an overview of hierarchical temporal memory: a new ... · application of hierarchical temporal...

Abstract—The overview presents the development and application of Hierarchical Temporal Memory (HTM). HTM is a new machine learning method which was proposed by Jeff Hawkins in 2005. It is a biologically inspired cognitive method based on the principle of how human brain works. The method invites hierarchical structure and proposes a memory-prediction framework, thus making it able to predict what will happen in the near future. This overview mainly introduces the developing process of HTM, as well as its principle, characteristics, advantages and applications in vision, image processing and robots movement, some potential applications by using HTM , such as thinking process, are also put forward.

Index Terms—hierarchical Bayesian network; spatial-temporal; memory-prediction; temporal sequence; pattern recognition

I. INTRODUCTION

N recent years a new theory on brain function has been presented by Jeff Hawkins[1], who is a brain scientists and

founder of the redwood neuroscience research institute. The main tenets of this new theory can be modeled using Bayesian network[2], but virtually there exists some differences. This model is called Hierarchical Temporal Memory network. It is a stimulant paradigm with a new set of bio-inspired suppositions, which putting theories about neocortical function into a set of algorithms. HTM theory incorporates the hierarchical organization of the mammalian neocortex into its topological architecture [3].

HTM can be thought of a special kind of hierarchical Bayesian model. It also uses spatial-temporal theory [4] as a way to encapsulate and learn the structure and invariance of problems’ space. Hierarchical organization and spatial-temporal coding are both well documented principles of information processing in neural systems.

Bayesian network [5] was first proposed by J. Pearl in

.The manuscript is submitted on March 15, 2012. This work is

partially supported by National Natural Science Foundation of China (Grant No. 70903026)

Xi Chen, Associate professor in the Institute of System engineering, Huazhong University of Science & Technology, Image Processing and Intelligent Control Key Laboratory of Education Ministry of China, Wuhan, Hubei, China, 430074 (Phone: +86-27-87540210. Email: [email protected])

Wei Wang, *Corresponding author, Candidate for Master of the Institute of System engineering, Huazhong University of Science & Technology, Image Processing and Intelligent Control Key Laboratory of Education Ministry of China(Phone: 15549088762. Email: [email protected])

Wei Li: Associate professor in the Department of Control Science & Engineering, Image Processing and Intelligent Control Key Laboratory of Education Ministry of China (Phone: +86-27-87556242. Email: [email protected].)

1988. Then T.S. Lee and D. Mumford used Bayesian network in invariant pattern recognition seen in the visual cortex [6]. Finally T. Dean proposed a hierarchical Bayesian

model [7] based on the work by Lee and Mumford [23]ca A 2 (7) (2003), pp. 1434-1448. Full Text via CrossRef to model the invariant pattern recognition in 2006. HTM can be considered a form of Bayesian network, where both the networks consist of a collection of nodes arranged in a tree-shaped hierarchy and all nodes share the same computing algorithm, both networks use Bayesian-belief propagation mechanism. Input data is preprocessed before being fed to the bottom layer of nodes. There are several feed-forward and feed-back channels through the networks to allow a proper distribution of information throughout the networks. So they both need a set of training data to be put into the bottom layer of nodes multiple times [2].

However, unlike many Bayesian networks, HTMs are self-training, have a well-defined parent/child relationship between each node. What is the most important, HTM emphasizes the significance of “temporal”, pointed that every event in the world, is all composed by tiny element [8], that’s to say, the world is a hierarchical structure and in the final analysis can be broken down into basic constitutions. The temporal sequences of patterns lead to memory. Inherently HTM method handles time-varying data and affords mechanisms for covert attention, in this way, it achieves prediction.

