From Bidirectional Associative Memory to a noise-tolerant, robust Protein Processor Associative Memory

AbstractProtein Processor Associative Memory (PPAM) is a novel architecture for learning associations incrementally and online and performing fast, reliable, scalable hetero-associative recall. This paper presents a comparison of the PPAM with the Bidirectional Associative Memory (BAM), both with Kosko's original training algorithm and also with the more popular Pseudo-Relaxation Learning Algorithm for BAM (PRLAB). It also compares the PPAM with a more recent associative memory architecture called SOIAM. Results of training for object-avoidance are presented from simulations using player/stage and are verified by actual implementations on the E-Puck mobile robot. Finally, we show how the PPAM is capable of achieving an increase in performance without using the typical weighted-sum arithmetic operations or indeed any arithmetic operations

Some experiments around a neural network for multimodal associations

This paper presents a study of the model of triple BAM by E.Reynaud which is an improved variation of the original BAM model by Kosko. This class of model aims at integrating different sensory inputs in order to memorize a unified and distributed representation. An experimental evaluation of the model is presented that underlines its limitations in terms of noise robustness and learning capacities. A new model is presented in order to overcome those initial limitations by introducing a new online learning algorithm adapted from the PRLAB initial algorithm that improve both noise robustness and learning capacities. Finally, model properties and limitations are considered and discussed within the context of multi-modal integration and brain modeling

NASA JSC neural network survey results

A survey of Artificial Neural Systems in support of NASA's (Johnson Space Center) Automatic Perception for Mission Planning and Flight Control Research Program was conducted. Several of the world's leading researchers contributed papers containing their most recent results on artificial neural systems. These papers were broken into categories and descriptive accounts of the results make up a large part of this report. Also included is material on sources of information on artificial neural systems such as books, technical reports, software tools, etc

Associative neural networks: properties, learning, and applications.

