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Formation of cognitive maps in large-scale environments by sensorimotor integration

COGNITIVE NEURODYNAMICS 2025年第1期19卷 1-14页

作者： Zhao, Dongye Si, Bailu China Elect Technol Grp Corp Informat Sci Acad Beijing 100086 Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China Chinese Inst Brain Res Beijing 102206 Peoples R China

Hippocampus in the mammalian brain supports navigation by building a cognitive map of the environment. However, only a few studies have investigated cognitive maps in large-scale arenas. To reveal the computational mechanisms underlying the formation of cognitive maps in large-scale environments, we propose a neural network model of the entorhinal-hippocampal neural circuit that integrates both spatial and non-spatial information. Spatial information is relayed from the grid units in medial entorhinal cortex (MEC) by integrating multimodal sensory-motor signals. Non-spatial, such as object, information is imparted from the visual units in lateral entorhinal cortex (LEC) by encoding visual scenes through a deep neural network. The synaptic weights from the grid units and the visual units to the place units in the hippocampus are learned by a competitive learning rule. We simulated the model in a large box maze. The place units in the model form irregularly-spaced multiple fields across the environment. When the strength of visual inputs is dominant, the responses of place units become conjunctive and egocentric. These results point to the key role of the hippocampus in balancing spatial and non-spatial information relayed via LEC and MEC.

关键词： Large-scale environment Hippocampal representations Self-organization Competitive learning sparse coding Cognitive map

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Experimental Demonstration of the Fixed-Point sparse coding Performance

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CYBERNETICS AND INFORMATION TECHNOLOGIES 2014年第5期14卷 40-50页

作者： Jiang, Jingfei Hu, Rongdong Zhang, Fei Dou, Yong Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Hunan Peoples R China

The sparse coding (SC) model has been proved to be among the best neural networks which are mainly used in unsupervised feature learning for many applications. Running a sparse coding algorithm is a time-consuming task due to its large scale and processing characteristics, which naturally leads to investigating FPGA acceleration. Fixed-point arithmetic can be used when implementing SC in FPGAs to reduce the execution time, but the implications for accuracy are not clear. Previous studies have focused only on accelerators using some fixed bitwidths on other neural networks models. Our work gives a comprehensive evaluation to demonstrate the bit-width effect on SCs, achieving the best performance and area efficiency. The method of data format conversion and the matrix blocking are the main factors considered according to the situation of hardware implementation. The simulation method of the simple truncation, the representation of the domain constraint and the matrix blocking with different parallelism were evaluated in this paper. The results have shown that the fixedpoint bit-width did have effect on the performance of SC. We must limit the representation domain of the data carefully and select an available bit-width according to the computation parallelism. The result has also shown that using a fixed-point arithmetic can guarantee the precision of the SC algorithm and get acceptable convergence speed.

关键词： sparse coding fixed-point data bit-width FPGA

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Improved sparse coding Under the Influence of Perceptual Attention

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NEURAL COMPUTATION 2014年第2期26卷 377-420页

作者： Amiri, Ashkan Haykin, Simon McMaster Univ Cognit Syst Lab Hamilton ON L8S 4K1 Canada

sparse coding has established itself as a useful tool for the representation of natural data in the neuroscience as well as signal-processing literature. The aim of this letter, inspired by the human brain, is to improve on the performance of the sparse coding algorithm by trying to bridge the gap between neuroscience and engineering. To this end, we build on the localized perception-action cycle in cognitive neuroscience by categorizing it under the umbrella of perceptual attention, which lends itself to increase gradually the contrast between relevant information and irrelevant information. Stated in another way, irrelevant information is filtered away, while relevant information about the environment is enhanced from one cycle to the next. We may thus think in terms of the information filter, which, in a Bayesian context, was introduced in the literature by Fraser (1967). In a Bayesian context, the information filter provides a method for algorithmic implementation of perceptual attention. The information filter may therefore be viewed as the basis for improving the algorithmic performance of sparse coding. To support this performance improvement, the letter presents two computer experiments. The first experiment uses simulated (real-valued) data that are generated to purposely make the problem challenging. The second uses real-life radar data that are complex valued, hence the proposal to introduce Wirtinger calculus into derivation of the new algorithm.