The concept of spatial-temporal [9] was first proposed by Torsten Hägerstrand in 1970. It was proposed based on the study of human migration patterns, emphasized the importance of time in human activities. During decades of years’ research, researchers established the relationship between human’s social behavior and human intelligence [10]. Sun and Giles presented a useful overview [11] including the characteristics, problems, and challenges for sequence learning from recognition and prediction to sequential decision making. Temporal sequence learning [12] is one of the most critical components for human intelligence. On the other hand, considering that any event exists within the space, so spatial factor is indispensable along with temporal factor in human intelligence. Time and space provide contrasting perspectives on events. A temporal perspective highlights the sequence of transitions, the dynamic changes from segment to segment, reflecting things in motion. However, a spatial perspective emphasizes the sequence of states, the static spatial configuration, reflecting things caught still. Handling the temporal and the spatial at once seems out of control, but the dynamic and the static appear to complement each other [8].Spatial and temporal relations

An Overview of Hierarchical Temporal Memory: A New Neocortex Algorithm

Xi Chen, Wei Wang, Wei Li

I

Proceedings of 2012 International Conference on Modelling, Identification and Control, Wuhan, China, June 24-26, 2012

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are learned in a hierarchical architecture. This method is currently successfully applied to the recognition of simple images [13].

HTM, actually, performs temporal and spatial process, but not completely copy the theory of spatial-temporal. In nature, HTM inherits the spirit of spatial-temporal, which exhibiting the two points of thinking and recognition things in the world, but how does HTM perform the process in deeds, it involves some other algorithm. In a word, HTM is a integration method which combining hierarchical Bayesian network with spatial-temporal model to gather their advantages, finally result in an advanced algorithm which can learn and recognize in hierarchy and make prediction based on the knowledge learned previously.

The rest of this paper is organized as follows: In section 2, the basic principle and developing process about HTM will be demonstrated through three sections: 2.1 will briefly introduce HTM; then the first generation of HTM algorithm and the contribution which Jeff Hawkins made on human intelligence and artificial intelligence will be explained in 2.2; in section 2.3, the second generation algorithm will be presented. In section 3, the application of HTM will be enumerated and concluded. In section 4, it’s the author’s conclusion.

II. HTM MODEL

A. HTM Overview

HTM is a biological-inspired method which built on the architecture of neocortex and trying to model the process of how human brain handles the information about vision, audio, behavior etc, thus leading to memory and prediction. An HTM system is trained rather than programming realized, the underlying learning algorithms used in it are not specific to particular sensory domains and can be applied to a broad set of problems that involve modeling complex sensory data. HTM has experienced a long journey from its rudiment to current achievement.

All the progress owes to Jeff Hawkins’ persistence and exploration. He is a neuroscience investigator and pursuing his dream of building machines with intelligence. He believes that the only way to build intelligent systems is by looking at how the brain works, especially its architecture [14]. Years later, he published his book On Intelligence, which indicated the first bridge connecting neuroscience and artificial intelligence. In this book, Hawkins mentioned the function of prediction of neocortex, pointed that prediction is not only the main function of neocortex, but also the basement of intelligence. These predictions come out based on memories which store in neocortex. New patterns compare with stored patterns to find similarities, according to these awake corresponding memories in neocortex, these awaken memories then lead to predictions. That is the framework of Memory-Prediction which also becomes the new framework of intelligence. Then the AI community is taking notice of Hawkins’ work and many researchers begin to seek deeper connections between

neuroscience and AI. Most algorithms, however, covered only one or few functions about human brain. Hawkins studied these theories thoroughly and extracted their redeeming features, proposed the HTM after amalgamation. HTM firstly learns and memories the spatial patterns, as well as those patterns which usually happens at the same time, then recognizes the temporal sequence of those spatial patterns appeared one after another, at last, these stored patterns and sequence help produce prediction when new pattern which is similar to stored pattern comes in. Hawkins admits that many of the ideas in his theory aren’t new. What is new, he says, is “putting the correct pieces together in the right way with an overall theoretical framework”.

