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sparse distributed memory using N-of-M codes

NEURAL NETWORKS 2004年第10期17卷 1437-1451页

作者： Furber, SB Bainbridge, WJ Cumpstey, JM Temple, S Univ Manchester Dept Comp Sci Manchester M13 9PL Lancs England

An analysis is presented of a sparse distributed memory (SDM) inspired by that described by Kanerva [Kanerva, P. (1988). sparse distributed memory. Cambridge, MA: MIT Press] but modified to facilitate an implementation based on spiking neurons. The memory presented here employs sparse binary N-of-M codes, unipolar binary synaptic weights and a simple Hebbian learning rule. It is a two-layer network. the first (fixed) layer being similar to the `address decoder' in Jaeckel's [Jaeckel, L.A. (1989). A class of designs for a sparse distributed memory. RIACS Technical Report 89.30, NASA Ames Research Centre] `hyperplane' variant of Kanerva's SDM and the second (writeable) 'data store' layer being a correlation matrix memory as first proposed by Willshaw et al. [Willshaw, D. J., Buneman, O.P., & Longuet-Higgins, H.C. (1969). Non-holographic associative memory. Nature, 222, 960-962]. The resulting network is shown to have good storage efficiency and is scalable. The analysis is supported by numerical simulations and gives results that enable the configuration of the memory to be optinused for a range of noiseless and noisy environments. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： neural networks spiking neurons sparse distributed memory associative memory correlation matrix memory unipolar weights N-of-M codes

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sparse distributed memory for 'conscious' software agents

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Cognitive Systems Research 2003年第4期4卷 339-354页

作者： Anwar, Ashraf Franklin, Stan Institute for Intelligent Systems The University of Memphis Memphis TN 38152 United States

In this work we are reporting a case study on the use of SDM as the associative memory for a software agent, CMattie, whose architecture is modeled on human cognition. sparse distributed memory (SDM) is a content-addressable memory technique that relies on close memory items tending to be clustered together. In this work, we used an enhanced version of SDM augmented with the use of genetic algorithms as an associative memory in our 'conscious' software agent, CMattie, who is responsible for emailing seminar announcements in an academic department. Interacting with seminar organizers via email in natural language, CMattie can replace the secretary who normally handles such announcements. SDM is a key ingredient in a complex agent architecture that implements global workspace theory, a psychological theory of consciousness and cognition. In this architecture, SDM, as the primary memory for the agent, provides associations with incoming percepts. These include disambiguation of the percept by removing noise, correcting misspellings, and adding missing pieces of information. It also retrieves behaviors and emotions associated with the percept. These associations are based on previous similar percepts, and their consequences, that have been recorded earlier. SDM also possesses several key psychological features. Some enhancements to SDM including multiple writes of important items, use of error detection and correction, and the use of hashing to map the original information into fixed size keys were used. Test results indicate that SDM can be used successfully as an associative memory in such complex agent architectures. The results show that SDM is capable of recovering a percept based on a part of that percept, and finding defaults for empty perception registers. The evaluation of suggested actions and emotional states is satisfactory. We think that this work opens the door to more scientific and empirical uses for SDM. © 2003 Elsevier B.V. All rights reserved.

关键词： Artificial intelligence Cognition Consciousness Genetic algorithms Software agents sparse distributed memory

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sparse distributed memory for Experience-Based Robot Manipulation

Sparse Distributed Memory for Experience-Based Robot Manipul...

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IEEE International Conference on Robotics and Biomimetics (ROBIO)

作者： Jockel, Sascha Lindner, Felix Zhang, Jianwei Univ Hamburg CINACS Int Res Training Grp Vogt Kolln Str 30 D-22527 Hamburg Germany

ISBN: (纸本)9781424426782

sparse distributed memory (SDM) is a mathematical technique based on the properties of high-dimensional space for storing and retrieving large binary patterns. This model has been proposed for cerebellar functions, and has been used in simple visual and linguistic applications to date. This paper presents an SDM for robotic applications, especially for storing and recognising mobile manipulation actions of a 6-DOF robot arm. Sequences of events are stored as subjective experiences and are later used to guide robot arm behaviour based on its memory content. Several simple manipulation tasks, such as lift and place a wastebin from and on the floor, push an object aside on a table-top, and draw shapes in the air are analysed under different operation modes. The robot system shows good reproduction abilities of task-dependent arm trajectories based on sparse distributed memory. Moreover, the content-addressable, associative memory even predicts the residual arm trajectory of a task if the arm is placed somewhere close to a learnt trajectory.

