Terry Sejnowski's 2020 paper [arXiv:2002.04806] is entitled "The unreasonable effectiveness of deep learning in artificial intelligence". However, the paper doesn't attempt to answer the implied question of why Deep Convolutional Neural Networks (DCNNs) can approximate so many of the mappings that they have been trained to model. While there are detailed mathematical analyses, this short paper attempts to look at the issue differently, considering the way that these networks are used, the subset of these functions that can be achieved by training (starting from some location in the original function space), as well as the functions to which these networks will actually be applied.

Extraction of relevant lip features is of continuing interest in the visual speech domain. Using end-to-end feature extraction can produce good results, but at the cost of the results being difficult for humans to comprehend and relate to. We present a new, lightweight feature extraction approach, motivated by human-centric glimpse-based psychological research into facial barcodes, and demonstrate that these simple, easy to extract 3D geometric features (produced using Gabor-based image patches), can successfully be used for speech recognition with LSTM-based machine learning. This approach can successfully extract low dimensionality lip parameters with a minimum of processing. One key difference between using these Gabor-based features and using other features such as traditional DCT, or the current fashion for CNN features is that these are human-centric features that can be visualised and analysed by humans. This means that it is easier to explain and visualise the results. They can also be used for reliable speech recognition, as demonstrated using the Grid corpus. Results for overlapping speakers using our lightweight system gave a recognition rate of over 82%, which compares well to less explainable features in the literature.

A spike (event) based sound coding technique has been presented in this study where the spikes are similar to the spikes exhibited by type 1 fibers of the auditory nerve. This lossy coding technique has already been shown useful for inter-aural time difference based sound source direction finding. Here, we show that decoding and resynthesising this code can produce intelligible speech even using a small number of spike trains. We have used few composite techniques including speaker verification to assess the effectiveness of the coding technique on a large number of TIMIT sentences. This biologically inspired coding technique can provide suitable input for a spiking neural network, as well as maintaining the accurate time structure of sound.

We fabricate a microelectromechanical systems (MEMS) microphone that incorporates a graphene-based membrane that vibrates in response to acoustic forcing. We employ a novel fabrication process, where a graphene/poly(methyl methacrylate) (PMMA) bilayer membrane is transferred over a cavity on a separate chip before being affixed to the surface of another chip containing an electrode, resulting in the fabrication of a moveable capacitor with a membrane-to-electrode gap of 8 mu m. The gap, which is less than half the size of other reported graphene membrane-based audio transducers, allows for the device to operate with the low-DC bias voltages of about 1 V and, when integrated with a custom-designed readout circuit, demonstrates a sensitivity to sound pressure between 0.1 and 10 mV/Pa across the range 100 Hz-20 kHz. As well as a sensitivity that is comparable with the previous work, the flat frequency response is stable when the sound pressure is varied between 70 and 80 dB(SPL), with the sensitivity value not varying by more than 0.2 mV/Pa.

We present research using single-image super-resolution (SISR) algorithms to enhance knowledge of the seafloor using the 1-minute GEBCO 2014 grid when 100m grids from high-resolution sonar systems are available for training. We performed numerical experiments of x15 upscaling along three midocean ridge areas in the Eastern Pacific Ocean. We show that four SISR algorithms can enhance this low-resolution knowledge of bathymetry versus bicubic or Splines-In-Tension algorithms through upscaling under these conditions: 1) rough topography is present in both training and testing areas and 2) the range of depths and features in the training area contains the range of depths in the enhancement area. We quantitatively judged successful SISR enhancement versus bicubic interpolation when Student's hypothesis testing show significant improvement of the root-mean squared error (RMSE) between upscaled bathymetry and 100m gridded ground-truth bathymetry at p < 0.05. In addition, we found evidence that random forest based SISR methods may provide more robust enhancements versus non-forest based SISR algorithms.