Then in 2005, Jeff Hawkins and his fellows Dileep George, Donna Dubinsky founded a company called Numenta[15]. It’s a corporation which applying itself to the research of HTM network and aiming at extending the impact of the HTM technology as well as its potential applications. Now the research carrying through mainly builds on a software development platform called NuPIC (Numenta Platform for Intelligent Computing). It’s Numenta’s legacy software which contains several generations of versions and provides tools to create, train, test and deploy HTM. So far Numenta has released two versions of NuPIC. The first one is version 1.7.1 called Zeta, released in 2009, which allows programmers to create and test HTMs on their own problems with their own data sets. It can only support the first generation algorithm but not actively useful for second generation. The new version which is able to provide experiment platform for the second generation algorithm is in the process of developing. Compared with Zeta, the new version is further well-rounded which will incorporate the new generation of algorithm, simultaneously permitting non-programmers to experiment with creating, training and doing inference on HTM prototypes with image classification and recognition.

It’s believed that biological principles will drive the next generation of intelligent computing. And the sustaining developing HTM theory is thought of a catalyst for this new age with the software development platform.

B. First Generation of HTM Algorithm

As mentioned above, until now Numenta has turned up two generations of HTM algorithms. The first generation which named “The HTM Learning Algorithms” [16] was proposed by Dileep George and Bobby Jaros who both are members of Numenta. The algorithm was published on white paper in their company in March 1, 2007. It indicated the elementary theory of HTM formed formally, following the outline of the theory described by Jeff Hawkins in his book On Intelligence.

In fact, before the release of first generation of HTM algorithm, much more work has been done by researchers. This mainly included the introduction of fundamental concepts and terminology behind HTM. In 2006, Jeff published a paper named Hierarchical Temporal Memory:


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Concepts, Theory and Terminology[17], It was a precursor of the first generation of HTM algorithm. In the same year, George Dileep pronounced a paper[18] to summarize the possible algorithms behind HTM and expect the potential application fields of HTM.

The foremost principle of HTM is time and hierarchy factors in the vision problem. The researchers got the idea through anatomizing neocortex and fond that human and other mammal’s neocortex share the same configuration, namely hierarchy. What’s more, they both recognize objects from a single snapshot of the image without integrating information over multiple time steps. These problems confused researchers and finally the temporal factor caught the researchers’ attention. Although apparently humans recognize with a snapshot, actually we learn with continuously varying data and use this temporal information to obtain important generalization characteristics.

Firstly the issue of vision problem was stated, pointed that mammalian recognizes objects in a unsupervised manner, where time acts as a supervisor to tell which patterns belong together and which patterns do not[19]. In other words, although two different images are not totally the same, the fact that they take place close by in time can be used to learn that they are produced by the same cause. This identity is called invariant representation. So Learning to recognize objects involves learning invariant representations, for an object’s identity remains the same although it experiences different transformations in the world. The concept of invariant representations firstly appeared on Jeff Hawkins’ book On Intelligence.

Then involves how HTM works and its training data as well as framework. The first generation of HTM algorithm takes the recognition of images as an example to illustrate the principle of HTM. Training data is a sequence of binary images presented in pixels, as Fig.1 shows.

Fig.1. Input data sequence into HTM network, it is a sequence of binary images presented in pixels along the time axis.

HTM network is a hierarchical configuration which contents numbers of nodes, these nodes share the same algorithm and each node has child node (except bottom nodes) and parent node (except top node), as Fig.2 shows. Lower nodes sensor smaller range of data and their stability is not a patch on higher nodes. On the contrary, higher nodes receive the output of their child nodes and amalgamate them, thus they sensor larger range of data and is more stable. The learning stage of HTM network covers training and inference. Each node operates two steps: spatial pooler and temporal pooler, as Fig.3 shows.