by Chi-sing Leung.Thesis (Ph.D.)--Chinese University of Hong Kong, 1994.Includes bibliographical references (leaves 236-244).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Background of Associative Neural Networks --- p.1Chapter 1.2 --- A Distributed Encoding Model: Bidirectional Associative Memory --- p.3Chapter 1.3 --- A Direct Encoding Model: Kohonen Map --- p.6Chapter 1.4 --- Scope and Organization --- p.9Chapter 1.5 --- Summary of Publications --- p.13Chapter I --- Bidirectional Associative Memory: Statistical Proper- ties and Learning --- p.17Chapter 2 --- Introduction to Bidirectional Associative Memory --- p.18Chapter 2.1 --- Bidirectional Associative Memory and its Encoding Method --- p.18Chapter 2.2 --- Recall Process of BAM --- p.20Chapter 2.3 --- Stability of BAM --- p.22Chapter 2.4 --- Memory Capacity of BAM --- p.24Chapter 2.5 --- Error Correction Capability of BAM --- p.28Chapter 2.6 --- Chapter Summary --- p.29Chapter 3 --- Memory Capacity and Statistical Dynamics of First Order BAM --- p.31Chapter 3.1 --- Introduction --- p.31Chapter 3.2 --- Existence of Energy Barrier --- p.34Chapter 3.3 --- Memory Capacity from Energy Barrier --- p.44Chapter 3.4 --- Confidence Dynamics --- p.49Chapter 3.5 --- Numerical Results from the Dynamics --- p.63Chapter 3.6 --- Chapter Summary --- p.68Chapter 4 --- Stability and Statistical Dynamics of Second order BAM --- p.70Chapter 4.1 --- Introduction --- p.70Chapter 4.2 --- Second order BAM and its Stability --- p.71Chapter 4.3 --- Confidence Dynamics of Second Order BAM --- p.75Chapter 4.4 --- Numerical Results --- p.82Chapter 4.5 --- Extension to higher order BAM --- p.90Chapter 4.6 --- Verification of the conditions of Newman's Lemma --- p.94Chapter 4.7 --- Chapter Summary --- p.95Chapter 5 --- Enhancement of BAM --- p.97Chapter 5.1 --- Background --- p.97Chapter 5.2 --- Review on Modifications of BAM --- p.101Chapter 5.2.1 --- Change of the encoding method --- p.101Chapter 5.2.2 --- Change of the topology --- p.105Chapter 5.3 --- Householder Encoding Algorithm --- p.107Chapter 5.3.1 --- Construction from Householder Transforms --- p.107Chapter 5.3.2 --- Construction from iterative method --- p.109Chapter 5.3.3 --- Remarks on HCA --- p.111Chapter 5.4 --- Enhanced Householder Encoding Algorithm --- p.112Chapter 5.4.1 --- Construction of EHCA --- p.112Chapter 5.4.2 --- Remarks on EHCA --- p.114Chapter 5.5 --- Bidirectional Learning --- p.115Chapter 5.5.1 --- Construction of BL --- p.115Chapter 5.5.2 --- The Convergence of BL and the memory capacity of BL --- p.116Chapter 5.5.3 --- Remarks on BL --- p.120Chapter 5.6 --- Adaptive Ho-Kashyap Bidirectional Learning --- p.121Chapter 5.6.1 --- Construction of AHKBL --- p.121Chapter 5.6.2 --- Convergent Conditions for AHKBL --- p.124Chapter 5.6.3 --- Remarks on AHKBL --- p.125Chapter 5.7 --- Computer Simulations --- p.126Chapter 5.7.1 --- Memory Capacity --- p.126Chapter 5.7.2 --- Error Correction Capability --- p.130Chapter 5.7.3 --- Learning Speed --- p.157Chapter 5.8 --- Chapter Summary --- p.158Chapter 6 --- BAM under Forgetting Learning --- p.160Chapter 6.1 --- Introduction --- p.160Chapter 6.2 --- Properties of Forgetting Learning --- p.162Chapter 6.3 --- Computer Simulations --- p.168Chapter 6.4 --- Chapter Summary --- p.168Chapter II --- Kohonen Map: Applications in Data compression and Communications --- p.170Chapter 7 --- Introduction to Vector Quantization and Kohonen Map --- p.171Chapter 7.1 --- Background on Vector quantization --- p.171Chapter 7.2 --- Introduction to LBG algorithm --- p.173Chapter 7.3 --- Introduction to Kohonen Map --- p.174Chapter 7.4 --- Chapter Summary --- p.179Chapter 8 --- Applications of Kohonen Map in Data Compression and Communi- cations --- p.181Chapter 8.1 --- Use Kohonen Map to design Trellis Coded Vector Quantizer --- p.182Chapter 8.1.1 --- Trellis Coded Vector Quantizer --- p.182Chapter 8.1.2 --- Trellis Coded Kohonen Map --- p.188Chapter 8.1.3 --- Computer Simulations --- p.191Chapter 8.2 --- Kohonen MapiCombined Vector Quantization and Modulation --- p.195Chapter 8.2.1 --- Impulsive Noise in the received data --- p.195Chapter 8.2.2 --- Combined Kohonen Map and Modulation --- p.198Chapter 8.2.3 --- Computer Simulations --- p.200Chapter 8.3 --- Error Control Scheme for the Transmission of Vector Quantized Data --- p.213Chapter 8.3.1 --- Motivation and Background --- p.214Chapter 8.3.2 --- Trellis Coded Modulation --- p.216Chapter 8.3.3 --- "Combined Vector Quantization, Error Control, and Modulation" --- p.220Chapter 8.3.4 --- Computer Simulations --- p.223Chapter 8.4 --- Chapter Summary --- p.226Chapter 9 --- Conclusion --- p.232Bibliography --- p.23