关键词： sparse distributed memory cognition service robotics mobile manipulation sensorimotor learning associative memory motion prediction

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sparse distributed memory for Binary sparse distributed Representations 2022

Sparse Distributed Memory for Binary Sparse Distributed Repr...

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7th International Conference on Machine Learning Technologies (ICMLT)

作者： Vdovychenko, Ruslan Tulchinsky, Vadim Natl Acad Sci Ukraine VM Glushkov Inst Cybernet Kiev Ukraine

ISBN: (纸本)9781450395748

sparse distributed memory (SDM) and Binary distributed Representations (BDR), as two phenomenological approaches to biological memory modelling, have a lot of common features. The idea of their integration in a hybrid semantic storage model with SDM used as the low (brain cell) level cleaning memory for BDR used as the high-level symbolic information coder seems natural. The hybrid semantic storage must be able to memorize holistic data (like the structures of interconnected and serialized key-value pairs) in a neural network. It has been proposed several times since 1990(th). However, the earlier proposed models are not practical because of insufficient scalability and/or low storage density. The gap between SDM and BDR can be filled using the results of a 3(rd) theory dealing with sparse signals: Compressive Sensing or Sampling (CS). Such a hybrid semantic storage model is presented. We call it CS-SDM to reflect using a new CS-based SDM design as the cleaning memory for a Binary sparse distributed Representation (BSDR) of the holistic data. CS-SDM has been implemented on GPU and demonstrated much better capacity and denoising capabilities than classical SDM designs.

关键词： sparse distributed memory Compressive Sensing Compressive Sampling Binary sparse distributed Representations Vector Symbolic Architecture associative memory neural networks GPU

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Extended sparse distributed memory and Sequence Storage

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COGNITIVE COMPUTATION 2012年第2期4卷 172-180页

作者： Snaider, Javier Franklin, Stan Univ Memphis Dept Comp Sci FedEx Inst Technol Memphis TN 38152 USA Univ Memphis Inst Intelligent Syst FedEx Inst Technol Memphis TN 38152 USA

sparse distributed memory (SDM) is an auto-associative memory system that stores high-dimensional Boolean vectors. SDM uses the same vector for the data (word) and the location where it is stored (address). Here, we present an extension of the original SDM that uses word vectors of larger size than address vectors. This extension preserves many of the desirable properties of the original SDM: auto-associability, content addressability, distributed storage and robustness over noisy inputs. In addition, it adds new functionality, enabling an efficient auto-associative storage of sequences of vectors, as well as of other data structures such as trees. Simulations testing this new memory are described.

关键词： sparse distributed memory Episodic memory Sequence representation Cognitive modeling

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Assigning meaning to data: Using sparse distributed memory for multilevel cognitive tasks

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NEUROCOMPUTING 1997年第1期14卷 15-39页

作者： Manevitz, LM Zemach, Y POLYTECH UNIV NEW YORKNY INTEL CORP HAIFAISRAEL

It is shown how a single homogeneous SDM memory can be organized to link between low level information and high level correlations. To illustrate this, we report on experiments run in a unified memory retrieval system, that combined pattern recognition of individual English characters followed by the assignment of 'meaning' to a string by giving it a Hebrew translation. Symmetry allows the reverse action on the same memory (i.e. Hebrew character identification followed by translation of a string to English).

