
DOI: 10.1162/NECO_A_00688
Abstract: In this letter, we provide a stochastic analysis of, and supporting simulation data for, a stochastic model of the generation of gamma bursts in local field potential (LFP) recordings by interacting populations of excitatory and inhibitory neurons. Our interest is in behavior near a fixed point of the stochastic dynamics of the model. We apply a recent limit theorem of stochastic dynamics to probe into details of this local behavior, obtaining several new results. We show that the stochastic model can be written in terms of a rotation multiplied by a two-dimensional standard Ornstein-Uhlenbeck (OU) process. Viewing the rewritten process in terms of phase and litude processes, we are able to proceed further in analysis. We demonstrate that gamma bursts arise in the model as excursions of the modulus of the OU process. The associated pair of stochastic phase and litude processes satisfies their own pair of stochastic differential equations, which indicates that large phase slips occur between gamma bursts. This behavior is mirrored in LFP data simulated from the original model. These results suggest that the rewritten model is a valid representation of the behavior near the fixed point for a wide class of models of oscillatory neural processes.
Information theoretic optimization of cochlear implant electrode usage probabilities
Publisher: IEEE
Date: 07-2013
Distance distributions for real cellular networks
Publisher: IEEE
Date: 04-2014
Enhancing deep extreme learning machines by error backpropagation
Publisher: IEEE
Date: 07-2016
Interaction of short-term depression and firing dynamics in shaping single neuron encoding
Publisher: Frontiers Media SA
Date: 2013
Analysis of Gradient Degradation and Feature Map Quality in Deep All-Convolutional Neural Networks Compared to Deep Residual Networks
Publisher: Springer International Publishing
Date: 2017
Input-rate modulation of gamma oscillations is sensitive to network topology, delays and short-term plasticity
Publisher: Elsevier BV
Date: 2012
DOI: 10.1016/J.BRAINRES.2011.08.070
Abstract: Simulated networks of excitatory and inhibitory neurons have previously been shown to reproduce critical features of experimental data regarding neural coding in V1, such as a positive relationship between thalamic input spike rate and the power of gamma frequency oscillations. This effect, referred to as modulated gamma power, could represent a neural code in V1 for stimulus characteristics that affect thalamic spike rate such as contrast or intensity. The simulated network's assumptions included homogeneous random connectivity, equal synaptic delays after spike arrival, and constant synaptic efficacies. Plausible alternative assumptions include small world connectivity, a wide distribution of axonal propagation delays, and short term synaptic plasticity, and here we assess the in idual impact of each of these on the model's success in reproducing modulated gamma power. First, we developed several alternative algorithms for simulating directed networks with clustered connectivity and balanced excitation and inhibition. We found that modulated gamma power was absent in all small-world networks that had a relatively low abundance of reciprocal connectivity, which suggests that such motifs are present in V1 cortical networks at levels at least equal to those found in random networks. We also found in a different network type that the balance of excitation and inhibition could be destroyed when the network was in the small-world regime. Given all neurons had identical in-degrees, this result suggests that balance relies on motif distributions as well as mean connectivity. Second, altering the distribution of axonal delays had little effect, but increasing the mean delay led to a secondary gamma modulation at harmonics of the main peak, and since this is not observed experimentally, it suggests a mean delay in V1 networks less than 2 ms. Finally, we compared two types of excitatory synaptic plasticity, and found that modulated beta power emerged in addition to gamma power for one type, in the presence of short term depression in interneurons. This article is part of a Special Issue entitled "Neural Coding".