关键词： sparse Influence Attention Neurophysiology sparse coding Classification (Cognitive Process) Web filters Neurosciences letter

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Robust Pedestrian Tracking and Recognition from FLIR Video: A Unified Approach via sparse coding

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SENSORS 2014年第6期14卷 11245-11259页

作者： Li, Xin Guo, Rui Chen, Chao CSEE Lane Dept Morgantown WV 26506 USA Univ Tennessee Dept EECS Knoxville TN 37996 USA Univ Missouri Dept Elect & Comp Engn Columbia MO 65211 USA

sparse coding is an emerging method that has been successfully applied to both robust object tracking and recognition in the vision literature. In this paper, we propose to explore a sparse coding-based approach toward joint object tracking-and-recognition and explore its potential in the analysis of forward-looking infrared (FLIR) video to support nighttime machine vision systems. A key technical contribution of this work is to unify existing sparse coding-based approaches toward tracking and recognition under the same framework, so that they can benefit from each other in a closed-loop. On the one hand, tracking the same object through temporal frames allows us to achieve improved recognition performance through dynamical updating of template/dictionary and combining multiple recognition results;on the other hand, the recognition of individual objects facilitates the tracking of multiple objects (i.e., walking pedestrians), especially in the presence of occlusion within a crowded environment. We report experimental results on both the CASIAPedestrian Database and our own collected FLIR video database to demonstrate the effectiveness of the proposed joint tracking-and-recognition approach.

关键词： robust tracking pedestrian recognition sparse coding template updating FLIR video

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sparse representation for heterogeneous information networks

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NEUROCOMPUTING 2023年 525卷 111-122页

作者： Zhai, Xuemeng Tang, Zhiwei Liu, Zhiwei Zhou, Wanlei Hu, Hangyu Fei, Gaolei Hu, Guangmin Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China City Univ Macau Inst Data Sci Macau Peoples R China

A complex network is a fundamental tool to describe real-world complex systems, with most real-world systems containing multiple object types and relationships that can be described as heterogeneous infor-mation networks. However, with the increasing network complexity, understanding the complex pat-terns and finding the meta paths or meta-structures of the heterogeneous information networks has become challenging. This paper proposes a sparse representation for heterogeneous information net-works and extracts the heterogeneous information atoms that describe the basic connection pattern of the original heterogeneous information network. The heterogeneous information atoms help extract the main meta-paths or meta-structures and understand the complex patterns of the original heteroge-neous information network. Furthermore, the heterogeneous information networks can be decomposed, dimension-reduced, and reconstructed through the heterogeneous information atoms. Extensive exper-imental results demonstrate that heterogeneous information atoms and sparse coding represent the basic connection pattern of real-world heterogeneous information networks. Indeed, the developed method can reconstruct a network with a recovery exceeding 90%. (c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Complex network Heterogeneous information network sparse representation Heterogeneous information atoms Dictionary learning sparse coding

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Unrolled Compressed Blind-Deconvolution

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2023年 71卷 2118-2129页

作者： Tolooshams, Bahareh Mulleti, Satish Ba, Demba Eldar, Yonina C. C. Harvard Univ Sch Engn & Appl Sci Cambridge MA 02138 USA Indian Inst Technol IIT Bombay Dept Elect Engn Mumbai 400076 Maharashtra India Weizmann Inst Sci Fac Math & Comp Sci IL-7632706 Rehovot Israel

