Design and Analysis of a Reconfigurable Hierarchical Temporal Memory Architecture
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Memory-Prediction Framework for Pattern
Memory–Prediction Framework for Pattern Recognition: Performance and Suitability of the Bayesian Model of Visual Cortex Saulius J. Garalevicius Department of Computer and Information Sciences, Temple University Room 303, Wachman Hall, 1805 N. Broad St., Philadelphia, PA, 19122, USA [email protected] Abstract The neocortex learns sequences of patterns by storing This paper explores an inferential system for recognizing them in an invariant form in a hierarchical neural network. visual patterns. The system is inspired by a recent memory- It recalls the patterns auto-associatively when given only prediction theory and models the high-level architecture of partial or distorted inputs. The structure of stored invariant the human neocortex. The paper describes the hierarchical representations captures the important relationships in the architecture and recognition performance of this Bayesian world, independent of the details. The primary function of model. A number of possibilities are analyzed for bringing the neocortex is to make predictions by comparing the the model closer to the theory, making it uniform, scalable, knowledge of the invariant structure with the most recent less biased and able to learn a larger variety of images and observed details. their transformations. The effect of these modifications on The regions in the hierarchy are connected by multiple recognition accuracy is explored. We identify and discuss a number of both conceptual and practical challenges to the feedforward and feedback connections. Prediction requires Bayesian approach as well as missing details in the theory a comparison between what is happening (feedforward) that are needed to design a scalable and universal model. and what you expect to happen (feedback). -
Unsupervised Anomaly Detection in Time Series with Recurrent Neural Networks
DEGREE PROJECT IN TECHNOLOGY, FIRST CYCLE, 15 CREDITS STOCKHOLM, SWEDEN 2019 Unsupervised anomaly detection in time series with recurrent neural networks JOSEF HADDAD CARL PIEHL KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE Unsupervised anomaly detection in time series with recurrent neural networks JOSEF HADDAD, CARL PIEHL Bachelor in Computer Science Date: June 7, 2019 Supervisor: Pawel Herman Examiner: Örjan Ekeberg School of Electrical Engineering and Computer Science Swedish title: Oövervakad avvikelsedetektion i tidsserier med neurala nätverk iii Abstract Artificial neural networks (ANN) have been successfully applied to a wide range of problems. However, most of the ANN-based models do not attempt to model the brain in detail, but there are still some models that do. An example of a biologically constrained ANN is Hierarchical Temporal Memory (HTM). This study applies HTM and Long Short-Term Memory (LSTM) to anomaly detection problems in time series in order to compare their performance for this task. The shape of the anomalies are restricted to point anomalies and the time series are univariate. Pre-existing implementations that utilise these networks for unsupervised anomaly detection in time series are used in this study. We primarily use our own synthetic data sets in order to discover the networks’ robustness to noise and how they compare to each other regarding different characteristics in the time series. Our results shows that both networks can handle noisy time series and the difference in performance regarding noise robustness is not significant for the time series used in the study. LSTM out- performs HTM in detecting point anomalies on our synthetic time series with sine curve trend but a conclusion about the overall best performing network among these two remains inconclusive. -
Neuromorphic Architecture for the Hierarchical Temporal Memory Abdullah M
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 1 Neuromorphic Architecture for the Hierarchical Temporal Memory Abdullah M. Zyarah, Student Member, IEEE, Dhireesha Kudithipudi, Senior Member, IEEE, Neuromorphic AI Laboratory, Rochester Institute of Technology Abstract—A biomimetic machine intelligence algorithm, that recognition and classification [3]–[5], prediction [6], natural holds promise in creating invariant representations of spatiotem- language processing, and anomaly detection [7], [8]. At a poral input streams is the hierarchical temporal memory (HTM). higher abstraction, HTM is basically a memory based system This unsupervised online algorithm has been demonstrated on several machine-learning tasks, including anomaly detection. that can be trained on a sequence of events that vary over time. Significant effort has been made in formalizing and applying the In the algorithmic model, this is achieved using two core units, HTM algorithm to different classes of problems. There are few spatial pooler (SP) and temporal memory (TM), called cortical early explorations of the HTM hardware architecture, especially learning algorithm (CLA). The SP is responsible for trans- for the earlier version of the spatial pooler of HTM algorithm. forming the input data into sparse distributed representation In this article, we present a full-scale HTM architecture for both spatial pooler and temporal memory. Synthetic synapse design is (SDR) with fixed sparsity, whereas the TM learns sequences proposed to address the potential and dynamic interconnections and makes predictions [9]. occurring during learning. The architecture is interweaved with A few research groups have implemented the first generation parallel cells and columns that enable high processing speed for Bayesian HTM. Kenneth et al., in 2007, implemented the the HTM. -