This workshop was organized by INCF in Stockholm, Sweden, 18 - 19 June 2012. To ensure that research results can be trusted, it is essential to use methods that are validated. This holds both for analysis and sharing of data, and has become particularly pertinent in the context of the large-scale concerted brain projects that are presently emerging both in Europe and in the US. This workshop brought together scientists concerned with the validation of methods for data analysis from different perspectives. The workshop was motivated by a collaboration involving members of the Norwegian, Polish and German Nodes on establishing a community site for the evaluation of spike-sorting methods (spike.g-node.org). However, the need for validation of data analysis methods is not restricted to spike sorting, but pertains to all physiological and anatomical measurement methods used in neuroscience. Besides spike sorting of extracellular recordings, the workshop addressed the extraction of spikes from two-photon calcium imaging, methods for analysis of local-field potentials, and statistical analysis of spike trains. In addition, management and documentation of analysis workflows, which are crucial for validation of complex multi-stage analyses and thus an essential element of reproducible science, were discussed. An important additional measurement technique, functional magnetic resonance imaging (fMRI), was not considered extensively in the workshop, but it was understood by the workshop participants that the issues, problems and needs identified for the other fields, as well as the conclusions and recommendations, would equally apply to the field of fMRI. Typically, the efforts to define validation procedures for analysis methods, including the collection of benchmark data, start in single labs. However, they should be made available for use in the wide community. As requirements, benchmarks must be: • Broadly accepted by a wide range of laboratories • Available to these laboratories • Easily evaluated The workshop participants discussed what is necessary to bring a validation effort from a prototype-like state, typically achieved in the initiating lab, to a community resource. This process must involve the initiating scientists, but also the community, and ideally an organization that has built up the expertise to support this process. An example of this scheme is the spike-sorting validation project which has been enabled by a close collaboration of the involved scientists with the German INCF Node. The lesson learned from this example is that such a task should be taken on at the scale of an INCF program with expertise and support built up at the secretariat. Discussing the different examples, the workshop made clear that methods validation is at different stages for the various measurement techniques. Thus, specific recommendations differed, but overall the clear picture emerged that there is an unequivocal need for benchmarking activities. The participants agreed on the following overall key recommendations for supporting the various method validation initiatives: 1. Assure development and maintenance of the web site for validation of algorithms for spike sorting of electrical recordings hosted by the G-Node (spike.g-node.org). 2. Use the same technical resources to develop, host and maintain a corresponding website for validation of spike-detection algorithms for calcium imaging data. 3. Develop extensible framework allowing for validation of methods of analysis of other types of data. 4. Involve the community in the development of benchmarks and other means to validate methods for analysis. 5. Initiate and support training activities to educate users in methods validation. 6. Gather experts and users to discuss workflow standardization, and start activities to support reproducibility in data analysis.

This paper aims to draw a comparison between the traditional view of hyper-heuristics and a lesser known type of multi-agent system known as a blackboard system. Both approaches share many similarities in both implementation and philosophy but also have several important differences in terms of characteristics and approach, such as a difference in control scheme. To investigate the consequences of the perceived differences, both approaches are decomposed into their constituent parts and compared with a focus on the perceived strengths and weaknesses of adopting one methodology over the other.

Deep Learning has proven to be an effective method for making highly accurate predictions from complex data sources. Convolutional neural networks continue to dominate image classification problems and recursive neural networks have proven their utility in caption generation and language translations. While these approaches are powerful, they do not offer explanation for how the output is generated. Without understanding how deep learning arrives at a solution there is no guarantee that these networks will transition from controlled laboratory environments to fieldable systems. This paper presents an approach for incorporating such rule based methodology into neural networks by embedding fuzzy inference systems into deep learning networks.

Deep learning is a powerful tool for labeling images in computer vision. We apply deep learning to select subgoals in the simulated game environment of Minecraft. Prior work showed that subgoal selection could be learned with off-the-shelf machine learning techniques and full state knowledge. We extend that work to learn subgoal selection from raw pixels. In a limited pilot study where a virtual chracter must overcome obstacles, we show that AlexNet can learn an effective policy 93% of the time with very little training.