Complex Neural Networks for Audio

Audio is represented in two mathematically equivalent ways: the real-valued time domain (i.e., waveform) and the complex-valued frequency domain (i.e., spectrum). There are advantages to the frequency-domain representation, e.g., the human auditory system is known to process sound in the frequency-domain. Furthermore, linear time-invariant systems are convolved with sources in the time-domain, whereas they may be factorized in the frequency-domain. Neural networks have become rather useful when applied to audio tasks such as machine listening and audio synthesis, which are related by their dependencies on high quality acoustic models. They ideally encapsulate fine-scale temporal structure, such as that encoded in the phase of frequency-domain audio, yet there are no authoritative deep learning methods for complex audio. This manuscript is dedicated to addressing the shortcoming. Chapter 2 motivates complex networks by their affinity with complex-domain audio, while Chapter 3 contributes methods for building and optimizing complex networks. We show that the naive implementation of Adam optimization is incorrect for complex random variables and show that selection of input and output representation has a significant impact on the performance of a complex network. Experimental results with novel complex neural architectures are provided in the second half of this manuscript. Chapter 4 introduces a complex model for binaural audio source localization. We show that, like humans, the complex model can generalize to different anatomical filters, which is important in the context of machine listening. The complex model\u27s performance is better than that of the real-valued models, as well as real- and complex-valued baselines. Chapter 5 proposes a two-stage method for speech enhancement. In the first stage, a complex-valued stochastic autoencoder projects complex vectors to a discrete space. In the second stage, long-term temporal dependencies are modeled in the discrete space. The autoencoder raises the performance ceiling for state of the art speech enhancement, but the dynamic enhancement model does not outperform other baselines. We discuss areas for improvement and note that the complex Adam optimizer improves training convergence over the naive implementation

A Review on MAS-Based Sentiment and Stress Analysis User-Guiding and Risk-Prevention Systems in Social Network Analysis