Memory-Prediction Framework for Pattern Recognition: Performance
Memory–Prediction Framework for Pattern Recognition: Performance and Suitability of the Bayesian Model of Visual Cortex Saulius J. Garalevicius Department of Computer and Information Sciences, Temple University Room 303, Wachman Hall, 1805 N. Broad St., Philadelphia, PA, 19122, USA [email protected] Abstract The neocortex learns sequences of patterns by storing This paper explores an inferential system for recognizing them in an invariant form in a hierarchical neural network. visual patterns. The system is inspired by a recent memory- It recalls the patterns auto-associatively when given only prediction theory and models the high-level architecture of partial or distorted inputs. The structure of stored invariant the human neocortex. The paper describes the hierarchical representations captures the important relationships in the architecture and recognition performance of this Bayesian world, independent of the details. The primary function of model. A number of possibilities are analyzed for bringing the neocortex is to make predictions by comparing the the model closer to the theory, making it uniform, scalable, knowledge of the invariant structure with the most recent less biased and able to learn a larger variety of images and observed details. their transformations. The effect of these modifications on The regions in the hierarchy are connected by multiple recognition accuracy is explored. We identify and discuss a number of both conceptual and practical challenges to the feedforward and feedback connections. Prediction requires Bayesian approach as well as missing details in the theory a comparison between what is happening (feedforward) that are needed to design a scalable and universal model. and what you expect to happen (feedback). -
The Neuroscience of Human Intelligence Differences
Edinburgh Research Explorer The neuroscience of human intelligence differences Citation for published version: Deary, IJ, Penke, L & Johnson, W 2010, 'The neuroscience of human intelligence differences', Nature Reviews Neuroscience, vol. 11, pp. 201-211. https://doi.org/10.1038/nrn2793 Digital Object Identifier (DOI): 10.1038/nrn2793 Link: Link to publication record in Edinburgh Research Explorer Document Version: Peer reviewed version Published In: Nature Reviews Neuroscience Publisher Rights Statement: This is an author's accepted manuscript of the following article: Deary, I. J., Penke, L. & Johnson, W. (2010), "The neuroscience of human intelligence differences", in Nature Reviews Neuroscience 11, p. 201-211. The final publication is available at http://dx.doi.org/10.1038/nrn2793 General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 02. Oct. 2021 Nature Reviews Neuroscience in press The neuroscience of human intelligence differences Ian J. Deary*, Lars Penke* and Wendy Johnson* *Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh EH4 2EE, Scotland, UK. All authors contributed equally to the work. -
Approximate Pattern Matching Using Hierarchical Graph Construction and Sparse Distributed Representation
Portland State University PDXScholar Electrical and Computer Engineering Faculty Publications and Presentations Electrical and Computer Engineering 7-23-2019 Approximate Pattern Matching using Hierarchical Graph Construction and Sparse Distributed Representation Aakanksha Mathuria Portland State University, [email protected] Dan Hammerstrom Portland State University Follow this and additional works at: https://pdxscholar.library.pdx.edu/ece_fac Part of the Engineering Commons Let us know how access to this document benefits ou.y Citation Details "Approximate Pattern Matching using Hierarchical Graph Construction and Sparse Distributed Representation," A. Mathuria, D.W. Hammerstrom, International Conference on Neuromorphic Systems, Knoxville, TN, July 23-5, 2019 This Conference Proceeding is brought to you for free and open access. It has been accepted for inclusion in Electrical and Computer Engineering Faculty Publications and Presentations by an authorized administrator of PDXScholar. Please contact us if we can make this document more accessible: [email protected]. Approximate Pattern Matching using Hierarchical Graph Construction and Sparse Distributed Representation Aakanksha Mathuria Electrical and Computer Engineering Portland State University USA [email protected] Dan W. Hammerstrom Electrical and Computer Engineering Portland State University USA [email protected] ABSTRACT training on large numbers of images. None of these techniques actually captures the spatial relationships of the low level or high- With recent developments in deep networks, there have been level features, which biological networks appear to do. significant advances in visual object detection and Efficient graph representations capture the higher order recognition. However, some of these networks are still easily information content of the objects and provide algorithmic benefits fooled/hacked and have shown “bag of features” failures. -