The CARMEN Virtual Laboratory (VL) is a cloud-based platform which allows neuroscientists to store, share, develop, execute, reproduce and publicise their work. This paper describes new functionality in the CARMEN VL: an interactive publications repository. This new facility allows users to link data and software to publications. This enables other users to examine data and software associated with the publication and execute the associated software within the VL using the same data as the authors used in the publication. The cloud-based architecture and SaaS (Software as a Service) framework allows vast data sets to be uploaded and analysed using software services. Thus, this new interactive publications facility allows others to build on research results through reuse. This aligns with recent developments by funding agencies, institutions, and publishers with a move to open access research. Open access provides reproducibility and verification of research resources and results. Publications and their associated data and software will be assured of long-term preservation and curation in the repository. Further, analysing research data and the evaluations described in publications frequently requires a number of execution stages many of which are iterative. The VL provides a scientific workflow environment to combine software services into a processing tree. These workflows can also be associated with publications and executed by users. The VL also provides a secure environment where users can decide the access rights for each resource to ensure copyright and privacy restrictions are met.

It seems that seeing others in slow-motion by heroes does not belong only to movies. When Lionel Messi plays football, you can hardly see anything from him that other players cannot do. Then why he is not stoppable really? It seems the answer may be that opponents do not have enough time to do what they want; because in Messi's neural system, time passes slower. In differential equations that model a single neuron, this speed can be generated by multiplying an equal term in all equations. Or maybe interactions between neurons and the structure of neural networks play this role.

Radium (226Ra) contamination derived from military, industrial, and pharmaceutical products can be found at a number of historical sites across the world posing a risk to human health. The analysis of spectral data derived using gamma-ray spectrometry can offer a powerful tool to rapidly estimate and map the activity, depth, and lateral distribution of 226Ra contamination covering an extensive area. Subsequently, reliable risk assessments can be developed for individual sites in a fraction of the timeframe compared to traditional labour-intensive sampling techniques: for example soil coring. However, local heterogeneity of the natural background, statistical counting uncertainty, and non-linear source response are confounding problems associated with gamma-ray spectral analysis. This is particularly challenging, when attempting to deal with enhanced concentrations of a naturally occurring radionuclide such as 226Ra. As a result, conventional surveys tend to attribute the highest activities to the largest total signal received by a detector (Gross counts): an assumption that tends to neglect higher activities at depth. To overcome these limitations, a methodology was developed making use of Monte Carlo simulations, Principal Component Analysis and Machine Learning based algorithms to derive depth and activity estimates for 226Ra contamination. The approach was applied on spectra taken using two gamma-ray detectors (Lanthanum Bromide and Sodium Iodide), with the aim of identifying an optimised combination of detector and spectral processing routine. It was confirmed that, through a combination of Neural Networks and Lanthanum Bromide, the most accurate depth and activity estimates could be found. The advantage of the method was demonstrated by mapping depth and activity estimates at a case study site in Scotland. There the method identified significantly higher activity (<3Bqg−1) occurring at depth (>0.4m), that conventional gross counting algorithms failed to identify. It was concluded that the method could easily be employed to identify areas of high activity potentially occurring at depth, prior to intrusive investigation using conventional sampling techniques.