Fig.2. Structure of an HTM network for learning the binary images. This network is organized in 3 levels. Input is fed in at the bottom level. Nodes

are shown as squares. (This figure is quoted from the reference [16])

Fig.3 a completely learned node which both the spatial pooler and

temporal pooler have finished their learning processes. The spatial pooler now has 5 spatial groups and temporal pooler has 2 temporal groups. (This

figure is quoted from the reference [16])

After the appearance of first generation of HTM algorithm, researchers in Numenta and other institutions didn’t stop their steps for detailed and deeper studying on HTM. In 2007, Sara Reese Hedberg wrote a biography [14] to narrate Jeff Hawkins’ experience on the research of HTM since he was an academician and his contribution to neuroscience and artificial intelligence. Then one year later, Jeff Hawkins’ student Dileep George delivered his dissertation for doctor degree [20]. This paper presents the theory of how human’s neocortex performs, and then introduces the HTM’s framework and principle in detail. Afterward, George outlined a figure about intelligent machine which is able to learn, categorize and predict [21]. The ultimate purpose of researching the neocortex is to produce intelligent machine that realize artificial intelligence.

Besides, some other evaluation and betterment have come forth about HTM. John Thornton and Jolon Faichney have evaluated HTM’s ability to represent temporal sequences of input within a hierarchically structured vector quantization algorithm [22].The result revealed that the temporal pooler algorithm is a surprisingly independent approach that is immune from the use of preprocessing techniques. Unlike HTM model which built its framework on Bayesian network, Kiruthika Ramanathan and Luping Shi proposed a hierarchical temporal-spatial memory model based on neural network [23]. The spatial pooler process uses competitive neural network. It performs comparable


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recognition results to HTM and show definitely improvement over MLP. However, as competitive neural network is a full-connection network, the bulky computation can easily lead to the inefficient of the model.

C. Second Generation of HTM Algorithm

After about three years’ researching and improvement, the second generation of HTM algorithm was published on December 10, 2010 [24]. The paper is finished by chapter under the cooperation of the authors, but this is a draft version that some information is not available in it, the detailed and complete version is on the way of complement and consummating.

In general, compared with the first generation of HTM algorithm, the second generation mainly represents the problem of recognition on a level of global perspective, studies the architecture of neocortex from the view of biology anatomy. It mines the nerve connection in the neocortex and manages to find out the function of each type of nerve. In the second generation, node in each level is extended to a column, not just a single node in the first generation, as Fig. 4 shows. Each column contains several cells. Among cells there exist put-forward connection and lateral connection. The cells can be one of three states: active, inactive and predictive. If a cell is active due to put-forward input, then it is in active state, on the other hand, if it is active due to lateral connection, it is in predictive state. Columns only activate predicted cells. Those with no predicted cells in them activate all the cells in the column, as Fig.5 shows. The aim of this treatment is to make HTM level represent the same input in many different contexts. The cell in predictive state gives a signal of what will happen next based on current training.

Compared to the first generation version, the document describes the new algorithm for learning and prediction in detail. The second generation can be considered as the continuing work about On Intelligence and an improvement about first generation. The first generation mainly characterizes about HTM learning algorithm within single node and how nodes operate and connect to each other in a hierarchy to achieve system level results minutely, it primarily applies itself to vision problem

Fig.4. A section of HTM level, HTM level are comprised of many cells.

The cells are organized in a two dimensional array of columns. (This figure is quoted from the reference [24]).

Fig.5. Cells in different states. Columns with predicted cells only activate

predicted cells. Columns with no predicted cells activate all the cells in the column. (This figure is quoted from the reference [24]).

and takes image recognition as an example to illustrate HTM’s effect on recognition, while the second generation pays relatively more attention to application and the new algorithm is depicted in adequate detail so that a programmer can easily understand and implement it if desired. Besides, pseudocodes of the algorithm’s two processes namely spatial pooler and temporal pooler are presented in the document that favor of non-experienced programmers coming to the road of implementation as soon as possible. The new algorithm introduces some terminologies which don’t appear in the first generation such as region, sparse distributed representation. There is a biggest difference between the two algorithms, or calling it improvement, the new algorithm highlights the HTM’s function of prediction as well as key properties, and also breaks down the procedure a little further into three steps, which is thought of the most influential characteristic among existing AI algorithm. Each column which contains several nodes is called variable order sequence, and each column which contains only one node is called first order sequence. Variable order HTM is ideally suited for recognizing time-based sequences.