Neural information transfer in a noisy environment
Publisher: SPIE
Date: 21-11-2001
DOI: 10.1117/12.449175
Optimising threshold levels for information transmission in binary threshold networks: Independent multiplicative noise on each threshold
Publisher: Elsevier BV
Date: 02-2015
An analysis of noise enhanced information transmission in an array of comparators
Publisher: Elsevier BV
Date: 12-2002
Performance of macro-scale molecular communications with sensor cleanse time
Publisher: IEEE
Date: 05-2014
Modeling electrode place discrimination in cochlear implants: Analysis of the influence of electrode array insertion depth
Publisher: IEEE
Date: 04-2015
Learned filters for object detection in multi-object visual tracking
Publisher: SPIE
Date: 12-05-2016
DOI: 10.1117/12.2225200
Stochastic resonance: From suprathreshold stochastic resonance to stochastic signal quantization
Publisher: Cambridge University Press
Date: 02-10-2008
Abstract: Stochastic resonance has been observed in many forms of systems, and has been hotly debated by scientists for over 30 years. Applications incorporating aspects of stochastic resonance may yet prove revolutionary in fields such as distributed sensor networks, nano-electronics, and biomedical prosthetics. Ideal for researchers in fields ranging from computational neuroscience through to electronic engineering, this book addresses in detail various theoretical aspects of stochastic quantization, in the context of the suprathreshold stochastic resonance effect. Initial chapters review stochastic resonance and outline some of the controversies and debates that have surrounded it. The book then discusses suprathreshold stochastic resonance, and its extension to more general models of stochastic signal quantization. Finally, it considers various constraints and tradeoffs in the performance of stochastic quantizers, before culminating with a chapter in the application of suprathreshold stochastic resonance to the design of cochlear implants.
Cross-spectral measurement of neural signal transfer
Publisher: SPIE
Date: 25-05-2004
DOI: 10.1117/12.546926
Modern Value Based Reinforcement Learning: A Chronological Review
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Bio-inspired communication: Performance limits for information transmission and compression in stochastic pooling networks with binary quantizing nodes
Publisher: American Scientific Publishers
Date: 05-2010
Adaptive recursive algorithm for optimal weighted suprathreshold stochastic resonance
Publisher: The Royal Society
Date: 09-2017
DOI: 10.1098/RSOS.160889
Abstract: Suprathreshold stochastic resonance (SSR) is a distinct form of stochastic resonance, which occurs in multilevel parallel threshold arrays with no requirements on signal strength. In the generic SSR model, an optimal weighted decoding scheme shows its superiority in minimizing the mean square error (MSE). In this study, we extend the proposed optimal weighted decoding scheme to more general input characteristics by combining a Kalman filter and a least mean square (LMS) recursive algorithm, wherein the weighted coefficients can be adaptively adjusted so as to minimize the MSE without complete knowledge of input statistics. We demonstrate that the optimal weighted decoding scheme based on the Kalman–LMS recursive algorithm is able to robustly decode the outputs from the system in which SSR is observed, even for complex situations where the signal and noise vary over time.
Neural Population Coding Is Optimized by Discrete Tuning Curves
Publisher: American Physical Society (APS)
Date: 22-09-2009
Gain from the two-envelope problem via information asymmetry: on the suboptimality of randomized switching
Publisher: The Royal Society
Date: 11-05-2011
Abstract: The two-envelope problem (or exchange problem) is one of maximizing the payoff in choosing between two values, given an observation of only one. This paradigm is of interest in a range of fields from engineering to mathematical finance, as it is now known that the payoff can be increased by exploiting a form of information asymmetry. Here, we consider a version of the ‘two-envelope game’ where the envelopes’ contents are governed by a continuous positive random variable. While the optimal switching strategy is known and deterministic once an envelope has been opened, it is not necessarily optimal when the content's distribution is unknown. A useful alternative in this case may be to use a switching strategy that depends randomly on the observed value in the opened envelope. This approach can lead to a gain when compared with never switching. Here, we quantify the gain owing to such conditional randomized switching when the random variable has a generalized negative exponential distribution, and compare this to the optimal switching strategy. We also show that a randomized strategy may be advantageous when the distribution of the envelope's contents is unknown, since it can always lead to a gain.
Extreme westward surface drift in the North Sea: Public reports of stranded drifters and Lagrangian tracking
Publisher: Elsevier BV
Date: 04-2019
A review of methods for identifying stochastic resonance in simulations of single neuron models
Publisher: Informa UK Limited
Date: 11-03-2015
Efficient computation of the Levenberg-Marquardt algorithm for feedforward networks with linear outputs
Publisher: IEEE
Date: 07-2016
Modular expansion of the hidden layer in Single Layer Feedforward neural Networks
Publisher: IEEE
Date: 07-2016
A Framework for Sharing Staff between Outbound and Inbound Airport Processes
Publisher: MDPI AG
Date: 02-06-2020
DOI: 10.3390/MATH8060895
Abstract: This paper proposes an advanced simulation-optimization approach to evaluate and optimize the passenger flows within international airports. This approach allocates resources intelligently during the simulation process and balances demand and service quality. The resource allocation performed by our Advanced Resource Management (ARM) algorithm was used to develop an integrated system for arranging resources, identifying the proper resources, and allocating them throughout the model. It was used to investigate the influences of different staff allocation techniques on the inbound and outbound processes of an airport terminal. The purpose of the proposed simulation-optimization approach is to enhance passenger satisfaction through ensuring reasonable wait times during processing at the lowest cost possible (minimal staff hours).