This project was conceived by Nalanda Technology, a text data search and analysis company. This study concerned the following central research question: Can Machine Learning techniques be used to analyze automatically the change in aggregate levels of sentiment polarity within a defined topic in UK parliamentary speeches? We addressed the question by making use of methods of natural language processing (NLP). The project evaluated whether or not opinion-mining techniques can successfully be applied to the textual analysis of parliamentary debates. For data, we used the transcriptions of the speeches made at the plenary parliamentary meetings as reported in the Hansard Archive (digitised debates of the UK Parliament from 1803 to the present). A Sentiment Model was implemented to retrieve meaningful patterns based on the classification results. A substantial selection of parliamentary proceedings was compiled and a Gold Standard Corpus (GSC) created, covering both subjectivity and orientation. The main idea of this study is that an automatic sentiment analysis model can be very useful for rapidly visualizing how the opinion of the parliament – which normally reflects the national sentiment – changes over a period of time. This enables the user to put together an overall picture of the position on a specific issue and to pick up on the underlying trends without having to go through the entire sequence of the Hansard speeches. The visualizations have to be clear and easy to understand, with all ambiguities eliminated. The Sentiment Classifier was built on top of the Nalytics search engine, the core product of Nalanda Technology, to add a new analytical feature to this data-search and discovery platform. The classifier was the outcome of a series of experiments, including an analysis of the performance levels of various supervised learning algorithms, such as Support Vector Machines, Linear Regression, Naive Bayes, etc. These techniques and methodologies were written in Python using the NLTK and SciKit-Learn toolkits. The model achieved a classification accuracy level of 75% when trained and tested with the GSC – this was considered a promising result, in light of the complex nature of parliamentary discourse.

Many journals and research funding organisations now demand that researchers make their datasets, metadata, and processing software publically and permanently available once their work has been published. Repeatability of results and re-use of materials for cross-analysis requires this. Yet often the mechanism for archiving is unspecified. The International Neuroinformatics Co-ordinating Facility (INCF) keeps a list of re-usable Neuroscience (including Neurophysiology) resources at http://incf.org/ resources/research-tools. In addition, the INCF’s Electrophysiology Task Force maintains a list of resources for data sharing in (neuroimaging and) electrophysiology at http://tinyurl.com/d7f35qb . However, many of these are intended for specimen datasets, rather than data generated during research. The UK based CARMEN project ( https://portal.carmen.org.uk ) is a portal based collaborative facility for neuroscientists (and in particular electrophysiologists) to share data and tools for working on data. Started in 2006, it has been providing a gradually improving service for about eight years. The system can translate many common formats into its own internal format and can generate a quick graphical display of datasets. Though successful for enabling geographically distributed groups to share data and techniques, few groups have made data publically available. Neuroinformaticians are already convinced of the importance of data sharing, so it is imperative to find out what is impeding uptake by experimentalists. Following a questionnaire issued to users (and non- users) we found that the issues were ease of uploading (speed, and issues with metadata), difficulties in finding and using services and developing and using workflows. Many users wanted only archival capability, and found the system complex. However some users have used the system in a much more powerful way, performing historic cross-analyses which could pave the way for others to start using the resource to its full potential. Long term storage and archiving of data and processing techniques requires long term funding of infrastructure projects. Yet although publishers and funders want this, long term project funding remains difficult.