Thanks to the new algorithm is updated not long before, some improvements about it don’t come forth. However, around the releasing, some other scholars put forward their ideas from different perspectives. David Rozado, Francisco B. Rodriguez, and Pablo Varona optimized the HTM algorithm for multivariable time series [25]. In allusion to the problems involving multi-variable time series where samples unfold over time with no complete spatial representation at any point in time, HTM feels intractable and doesn’t perform well. Then this paper extends the traditional HTMs’ principles by means of a top node that stores and arranges sequences of input patterns representing the spatial-temporal structure of instances to be learned. The extended model is tested in the problem of sign language recognition which is used by deaf people and consists of an ordered sequence of hand movements. The result reveals much better performance relative to traditional HTM.

HTM is on the way of continuing development and attracting more and more focus of researchers coming to neocortex and artificial intelligence fields. The researchers pursue optimized architecture from detailed points and at the same time many applications based on HTM lift the boom of computing intelligence.


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III. APPLICATION

Pattern recognition has always been a hot research project. In these years, researchers have presented many theories and methods as well as improved optimizations to the questions of image recognition, human action recognition and so on. For instance, Yunzhi Jiang has proposed a Bayesian particle swarm optimization for image segmentation [26]. Qiming Fu has put forward some relevance feedback techniques and genetic algorithm for image retrieval [27]. Fawang Liu has researched human action recognition from silhouettes’ perspective using manifold learning [28].

However, HTM algorithms’ appearance stimulates the researchers interests in recognition and prediction not only vision recognition but also audio and behavior. Numbers of production come out and exhibit promising application prospect. Nearly all the papers were released after 2007, especially recent years, with the developing of algorithm learning, wider and deeper application domains have been taken on, come with the larger number of application results arose.

In the domain of vision recognition, HTM has been used in many aspects. Tomasz Kapuscinski and Marian Wysocki used HTM to recognize signed polish words [29]. One year later, Tomasz Kapuscinski published another paper [30]

applying HTM to recognize hand shape when hand in state of dynamic. Not only in hand movement, but in traffic signal recognition, HTM also plays a perfect assistant. Wim J.C. Melis studied the function of color channels on traffic light using HTM framework [31]. In hand-written digit recognition, on account of the nonstandard characteristic of hand-written digits, HTM were used mainly for prediction and achieved approving accuracy [32], [33]. Other applications about vision recognition involves image retrieval [34] and face recognition [35].

Up to now HTM has almost exclusively been applied to image processing. However, the underlying theory can also be used as an approach to active perception of audio signals

[36]. Actually, the speech recognition problem can be most easily cast in a form similar to image recognition. In despite of the present implementation is not perfectly suited for handling signals that encode information mainly in dynamic changes, the result all the same shows that the HTM approach holds promises for speech recognition.

Not only about vision and audio signals, but some simple behavior signals, HTM can also be used to deal with. Kwang-Ho Seok and Yoon Sang Kim used HTM to author robot motion [37] to produce humanoid machine with 26 degrees of freedom. N. Farahmand used HTM to build a high-level self-organizing visual system for a soccer bot [38] S. Zhang, and M. H Ang Jr.etc proposed a two-stage action recognition approach for detecting arm gesture related to human eating or drinking [39] . The process contained two steps: feature exaction and classification. The former applied Extended Kalman filter (EKF) to exact features from arm action in a three dimensional space, the latter

adopted HTM to receive the exacted features from EKF and classify the static actions or dynamic signals which are varying with both time and space. The experimental results show that the HTM and EKF based method can perform very high accuracy for the dynamic action detection. Another two papers involving human body movement detection which used HTM are respectively about human body movements during daily life and fall detection [40] , [41]. In these applications, HTM perform together with one or even more other techniques.