Mathematical methods for spatially cohesive reserve design
Publisher: Springer Science and Business Media LLC
Date: 2002
Metabolic cost of neuronal information in an empirical stimulus-response model
Publisher: Springer Science and Business Media LLC
Date: 07-03-2013
DOI: 10.1007/S00422-013-0554-6
Abstract: The limits on maximum information that can be transferred by single neurons may help us to understand how sensory and other information is being processed in the brain. According to the efficient-coding hypothesis (Barlow, Sensory Comunication, MIT press, Cambridge, 1961), neurons are adapted to the statistical properties of the signals to which they are exposed. In this paper we employ methods of information theory to calculate, both exactly (numerically) and approximately, the ultimate limits on reliable information transmission for an empirical neuronal model. We couple information transfer with the metabolic cost of neuronal activity and determine the optimal information-to-metabolic cost ratios. We find that the optimal input distribution is discrete with only six points of support, both with and without a metabolic constraint. However, we also find that many different input distributions achieve mutual information close to capacity, which implies that the precise structure of the capacity-achieving input is of lesser importance than the value of capacity.
Using noise to break the noise barrier in circuits
Publisher: SPIE
Date: 28-02-2005
DOI: 10.1117/12.582597
Randomized switching in the two-envelope problem
Publisher: The Royal Society
Date: 05-08-2009
Abstract: The two-envelope problem is a conundrum in decision theory that is subject to longstanding debate. It is a counterintuitive problem of decidability between two different states, in the presence of uncertainty, where a player’s payoff must be maximized in some fashion. The problem is a significant one as it impacts on our understanding of probability theory, decision theory and optimization. It is timely to revisit this problem, as a number of related two-state switching phenomena are emerging in physics, engineering and economics literature. In this paper, we discuss this wider significance, and offer a new approach to the problem. For the first time, we analyse the problem by adopting Cover’s switching strategy—this is where we randomly switch states with a probability that is a smoothly decreasing function of the observed value of one state. Surprisingly, we show that the player’s payoff can be increased by this strategy. We also extend the problem to show that a deterministic switching strategy, based on a thresholded decision once the amount in an envelope is observed, is also workable.
Inferring the dynamic range of electrode current by using an information theoretic model of cochlear implant stimulation
Publisher: IEEE
Date: 11-2014
Integrating convolutional neural networks into a sparse distributed representation model based on mammalian cortical learning
Publisher: IEEE
Date: 07-2016
Semi-supervised convolutional extreme learning machine
Publisher: IEEE
Date: 05-2017
Erratum: Channel-noise-induced stochastic facilitation in an auditory brainstem neuron model (Physical Review E (2013) 88 (052722))
Publisher: American Physical Society (APS)
Date: 13-12-2013
Applying stochastic signal quantization theory to the robust digitization of noisy analog signals
Publisher: Springer Berlin Heidelberg
Date: 2009
Stochastic pooling networks: A biologically inspired model for robust signal detection and compression
Publisher: IEEE
Date: 09-2008
Downlink interference estimation without feedback for heterogeneous network interference avoidance
Publisher: IEEE
Date: 05-2014
Fast, simple and accurate handwritten digit classification by training shallow neural network classifiers with the 'Extreme learning machine' algorithm
Publisher: Public Library of Science (PLoS)
Date: 11-08-2015
Too good to be true: when overwhelming evidence fails to convince
Publisher: The Royal Society
Date: 03-2016
Abstract: Is it possible for a large sequence of measurements or observations, which support a hypothesis, to counterintuitively decrease our confidence? Can unanimous support be too good to be true? The assumption of independence is often made in good faith however, rarely is consideration given to whether a systemic failure has occurred. Taking this into account can cause certainty in a hypothesis to decrease as the evidence for it becomes apparently stronger. We perform a probabilistic Bayesian analysis of this effect with ex les based on (i) archaeological evidence, (ii) weighing of legal evidence and (iii) cryptographic primality testing. In this paper, we investigate the effects of small error rates in a set of measurements or observations. We find that even with very low systemic failure rates, high confidence is surprisingly difficult to achieve in particular, we find that certain analyses of cryptographically important numerical tests are highly optimistic, underestimating their false-negative rate by as much as a factor of 2 80 .