The problem of sparse multichannel blind deconvolution (S-MBD) arises frequently in many engineering applications such as radar/sonar/ultrasound imaging. To reduce its computational and implementation cost, we propose a compression method that enables blind recovery from much fewer measurements with respect to the full received signal in time. The proposed compression measures the signal through a filter followed by subsampling, allowing for a significant reduction in implementation cost. We derive theoretical guarantees for the identifiability and recovery of a sparse filter from compressed measurements. Our results allow for the design of a wide class of compression filters. We, then, propose a data-driven unrolled learning framework to learn the compression filter and solve the S-MBD problem. The encoder is a recurrent inference network that maps compressed measurements into an estimate of sparse filters. We demonstrate that our unrolled learning method is more robust to choices of source shapes and has better recovery performance compared to optimization-based methods. Finally, in data-limited applications (fewshot learning), we highlight the superior generalization capability of unrolled learning compared to conventional deep learning.

关键词： Index Terms-Compression deep learning dictionary learning multichannel blind-deconvolution sparse coding unrolling

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Dynamic Sensor Placement Based on Sampling Theory for Graph Signals

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF SIGNAL PROCESSING 2024年 5卷 1042-1051页

作者： Nomura, Saki Hara, Junya Higashi, Hiroshi Tanaka, Yuichi Tokyo Univ Agr & Technol Dept Elect Engn & Comp Sci Tokyo 1848588 Japan Osaka Univ Grad Sch Engn Osaka 5650871 Japan

In this paper, we consider a sensor placement problem where sensors can move within a network over time. Sensor placement problem aims to select K sensor positions from N candidates where K < N. Most existing methods assume that sensor positions are static, i.e., they do not move, however, many mobile sensors like drones, robots, and vehicles can change their positions over time. Moreover, underlying measurement conditions could also be changed, which are difficult to cover with statically placed sensors. We tackle the problem by allowing the sensors to change their positions in their neighbors on the network. We dynamically determine the sensor positions based on graph signal sampling theory such that the non-observed signals on the network can be best recovered from the observations. For signal recovery, the dictionary is learned from a pool of observed signals. It is also used for the sensor position selection. In experiments, we validate the effectiveness of the proposed method via the mean squared error of the reconstructed signals. The proposed dynamic sensor placement outperforms the existing static ones for both synthetic and real data.

关键词： Dynamic sensor placement on graphs dictionary sparse coding graph sampling theory.

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Competitive learning to generate sparse representations for associative memory

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NEURAL NETWORKS 2023年 168卷 32-43页

作者： Sacouto, Luis Wichert, Andreas Univ Lisbon INESC ID Ave Rovisco Pais 1 P-1049001 Lisbon Portugal Univ Lisbon Inst Super Tecn Ave Rovisco Pais 1 P-1049001 Lisbon Portugal

One of the most well established brain principles, Hebbian learning, has led to the theoretical concept of neural assemblies. Based on it, many interesting brain theories have spawned. Palm's work implements this concept through multiple binary Willshaw associative memories, in a model that not only has a wide cognitive explanatory power but also makes neuroscientific predictions. Yet, Willshaw's associative memory can only achieve top capacity when the stored vectors are extremely sparse (number of active bits can grow logarithmically with the vector's length). This strict requirement makes it difficult to apply any model that uses this associative memory, like Palm's, to real data. Hence the fact that most works apply the memory to optimal randomly generated codes that do not represent any information. This issue creates the need for encoders that can take real data, and produce sparse representations - a problem which is also raised following Barlow's efficient coding principle. In this work, we propose a biologically-constrained network that encodes images into codes that are suitable for Willshaw's associative memory. The network is organized into groups of neurons that specialize on local receptive fields, and learn through a competitive scheme. After conducting autoand hetero-association experiments on two visual data sets, we can conclude that our network not only beats sparse coding baselines, but also that it comes close to the performance achieved using optimal random codes. (c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC license (http://***/licenses/by-nc/4.0/).