On Intelligence As Memory
Artificial Intelligence 169 (2005) 181–183 www.elsevier.com/locate/artint Book review Jeff Hawkins and Sandra Blakeslee, On Intelligence, Times Books, 2004. On intelligence as memory Jerome A. Feldman International Computer Science Institute, Berkeley, CA 94704-1198, USA Available online 3 November 2005 On Intelligence by Jeff Hawkins with Sandra Blakeslee has been inspirational for non- scientists as well as some of our most distinguished biologists, as can be seen from the web site (http://www.onintelligence.org). The book is engagingly written as a first person memoir of one computer engineer’s search for enlightenment on how human intelligence is computed by our brains. The central insight is important—much of our intelligence comes from the ability to recognize complex situations and to predict their possible outcomes. There is something fundamental about the brain and neural computation that makes us intelligent and AI should be studying it. Hawkins actually understates the power of human associative memory. Because of the massive parallelism and connectivity, the brain essentially reconfigures itself to be constantly sensitive to the current context and goals [1]. For example, when you are planning to buy some kind of car, you start noticing them. The book is surely right that better AI systems would follow if we could develop programs that were more like human memory. For whatever reason, memory as such is no longer studied much in AI—the Russell and Norvig [3] text has one index item for memory and that refers to semantics. From a scientific AI/Cognitive Science perspective, the book fails to tackle most of the questions of interest. -
Beneficial AI 2017
Beneficial AI 2017 Participants & Attendees 1 Anthony Aguirre is a Professor of Physics at the University of California, Santa Cruz. He has worked on a wide variety of topics in theoretical cosmology and fundamental physics, including inflation, black holes, quantum theory, and information theory. He also has strong interest in science outreach, and has appeared in numerous science documentaries. He is a co-founder of the Future of Life Institute, the Foundational Questions Institute, and Metaculus (http://www.metaculus.com/). Sam Altman is president of Y Combinator and was the cofounder of Loopt, a location-based social networking app. He also co-founded OpenAI with Elon Musk. Sam has invested in over 1,000 companies. Dario Amodei is the co-author of the recent paper Concrete Problems in AI Safety, which outlines a pragmatic and empirical approach to making AI systems safe. Dario is currently a research scientist at OpenAI, and prior to that worked at Google and Baidu. Dario also helped to lead the project that developed Deep Speech 2, which was named one of 10 “Breakthrough Technologies of 2016” by MIT Technology Review. Dario holds a PhD in physics from Princeton University, where he was awarded the Hertz Foundation doctoral thesis prize. Amara Angelica is Research Director for Ray Kurzweil, responsible for books, charts, and special projects. Amara’s background is in aerospace engineering, in electronic warfare, electronic intelligence, human factors, and computer systems analysis areas. A co-founder and initial Academic Model/Curriculum Lead for Singularity University, she was formerly on the board of directors of the National Space Society, is a member of the Space Development Steering Committee, and is a professional member of the Institute of Electrical and Electronics Engineers (IEEE). -
Mapping Mind-Brain Development: Towards a Comprehensive Theory
Journal of Intelligence Review Mapping Mind-Brain Development: Towards a Comprehensive Theory George Spanoudis 1,* and Andreas Demetriou 2,3 1 Psychology Department, University of Cyprus, 1678 Nicosia, Cyprus 2 Department of Psychology, University of Nicosia, 1700 Nicosia, Cyprus; [email protected] 3 Cyprus Academy of Science, Letters, and Arts, 1700 Nicosia, Cyprus * Correspondence: [email protected]; Tel.: +357-22892969 Received: 20 January 2020; Accepted: 20 April 2020; Published: 26 April 2020 Abstract: The relations between the developing mind and developing brain are explored. We outline a theory of intellectual development postulating that the mind comprises four systems of processes (domain-specific, attention and working memory, reasoning, and cognizance) developing in four cycles (episodic, realistic, rule-based, and principle-based representations, emerging at birth, 2, 6, and 11 years, respectively), with two phases in each. Changes in reasoning relate to processing efficiency in the first phase and working memory in the second phase. Awareness of mental processes is recycled with the changes in each cycle and drives their integration into the representational unit of the next cycle. Brain research shows that each type of processes is served by specialized brain networks. Domain-specific processes are rooted in sensory cortices; working memory processes are mainly rooted in hippocampal, parietal, and prefrontal cortices; abstraction and alignment processes are rooted in parietal, frontal, and prefrontal and medial cortices. Information entering these networks is available to awareness processes. Brain networks change along the four cycles, in precision, connectivity, and brain rhythms. Principles of mind-brain interaction are discussed. Keywords: brain; mind; development; intelligence; mental and brain changes; brain networks 1. -