关键词： sparse distributed memory high-order-correlation associative memory

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Robot navigation based on view sequences stored in a sparse distributed memory

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ROBOTICA 2012年第4期30卷 571-581页

作者： Mendes, Mateus Paulo Coimbra, A. Crisostomo, Manuel M. Polytech Inst Coimbra ESTGOH P-3400124 Oliveira Do Hosp Portugal Inst Syst & Robot P-3030290 Coimbra Portugal

Robot navigation is a large area of research, where many different approaches have already been tried, including navigation based on visual memories. The sparse distributed memory (SDM) is a kind of associative memory based on the properties of high-dimensional binary spaces. It exhibits characteristics, such as tolerance to noise and incomplete data, ability to work with sequences and the possibility of one-shot learning. Those characteristics make it appealing to use for robot navigation. The approach followed here was to navigate a robot using sequences of visual memories stored into a SDM. The robot makes intelligent decisions, such as selecting only relevant images to store, adjusting memory parameters to the level of noise and inferring new paths from the learnt trajectories. The method of encoding the information may influence the tolerance of the SDM to noise and saturation. This paper reports novel results of the limits of the model under different typical navigation problems. The SDM showed to be very robust to illumination and scenario changes, occlusion and saturation. An algorithm to build a topological map of the environment based on the visual memories is also described.

关键词： sparse distributed memory SDM Robot navigation Data encoding Vision-based navigation

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Analogical Mapping with sparse distributed memory: A Simple Model that Learns to Generalize from Examples

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COGNITIVE COMPUTATION 2014年第1期6卷 74-88页

作者： Emruli, Blerim Sandin, Fredrik Lulea Univ Technol EISLAB S-97187 Lulea Sweden

We present a computational model for the analogical mapping of compositional structures that combines two existing ideas known as holistic mapping vectors and sparse distributed memory. The model enables integration of structural and semantic constraints when learning mappings of the type and computing analogies for novel inputs x (j) . The model has a one-shot learning process, is randomly initialized, and has three exogenous parameters: the dimensionality of representations, the memory size S, and the probability chi for activation of the memory. After learning three examples, the model generalizes correctly to novel examples. We find minima in the probability of generalization error for certain values of chi, S, and the number of different mapping examples learned. These results indicate that the optimal size of the memory scales with the number of different mapping examples learned and that the sparseness of the memory is important. The optimal dimensionality of binary representations is of the order 10(4), which is consistent with a known analytical estimate and the synapse count for most cortical neurons. We demonstrate that the model can learn analogical mappings of generic two-place relationships, and we calculate the error probabilities for recall and generalization.

关键词： Analogical mapping Compositional structures distributed representations Holistic processing sparse distributed memory

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A genetic sparse distributed memory approach to the application of handwritten character recognition

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PATTERN RECOGNITION 1997年第12期30卷 2015-2022页

作者： Fan, KC Wang, YK Natl Cent Univ Inst Comp Sci & Informat Engn Chungli 32054 Taiwan Acad Sinica Inst Informat Sci Taipei Taiwan

Kanerva's sparse distributed memory (SDM) is one of the self-organizing neural networks that mimic closely the psychological behavior of the human brain. In this paper, a Genetic sparse distributed memory (GSDM) model that combines SDM with genetic algorithms is proposed. The proposed GSDM model not only maintains the advantages of both SDM and genetic algorithms, but also has higher memory utilization to improve the recognition rate. Its effective performance is also verified by application to Optical Character Recognition (OCR). Experimental results reveal the feasibility and validity of the proposed model. (C) 1997 Pattern Recognition Society. Published by Elsevier Science Ltd.

关键词： optical character recognition sparse distributed memory neural networks genetic algorithms

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Integer sparse distributed memory: Analysis and results

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NEURAL NETWORKS 2013年 46卷 144-153页

作者： Snaider, Javier Franklin, Stan Strain, Steve George, E. Olusegun Univ Memphis Dept Comp Sci Memphis TN 38152 USA Univ Memphis Inst Intelligent Syst Memphis TN 38152 USA Univ Memphis Dept Biomed Engn Memphis TN 38152 USA Univ Memphis Dept Math Sci Memphis TN 38152 USA

sparse distributed memory is an auto-associative memory system that stores high dimensional Boolean vectors. Here we present an extension of the original SDM, the Integer SDM that uses modular arithmetic integer vectors rather than binary vectors. This extension preserves many of the desirable properties of the original SDM: auto-associativity, content addressability, distributed storage, and robustness over noisy inputs. In addition, it improves the representation capabilities of the memory and is more robust over normalization. It can also be extended to support forgetting and reliable sequence storage. We performed several simulations that test the noise robustness property and capacity of the memory. Theoretical analyses of the memory's fidelity and capacity are also presented. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： sparse distributed memory High dimensional space Auto-associative memory

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