Event Abstract Back to Event The CARMEN (Code Analysis Repository and Modelling for E-Neuroscience) project for collaborative sharing of data and analysis tools in electrophysiology: reviewing an early co-laboratory. Leslie S. Smith1* 1 University of Stirling, Computing Science and Mathematics, United Kingdom The CARMEN portal (developed under a UK EPSRC grant from 2006, and under a UK BBSRC grant until 2015) launched in 2008 (https://portal.carmen.org.uk). It allows researchers to store and share electrophysiology datasets and analysis tools, with a sophisticated security system, enabling researchers to collaborate and share datasets and tools. It was intended as a place where researchers could work collaboratively on data as though the analysis and modelling software, and the datasets were local to the researchers. Considerable effort was put into developing the metadata system [1] and the internal data format, Neurophysiology Data Translation Format (NDF) [2]. Metadata is critical for allowing re-use of datasets and tools. Nonetheless, users report that inputting metadata discourages them from uploading data. This problem has two aspects. Firstly, neurophysiology metadata is complex and multi-faceted, starting from the relatively simple structural metadata (about the detailed file format) and ranging through more complex issues (e.g. the type and precise geometrical arrangement of electrodes and the detailed description of the neural tissue being recorded from) to even more difficult descriptions of the stimuli being used while recordings are being made (if they are from a live animal). Secondly, neurophysiologists do record this information, but often in a physical lab book. Uploading it feels like duplication of effort, and this is made worse because of the constraints imposed by the (generally) form-based input system for metadata failing to match the text in the lab book. The obvious solution to this is the use of an electronic lab book, so that the data is only entered once, but here too, standardisation is critical, particularly for extracting metadata. Because most electrophysiology research is performed on equipment manufactured by a number of companies, and because they do not agree on standard formats, there are many different data formats for electrophysiology. Making analysis tools work across the plethora of formats for electrophysiology datasets entails an internal data format: this is critical, because the analysis and visualisation tools need to be able to interpret data without having to interrogate the format first. Services were provided to translate user data into this format. However, the format used [2] was designed at a time when overall formats were still changing. For example, HDF5 was not used, unlike the more recent formats proposed by Teeters et al [3] in their recent Neuroscience Without Borders project paper. Much CARMEN usage has been by groups collaborating geographically: the security system allows sharing datasets and analysis tools with a high degree of granularity. These users have made little use of workflow technology. However, this may be because there were problems in providing workflow capability. The capability now provided includes a graphical editor, but this does not permit loops in the workflow, seriously reducing its usefulness (for example, it does not currently support applying a workflow over a range of parameters). This is an example of ease of use taking precedence over usefulness: the intended users were thought not to be programmers, so a visual interface for building workflows was provided. However, it is very difficult to provide the range of capabilities in a visual programming system that can be provided by a textual system – and this can be seen in the rarity of visual programming languages for general-purpose programming. In particular, providing loops and if-branches that allow for alteration of parameters is much easier in a scripting language. The unintended effect was to make sophisticated use of the workflow facility essentially impossible. The recently released integrated capability to eyeball datasets has been useful, more so than the original tool used for data visualisation, which required the use of a separate program. This is an example of how developments in the technology alter the way in which one writes systems: in this case, the inclusion in HTML5 of an integrated (and cross-browser) capability for graph drawing changed the way in which graphics was best presented. Retinal neurophysiologists have been the most effective users of the CARMEN collaboratory. A cross-animal study of excised retinal development has re-used data from different animals, using workflows to perform identical analysis on the raw data from different experiments: all the data and tools are publicly available [4]. Aspects of CARMEN’s implementation are now out-of-date. As noted earlier, a new internal data format would use HDF5, and the more recent attempts at standardisation. Issues of storing metadata have not been entirely solved, although there is more recent work on metadata [5]. However, the issue of ensuring single entry metadata is not yet solved. On the more technical side, services currently run on single cores, and much of the client-side was coded as Java applets (for example, data uploading). A new implementation would change these early decisions. Very recently, a new collaboratory system has been unveiled by the Human Brain Project [6]. This has come from a large project, and covers much more than neurophysiology. It includes much of what CARMEN attempted in its SP5 Neuroinformatics Platform. The release to non-members of HBP is very recent, and as yet no comparisons have been made. It is, however, clear that the collaboratory concept remains very much alive, but that the technology to support it has moved on considerably since the CARMEN project was implemented. Acknowledgements EPSRC grant EP/E002331/1, BBSRC grant BB/I001042/1 References [1] F. Gibson, P.G. Overton, T.V. Smulders, S.R. Schultz, S.J. Eglen, C.D. Ingram, S. Panzeri, P. Bream, M. Whittington, E. Sernagor, M. Cunningham, C. Adams, C. Echtermeyer, J. Simonotto, M. Kaiser, D.C. Swan, M. Fletcher, P. Lord, Minimum Information about a Neuroscience Investigation (MINI): Electrophysiology, Nature Precedings, 2008, hdl:10101/npre.2009.1720.2 [2] See http://www.carmen.org.uk/?page_id=282 for the specification and MATLAB toolbox implementations. [3] J.L. Teeters, K. Godfrey, R. Young, C. Dang, C. Friedsam, B. Wark, H. Asari, S. Peron, N. Li, A. Peyrache, G. Denisov, J.H. Siegle, S. R. Olsen, C. Martin, M. Chun, S. Tripathy, T.J. Blanche, K. D. Harris, G. Buzsaki, C. Koch, M. Meister, K. Svoboda, F.T. Sommer: Neurodata Without Borders: Creating a common data format for neurophysiology. Neuron 88:629-634 (2015) [4] S.J. Eglen, M. Weeks, M. Jesso., J. Simmonotto, T. Jackson, E. Sernagor, A data repository and analysis framework for spontaneous neural activity recordings indeveloping retina. GigaScience, 3(3), 1–12, (2014). [5] Y. Le Franc, A. Bandrowski, P. Bruha, V. Papez, J. Grewe, R. Moucek, S.J. Tripathy, T. Wachtler, Describing neurophysiology data and metadata with OEN, the Ontology for Experimental Neurophysiology, Frontiers in Neuroinformatics 44, Neuroinformatics 2014, Leiden, Netherlands, 25 Aug - 27 Aug, 2014 [6] https://www.humanbrainproject.eu/en_GB/platforms-overview Keywords: neurophysiology portal, Collaboratory, CARMEN, Neural Data Format, metadata Conference: Neuroinformatics 2016, Reading, United Kingdom, 3 Sep - 4 Sep, 2016. Presentation Type: Poster Topic: Infrastructural and portal services Citation: Smith LS (2016). The CARMEN (Code Analysis Repository and Modelling for E-Neuroscience) project for collaborative sharing of data and analysis tools in electrophysiology: reviewing an early co-laboratory.. Front. Neuroinform. Conference Abstract: Neuroinformatics 2016. doi: 10.3389/conf.fninf.2016.20.00036 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 29 Apr 2016; Published Online: 18 Jul 2016. * Correspondence: Prof. Leslie S Smith, University of Stirling, Computing Science and Mathematics, Stirling, Scotland, FK9 4LA, United Kingdom, l.s.smith@cs.stir.ac.uk Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Leslie S Smith Google Leslie S Smith Google Scholar Leslie S Smith PubMed Leslie S Smith Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