关键词： sparse coding Associative memory Autoencoders Brain-inspired models

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Cross-Domain Joint Dictionary Learning for ECG Inference From PPG

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IEEE INTERNET OF THINGS JOURNAL 2023年第9期10卷 8140-8154页

作者： Tian, Xin Zhu, Qiang Li, Yuenan Wu, Min Univ Maryland Dept Elect & Comp Engn College Pk MD 20742 USA Meta Inc Menlo Pk CA 94025 USA Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

The inverse problem of inferring clinical gold-standard electrocardiogram (ECG) from photoplethysmogram (PPG) that can be measured by affordable wearable Internet of Healthcare Things (IoHT) devices is a research direction receiving growing attention. It combines the easy measurability of PPG and the rich clinical knowledge of ECG for long-term continuous cardiac monitoring. The prior art for reconstruction using a universal basis, such as discrete cosine transform (DCT), has limited fidelity for uncommon ECG shapes due to the lack of representative power. To better utilize the data and improve data representation, we design two dictionary learning frameworks, the cross-domain joint dictionary learning (XDJDL), and the label-consistent XDJDL (LC-XDJDL), to further improve the ECG inference quality and enrich the PPG-based diagnosis knowledge. Building on the K-SVD technique, the proposed joint dictionary learning frameworks extend the expressive power by optimizing simultaneously a pair of signal dictionaries for PPG and ECG with the transforms to relate their sparse codes and disease information. The proposed models are evaluated with a variety of PPG and ECG morphologies from two benchmark datasets that cover various age groups and disease types. The results show the proposed frameworks achieve better inference performance than previous methods with average Pearson coefficients being 0.88 using XDJDL and 0.92 using LC-XDJDL, suggesting an encouraging potential for ECG screening using PPG based on the proactively learned PPG-ECG relationship. By enabling the dynamic monitoring and analysis of the health status of an individual, the proposed frameworks contribute to the emerging digital twins paradigm for personalized healthcare.

关键词： Electrocardiography Machine learning Monitoring Dictionaries Sensors Transforms Diseases Digital twins electrocardiogram (ECG) Internet of Healthcare Things (IoHT) joint dictionary learning photoplethysmogram (PPG) sparse coding

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Robust and Energy Efficient sparse-Coded OFDM-DCSK System via Matrix Recovery

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IEEE TRANSACTIONS ON COMMUNICATIONS 2023年第8期71卷 4839-4850页

作者： Liu, Zhaofeng So, Hing Cheung Li, Xiao Peng Zhang, Lin Wang, Zhi-Yong City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Peoples R China Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou 510006 Peoples R China

In this paper, we devise a sparse-coded orthogonal frequency division multiplexing (OFDM) differential chaos shift keying (DCSK) communication system based on low-rank matrix recovery which can handle Gaussian background noise and outlier-contaminated symbols simultaneously. As the noise-free OFDM-DCSK symbol matrix has rank 1, we exploit the vector outer product for its modeling, while sparse coding is also applied to reduce the transmission energy. To demodulate information bits from the sparse-coded signal, we formulate an objective function which consists of a sum of Frobenius norm for rank-1 matrix recovery and l(0)-norm for identifying the possibly outlier-contaminated symbols, with a self-adaptive weight parameter. The resultant optimization problem is solved iteratively via block coordinate descent, and the Laplacian kernel with the Silverman's rule is adopted for outlier detection. Theoretical analysis including convergence of the objective function, bit error rate (BER), energy efficiency and computational complexity, are provided. Simulation results show that the proposed system has comparable mean square error and BER performance with the l(p)-norm minimization based matrix recovery approach at p = 2 in additive white Gaussian noise, and is superior to that of p = 1 in Middleton class A noise, even when sparse coding is applied. Moreover, compared with other binary DCSK systems, our system achieves higher energy efficiency thanks to the sparse coding.

关键词： Differential chaos shift keying sparse coding low-rank matrix recovery bit error rate impulsive noise energy efficiency

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