Beside these, HTM has some other applications. Firstly, in objects categorization, in 2007, Adam B. Csapo, Peter Baranyi and Domonkos Tikk used HTM and another method to undergo object categorization [42]. Another classification application about land-use [43] illustrates the recognition and classification using the photograph of land, achieving promising classification accuracy. Secondly, HTM is used in the telemedicine network [44] as an alternative method of traditional personal diagnosis. Thirdly, in the problem of accurately representing asymmetric warfare, HTM acted a strongly weapon [45]. Especially under the current world’s war situation of network-centric warfare transformed from last century’s force-to-force combat, HTM can help human understand the warfare model and analyze how human process the complex war information as well as predicting the consequences of corresponding tactics. Lastly, it comes to the evaluation function of HTM. Wim J.C. Melis, Shuhei Chizuwa and Michitaka Kameyama used HTM method on user support system, and toke the cellular phone intention estimation as an example[46], compared to the performance of Bayesian Network, it found that HTM required little effort for designing the application and could easily be optimized. Subsequently, they published another paper which also about HTM’s function on intention estimation information appliance system, the difference was that the authors supposed a possible VLSI architecture for HTM [47], although the expense and computing efficiency are waiting for solving. Other evaluations cover automated design [48],

[49] and automated risk assessment [50]. According to above, HTM appears to be a potential

intelligence computing method that has caught large numbers of investigators’ attention to research and try to apply it in aspects which can realize intelligence. It’s a hopeful method which contains promising application future.

IV. CONCLUSION

HTM is a soft computing method which models the neocortex. It is derived from biology and is suitable for tasks that are easy for people but difficult for computers such as recognizing objects, making predictions, and discovering patterns in complex data. It is a masterpiece displayed in the field of artificial intelligence and leading the computing intelligence coming to new age, also let the research of intelligent machine see the ray of hope. Now


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although the algorithm is immature and application in more intelligent manners exist restriction, persistent study is going on all the while and the achievements about development as well as application have been popping up with happy regularity. It’s believed that intelligent computing and artificial intelligence are on the way of prosperity and the principles by which HTM technology operates will lay the foundation of machine intelligence.

Up to now, the algorithm of HTM is on the way of durative optimizing, and the application field is extending from image recognition to more other aspects. However, most applications researched now aim at basic sense organs in human such as vision, audio and movement, applications about advanced logic analysis appear rarely. For example, human’s thinking process (such as recognition and reaction for outside information in daily life) may be a promising trend, as the most novel characteristic of HTM is the function of prediction. In terms of dealing information, human can predict what will happen next when he is confronted with a similar phenomenon which has seen before, thus he is able to take measures timely to react to the affair.

The feasibility of using HTM algorithm in cognition and reaction for human is represented as follow: firstly, the main research object is the same, for both are human; secondly, the target remains consistent, using HTM in information cognition and reaction for human is to reach the aim of predicting what will happen next; thirdly, also the most important one, both of the inputs have temporal identity, the messages which human received also possess the characteristic of temporal relativity. Messages which always happen together or come one after another suggesting that they have close relationship, thus when human receives one message of them, he will predict the next message he has seen before. As for inputs, they need to be taken quantitative, because messages are mostly described fuzzy, in addition, the time factor should not be ignored. So a sequence of binary vectors labeled time flag can be taken into consideration. In the end, a test for training result can be made to validate the correctness. A feed-back channel can be added to the network. In this way, correct prediction will strengthen the memory, and inaccurate prediction will become an error signal to modify the training.

ACKNOWLEDGMENT

The authors sincerely thank all participators for their hard work and valuable suggestions that have lead to the improvements of this paper. At the same time, the authors gratefully acknowledge anonymous reviewers, who help to improve the quality of this paper.

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