Impact of passenger-arrival patterns in outbound processes of airports
Publisher: Elsevier BV
Date: 2019
Identifying positive roles for endogenous stochastic noise during computation in neural systems
Publisher: IEEE
Date: 07-2013
Decoding suprathreshold stochastic resonance with optimal weights
Publisher: Elsevier BV
Date: 10-2015
Diagnosing convolutional neural networks using their spectral response
Publisher: IEEE
Date: 12-2018
Predicting financial well-being using observable features and gradient boosting
Publisher: Springer International Publishing
Date: 2019
Nest-site competition between invasive and native cavity nesting birds and its implication for conservation.
Publisher: Elsevier BV
Date: 10-2016
DOI: 10.1016/J.JENVMAN.2016.06.021
Abstract: Nesting cavities are often a limited resource that multiple species use. There is an ongoing discussion on whether invasive cavity nesting birds restrict the availability of this key limited resource. While the answer to this question has important conservation implications, little experimental work has been done to examine it. Here, we aimed to experimentally test whether alien cavity nesting birds affect the occupancy of cavities and the resulting breeding success of native cavity breeders in a large urban park located in Tel Aviv, Israel. Over three breeding seasons, we manipulated the entry size of nest boxes and compared the occupancy and breeding success of birds in nest boxes of two treatments. These included nest boxes with large-entrance and small-entrance holes. The large-entrance holes allowed access for both the native and invasive birds (the two main aliens in the park are the common mynas and rose-ringed parakeets). The smaller-entrance boxes, on the other hand, allowed only the smaller sized native cavity breeders (great tits and house sparrows) to enter the boxes but prevented the alien species from entering. We found that the large-entrance nest boxes were occupied by five different bird species, comprising three natives (great tit, house sparrow, Scops owl) and two invasive species (common myna, rose-ringed parakeet) while the small-entrance boxes were only occupied by the two native species. The alien common mynas and rose-ringed parakeets occupied 77.5% of the large-entrance nest boxes whereas native species, mainly great tits, occupied less than 9% of the large-entrance boxes and 36.5% of the small-entrance boxes. When examining the occupancy of those cavities that were not occupied by the aliens, natives occupied both the small and large-entrance nest boxes equally. Three quarters (78%) of the great tits breeding in the large-entrance boxes were usurped by common mynas during the breeding season and as a result breeding success was significantly lower for great tits breeding in the large-entrance boxes compared with the small-entrance boxes. The results of this study suggests that the invasive alien species can reduce the breeding potential of native cavity breeders both by exploiting the limited breeding resource (nest cavities) and by directly usurping cavities already occupied by the native species. Since the majority of large-entrance nest boxes were occupied by the larger alien birds, less native species bred in the limited number of unoccupied large-entrance nest boxes because of exploitation competition. We propose that for management purposes, nest-box programs that alter the entrance size of available natural cavities may be a practical approach, reducing the competition between native cavity breeders and alien invasive birds, and especially benefiting the smaller native cavity breeders.
Contribution of oxytocin receptor polymorphisms to amygdala activation in schizophrenia spectrum disorders
Publisher: Royal College of Psychiatrists
Date: 11-2016
DOI: 10.1192/BJPO.BP.116.003376
Abstract: Oxytocin has been proposed to mediate amygdala dysfunction associated with altered emotion processing in schizophrenia, but the contribution of oxytocin pathway genes is yet to be investigated. To identify potential different contributions of three oxytocin receptor polymorphisms (rs53576, rs237902 and rs2254298) between patients with schizophrenia spectrum disorders (SCZ), affective spectrum disorders (AD) and healthy controls (HC). In a total of 346 participants (104 with SCZ, 100 with AD, and 142 HC) underwent genotyping and functional magnetic resonance imaging (fMRI) during an emotional faces matching paradigm. Genetic association analyses were performed to test the possible effects on task-induced BOLD amygdala response to fearful/angry faces. In participants with SCZ, the rs237902 G allele was associated with low amygdala activation (left hemisphere: b = −4.99, Bonferroni corrected P =0.04) and interaction analyses showed that this association was disorder specific (left hemisphere: Bonferroni corrected P =0.003 right hemisphere: Bonferroni corrected P =0.03). There were no associations between oxytocin polymorphisms and amygdala activation in the total s le, among AD patients or HC. Rs237902 was associated with amygdala activation in response to fearful/angry faces only in patients with SCZ. Our findings indicate that the endogenous oxytocin system could serve as a contributing factor in biological underpinnings of emotion processing and that this contribution is disorder specific.