[EN] In the current world we live immersed in online applications, being one of the most present of them Social Network Sites (SNSs), and different issues arise from this interaction. Therefore, there is a need for research that addresses the potential issues born from the increasing user interaction when navigating. For this reason, in this survey we explore works in the line of prevention of risks that can arise from social interaction in online environments, focusing on works using Multi-Agent System (MAS) technologies. For being able to assess what techniques are available for prevention, works in the detection of sentiment polarity and stress levels of users in SNSs will be reviewed. We review with special attention works using MAS technologies for user recommendation and guiding. Through the analysis of previous approaches on detection of the user state and risk prevention in SNSs we elaborate potential future lines of work that might lead to future applications where users can navigate and interact between each other in a more safe way.This work was funded by the project TIN2017-89156-R of the Spanish government.Aguado-Sarrió, G.; Julian Inglada, VJ.; García-Fornes, A.; Espinosa Minguet, AR. (2020). A Review on MAS-Based Sentiment and Stress Analysis User-Guiding and Risk-Prevention Systems in Social Network Analysis. Applied Sciences. 10(19):1-29. https://doi.org/10.3390/app10196746S1291019Vanderhoven, E., Schellens, T., Vanderlinde, R., & Valcke, M. (2015). Developing educational materials about risks on social network sites: a design based research approach. Educational Technology Research and Development, 64(3), 459-480. doi:10.1007/s11423-015-9415-4Teens and ICT: Risks and Opportunities. Belgium: TIRO http://www.belspo.be/belspo/fedra/proj.asp?l=en&COD=TA/00/08Risks and Safety on the Internet: The Perspective of European Children: Full Findings and Policy Implications From the EU Kids Online Survey of 9–16 Year Olds and Their Parents in 25 Countries http://eprints.lse.ac.uk/33731/Vanderhoven, E., Schellens, T., & Valcke, M. (2014). Educating teens about the risks on social network sites. An intervention study in Secondary Education. Comunicar, 22(43), 123-132. doi:10.3916/c43-2014-12Christofides, E., Muise, A., & Desmarais, S. (2012). Risky Disclosures on Facebook. Journal of Adolescent Research, 27(6), 714-731. doi:10.1177/0743558411432635George, J. M., & Dane, E. (2016). Affect, emotion, and decision making. Organizational Behavior and Human Decision Processes, 136, 47-55. doi:10.1016/j.obhdp.2016.06.004Thelwall, M. (2017). TensiStrength: Stress and relaxation magnitude detection for social media texts. Information Processing & Management, 53(1), 106-121. doi:10.1016/j.ipm.2016.06.009Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010). Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61(12), 2544-2558. doi:10.1002/asi.21416Shoumy, N. J., Ang, L.-M., Seng, K. P., Rahaman, D. M. M., & Zia, T. (2020). Multimodal big data affective analytics: A comprehensive survey using text, audio, visual and physiological signals. Journal of Network and Computer Applications, 149, 102447. doi:10.1016/j.jnca.2019.102447Zhang, C., Zeng, D., Li, J., Wang, F.-Y., & Zuo, W. (2009). Sentiment analysis of Chinese documents: From sentence to document level. Journal of the American Society for Information Science and Technology, 60(12), 2474-2487. doi:10.1002/asi.21206Lu, B., Ott, M., Cardie, C., & Tsou, B. K. (2011). Multi-aspect Sentiment Analysis with Topic Models. 2011 IEEE 11th International Conference on Data Mining Workshops. doi:10.1109/icdmw.2011.125Nasukawa, T., & Yi, J. (2003). Sentiment analysis. Proceedings of the international conference on Knowledge capture - K-CAP ’03. doi:10.1145/945645.945658Borth, D., Ji, R., Chen, T., Breuel, T., & Chang, S.-F. (2013). Large-scale visual sentiment ontology and detectors using adjective noun pairs. Proceedings of the 21st ACM international conference on Multimedia - MM ’13. doi:10.1145/2502081.2502282Deb, S., & Dandapat, S. (2019). Emotion Classification Using Segmentation of Vowel-Like and Non-Vowel-Like Regions. IEEE Transactions on Affective Computing, 10(3), 360-373. doi:10.1109/taffc.2017.2730187Deng, J., Zhang, Z., Marchi, E., & Schuller, B. (2013). Sparse Autoencoder-Based Feature Transfer Learning for Speech Emotion Recognition. 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction. doi:10.1109/acii.2013.90Nicolaou, M. A., Gunes, H., & Pantic, M. (2011). Continuous Prediction of Spontaneous Affect from Multiple Cues and Modalities in Valence-Arousal Space. IEEE Transactions on Affective Computing, 2(2), 92-105. doi:10.1109/t-affc.2011.9Hossain, M. S., Muhammad, G., Alhamid, M. F., Song, B., & Al-Mutib, K. (2016). Audio-Visual Emotion Recognition Using Big Data Towards 5G. Mobile Networks and Applications, 21(5), 753-763. doi:10.1007/s11036-016-0685-9Zhou, F., Jianxin Jiao, R., & Linsey, J. S. (2015). Latent Customer Needs Elicitation by Use Case Analogical Reasoning From Sentiment Analysis of Online Product Reviews. Journal of Mechanical Design, 137(7). doi:10.1115/1.4030159Ceci, F., Goncalves, A. L., & Weber, R. (2016). A model for sentiment analysis based on ontology and cases. IEEE Latin America Transactions, 14(11), 4560-4566. doi:10.1109/tla.2016.7795829Vizer, L. M., Zhou, L., & Sears, A. (2009). Automated stress detection using keystroke and linguistic features: An exploratory study. International Journal of Human-Computer Studies, 67(10), 870-886. doi:10.1016/j.ijhcs.2009.07.005Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82-89. doi:10.1145/2436256.2436274Schouten, K., & Frasincar, F. (2016). Survey on Aspect-Level Sentiment Analysis. IEEE Transactions on Knowledge and Data Engineering, 28(3), 813-830. doi:10.1109/tkde.2015.2485209Ji, R., Cao, D., Zhou, Y., & Chen, F. (2016). Survey of visual sentiment prediction for social media analysis. Frontiers of Computer Science, 10(4), 602-611. doi:10.1007/s11704-016-5453-2Li, L., Cao, D., Li, S., & Ji, R. (2015). Sentiment analysis of Chinese micro-blog based on multi-modal correlation model. 2015 IEEE International Conference on Image Processing (ICIP). doi:10.1109/icip.2015.7351718Lee, P.-M., Tsui, W.-H., & Hsiao, T.-C. (2015). The Influence of Emotion on Keyboard Typing: An Experimental Study Using Auditory Stimuli. PLOS ONE, 10(6), e0129056. doi:10.1371/journal.pone.0129056Matsiola, M., Dimoulas, C., Kalliris, G., & Veglis, A. A. (2018). Augmenting User Interaction Experience Through Embedded Multimodal Media Agents in Social Networks. Information Retrieval and Management, 1972-1993. doi:10.4018/978-1-5225-5191-1.ch088Rosaci, D. (2007). CILIOS: Connectionist inductive learning and inter-ontology similarities for recommending information agents. Information Systems, 32(6), 793-825. doi:10.1016/j.is.2006.06.003Buccafurri, F., Comi, A., Lax, G., & Rosaci, D. (2016). Experimenting with Certified Reputation in a Competitive Multi-Agent Scenario. 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Brain-Inspired Computational Intelligence via Predictive Coding