Intelligence As a Developing Function: a Neuroconstructivist Approach
Journal of Intelligence Article Intelligence as a Developing Function: A Neuroconstructivist Approach Luca Rinaldi 1,2,* and Annette Karmiloff-Smith 3 1 Department of Brain and Behavioural Sciences, University of Pavia, Pavia 27100, Italy 2 Milan Center for Neuroscience, Milano 20126, Italy 3 Centre for Brain and Cognitive Development, Birkbeck, London WC1E 7HX, UK; [email protected] * Correspondence: [email protected]; Tel.: +39-02-6448-3775 Academic Editors: Andreas Demetriou and George Spanoudis Received: 23 December 2016; Accepted: 27 April 2017; Published: 29 April 2017 Abstract: The concept of intelligence encompasses the mental abilities necessary to survival and advancement in any environmental context. Attempts to grasp this multifaceted concept through a relatively simple operationalization have fostered the notion that individual differences in intelligence can often be expressed by a single score. This predominant position has contributed to expect intelligence profiles to remain substantially stable over the course of ontogenetic development and, more generally, across the life-span. These tendencies, however, are biased by the still limited number of empirical reports taking a developmental perspective on intelligence. Viewing intelligence as a dynamic concept, indeed, implies the need to identify full developmental trajectories, to assess how genes, brain, cognition, and environment interact with each other. In the present paper, we describe how a neuroconstructivist approach better explains why intelligence can rise or fall over development, as a result of a fluctuating interaction between the developing system itself and the environmental factors involved at different times across ontogenesis. Keywords: intelligence; individual differences; development; neuroconstructivism; emergent structure; developmental trajectory 1. -
Conversation with Jeff Hawkins – on Defining Intelligence
Episode 12: Conversation with Jeff Hawkins – On Defining Intelligence Matt: 00:00 Today on the Numenta On Intelligence podcast. Jeff: 00:04 You see something to believe the earth is flat and you give them all these facts that don't fit that model. Now, the Earth is flat predicts certain things. Right. And, and, and they will do their damnedest to try to fit these new facts into their model, no matter how, that's what they're going to, otherwise you have to throw the whole thing away and start over again, which is a really disconcerting thing for them. Matt: 00:26 It is. It is uncomfortable, to have to throw away a whole frame of reference. Jeff: 00:30 Basically, the models you build in the world are your reality. Matt: 00:34 It's your belief system. Jeff: 00:34 It's your belief system. It's your reality. This is what you know, that's the, oh, that's what it boils down to. There is no other reality in terms of your head. This is it. Instead of, if you say, well, you know what? Everything you believed these references, and you have to start over again. Um, then it's kind of like, oh, you're going back to square one. Matt: 00:49 Jeff Hawkins on defining intelligence. What is it? What makes us intelligent? How was our intelligence different from a mouse'? We're narrowing down the answers to some pretty existential questions. I'm Numenta community manager, Matt Taylor. -
Hierarchical Temporal Memory (HTM)
Hierarchical Temporal Memory (HTM) Computational Cognitive Neuroscience COSC 521, Spring 2019 Corey Johnson Resources for information on HTM Numenta.org HTM school NuPIC Jeff Hawkins 2004 book: On Intelligence: How a New Understanding of the Brain will Lead to the Creation of Truly Intelligent Machines (Note: some slide graphics are from Numenta.org, HTM school) 2 Hierarchical ● Higher levels → abstraction & permanence Temporal ● Change over time: patterns Memory ● Sparse Distributed Representation (SDR) 3 HTMs ● Biologically plausible model for intelligence ● Based on pyramidal neurons ● Neocortex micro-columns as building blocks "The neocortex comprises about 75% of the volume of the human brain and it is the seat of most of what we think of as intelligence." -Jeff Hawkins 4 HTMs continued ● Can learn / recall / infer high-order sequences ● Local learning rules, no global supervisor (HW) ● Relies on sparse distributed representation (SDR) ○ Fault tolerance ○ High capacity ● Learns by modeling the growth of new synapses 5 ANN Pyramidal HTM 6 HTM Layers (Lect. 2 slides) 7 Sparse Distributed Representation Fundamental to HTM systems SDR: 'Language of intelligence' Sparse as opposed to dense binary code, many bits are needed SDR bit similarity → similar semantic meaning Semantic error is key to generalization Cell only needs a few connections to neighbor to match pattern 8 Applications Good for: ● Data streams that change over time: text, GPS, dates, numbers ● Data with inherent structure ● System where many models are required rather than