Using convex optimization to compute channel capacity in a channel model of cochlear implant stimulation
Publisher: IEEE
Date: 06-2014
26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3
Publisher: Springer Science and Business Media LLC
Date: 08-2017
Quantization in the presence of large amplitude threshold noise
Publisher: World Scientific Pub Co Pte Lt
Date: 09-2005
DOI: 10.1142/S0219477505002884
Abstract: Signal quantization in the presence of independent, identically distributed, large litude threshold noise is examined. It has previously been shown that when all quantization thresholds are set to the same value, this situation exhibits a form of stochastic resonance known as suprathreshold stochastic resonance. This means the optimal quantizer performance occurs for a small input signal-to-noise ratio. Here we examine the performance of this stochastic quantization in terms of both mutual information and mean square error distortion. It is also shown that for low input signal-to-noise ratios that the case of all thresholds being identical provides the optimal mean square error distortion performance for the given noise conditions.
Pooling networks for a discrimination task: Noise-enhanced detection
Publisher: SPIE
Date: 07-06-2007
DOI: 10.1117/12.724641
Anomaly detection in satellite communications systems using LSTM networks
Publisher: IEEE
Date: 11-2018
Track Everything: Limiting Prior Knowledge in Online Multi-Object Recognition
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2017
A biologically inspired model for signal compression
Publisher: SPIE
Date: 27-12-2007
DOI: 10.1117/12.695646
Small modifications to network topology can induce stochastic bistable spiking dynamics in a balanced cortical model
Publisher: Public Library of Science (PLoS)
Date: 17-04-2014
A Channel Model for Inferring the Optimal Number of Electrodes for Future Cochlear Implants
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2010
Simulation of electromyographic recordings following transcranial magnetic stimulation
Publisher: American Physiological Society
Date: 11-2018
Abstract: Transcranial magnetic stimulation (TMS) is a technique that enables noninvasive manipulation of neural activity and holds promise in both clinical and basic research settings. The effect of TMS on the motor cortex is often measured by electromyography (EMG) recordings from a small hand muscle. However, the details of how TMS generates responses measured with EMG are not completely understood. We aim to develop a biophysically detailed computational model to study the potential mechanisms underlying the generation of EMG signals following TMS. Our model comprises a feed-forward network of cortical layer 2/3 cells, which drive morphologically detailed layer 5 corticomotoneuronal cells, which in turn project to a pool of motoneurons. EMG signals are modeled as the sum of motor unit action potentials. EMG recordings from the first dorsal interosseous muscle were performed in four subjects and compared with simulated EMG signals. Our model successfully reproduces several characteristics of the experimental data. The simulated EMG signals match experimental EMG recordings in shape and size, and change with stimulus intensity and contraction level as in experimental recordings. They exhibit cortical silent periods that are close to the biological values and reveal an interesting dependence on inhibitory synaptic transmission properties. Our model predicts several characteristics of the firing patterns of neurons along the entire pathway from cortical layer 2/3 cells down to spinal motoneurons and should be considered as a viable tool for explaining and analyzing EMG signals following TMS. NEW & NOTEWORTHY A biophysically detailed model of EMG signal generation following transcranial magnetic stimulation (TMS) is proposed. Simulated EMG signals match experimental EMG recordings in shape and litude. Motor-evoked potential and cortical silent period properties match experimental data. The model is a viable tool to analyze, explain, and predict EMG signals following TMS.
Stochastic pooling networks
Publisher: IOP Publishing
Date: 05-01-2009
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