The purpose of this document is to specify the basic data types required for storing electrophysiology and optical imaging data to facilitate computer-based neuroscience studies and data sharing. These requirements are being developed within a working group of the Electrophysiology Task Force in the International Neuroinformatics Coordinating Facility (INCF) Program on Standards for Data Sharing. While this document describes the requirements of the standard independent of the actual storage technology, the Task Force has recommended basing a standard on HDF5. This is in line with a number of groups who are already using HDF5 to store electrophysiology data, although currently without being based on a standard.

Area consumption is one of the most important design constrains in the development of compact digital systems. Several authors have proposed making compact Cochlear Implant processors using Gamma-tone filter banks. These model aspects of the cochlea spectral filtering. A good area-efficient design of the Gamma-tone Filter Bank could reduce the amount of circuitry allowing patients to wear these cochlear implants more easily. In consequence, many authors have reduced the area by using the minimum number of registers when implementing this type of filter. However, critical paths limit their performance. Here a compact Gamma-tone Filter processor, formulated using the impulse invariant transformation together with a normalization method, is presented. The normalization method in the model guarantees the same precision for any filter order. In addition, area resources are kept low due to the implementation of a single Second Order Section (SOS) IIR stage for processing several SOS IIR stages and several channels at different times. Results show that the combination of the properties of the model and the implementation techniques generate a processor with high processing speed, expending less resources than reported in the literature.

Can Deep Neural Networks (DNNs) provide the basis for Artificial General Intelligence (AGI) is the topic for this workshop. We do not expect to come to a specific conclusion, but what we provide here are some talks, and space for discussion. However, the speakers seem to have lined up on one side of the division (from my reading of their abstracts), so I propose to take the other side! But what is a DNN, exactly? Goertzel [Goe15] reckons that it is