Artificial intelligence (AI) is rapidly becoming one of the key technologies of this century. The majority of results in AI thus far have been achieved using deep neural networks trained with the error backpropagation learning algorithm. However, the ubiquitous adoption of this approach has highlighted some important limitations such as substantial computational cost, difficulty in quantifying uncertainty, lack of robustness, unreliability, and biological implausibility. It is possible that addressing these limitations may require schemes that are inspired and guided by neuroscience theories. One such theory, called predictive coding (PC), has shown promising performance in machine intelligence tasks, exhibiting exciting properties that make it potentially valuable for the machine learning community: PC can model information processing in different brain areas, can be used in cognitive control and robotics, and has a solid mathematical grounding in variational inference, offering a powerful inversion scheme for a specific class of continuous-state generative models. With the hope of foregrounding research in this direction, we survey the literature that has contributed to this perspective, highlighting the many ways that PC might play a role in the future of machine learning and computational intelligence at large.Comment: 37 Pages, 9 Figure

Analysing and enhancing the performance of associative memory architectures

This thesis investigates the way in which information about the structure of a set of training data with 'natural' characteristics may be used to positively influence the design of associative memory neural network models of the Hopfield type. This is done with a view to reducing the level of connectivity in models of this type. There are three strands to this work. Firstly, an empirical evaluation of the implementation of existing theory is given. Secondly, a number of existing theories are combined to produce novel network models and training regimes. Thirdly, new strategies for constructing and training associative memories based on knowledge of the structure of the training data are proposed. The first conclusion of this work is that, under certain circumstances, performance benefits may be gained by establishing the connectivity in a non-random fashion, guided by the knowledge gained from the structure of the training data. These performance improvements exist in relation to networks in which sparse connectivity is established in a purely random manner. This dilution occurs prior to the training of the network. Secondly, it is verified that, as predicted by existing theory, targeted post-training dilution of network connectivity provides greater performance when compared with networks in which connections are removed at random. Finally, an existing tool for the analysis of the attractor performance of neural networks of this type has been modified and improved. Furthermore, a novel, comprehensive performance analysis tool is proposed

The hardware implementation of an artificial neural network using stochastic pulse rate encoding principles

In this thesis the development of a hardware artificial neuron device and artificial neural network using stochastic pulse rate encoding principles is considered. After a review of neural network architectures and algorithmic approaches suitable for hardware implementation, a critical review of hardware techniques which have been considered in analogue and digital systems is presented. New results are presented demonstrating the potential of two learning schemes which adapt by the use of a single reinforcement signal. The techniques for computation using stochastic pulse rate encoding are presented and extended with new novel circuits relevant to the hardware implementation of an artificial neural network. The generation of random numbers is the key to the encoding of data into the stochastic pulse rate domain. The formation of random numbers and multiple random bit sequences from a single PRBS generator have been investigated. Two techniques, Simulated Annealing and Genetic Algorithms, have been applied successfully to the problem of optimising the configuration of a PRBS random number generator for the formation of multiple random bit sequences and hence random numbers. A complete hardware design for an artificial neuron using stochastic pulse rate encoded signals has been described, designed, simulated, fabricated and tested before configuration of the device into a network to perform simple test problems. The implementation has shown that the processing elements of the artificial neuron are small and simple, but that there can be a significant overhead for the encoding of information into the stochastic pulse rate domain. The stochastic artificial neuron has the capability of on-line weight adaption. The implementation of reinforcement schemes using the stochastic neuron as a basic element are discussed