The hippocampo-cortical loop: Spatio-temporal learning and goal-oriented planning in navigation
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The hippocampo-cortical loop: Spatio-temporal learning and goal-oriented planning in navigation paper review
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19 3 sep17 21may 6657 t269 revised (edit ndit)
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problem using different techniques. We propose a robust approach for human action recognition. This is
achieved by extracting stable spatio-temporal features in terms of pairwise local binary pattern (P-LBP)
and scale invariant feature transform (SIFT). These features are used to train an MLP neural network
during the training stage, and the action classes are inferred from the test videos during the testing stage.
The proposed features well match the motion of individuals and their consistency, and accuracy is higher
using a challenging dataset. The experimental evaluation is conducted on a benchmark dataset commonly
used for human action recognition. In addition, we show that our approach outperforms individual features
i.e. considering only spatial and only temporal feature.
A Survey about Object Retrieval
This document discusses unsupervised and supervised approaches to object retrieval.
It begins by covering unsupervised approaches, describing common local and global features used for object retrieval like SIFT, HOG, and deep features. It also discusses feature aggregation methods like bag-of-features and Fisher vectors.
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Marcus gave a talk called "Recognizing Locations on Objects" during the HTM Meetup on 11/03/2017.
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- Hawkins proposes that grid cells exist in the neocortex and represent the location of sensory input relative to objects. Each cortical column learns a complete model of objects, including their location spaces.
- Objects can be composed of other objects via displacement cells, allowing efficient learning of new combinations without relearning parts. Behaviors can also be represented as sequences of displacements.
- This framework provides insights into neuroscience, concepts, limits of intelligence, and has implications for building true artificial intelligence based on distributed object-centric representations.
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segmentation plays a crucial role in many medical imaging applications. There are many algorithms and
techniques have been developed to solve image segmentation problems. Spectral pattern is not sufficient in
high resolution image for image segmentation due to variability of spectral and structural information.
Thus the spatial pattern or texture techniques are used. Thus the concept of Holder Exponent for
segmentation of high resolution medical image is an efficient image segmentation technique. The proposed
method is implemented in Matlab and verified using various kinds of high resolution medical images. The
experimental results shows that the proposed image segmentation system is efficient than the existing
segmentation systems.
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This project proposes methods for classifying scenes in remote sensing images. It compares the accuracy of traditional bag-of-visual-words (BoVW) models using handcrafted features to a bag-of-convolutional features (BoCF) model using deep learning. It also applies a grey wolf optimizer (GWO) algorithm for image segmentation. Results show BoCF doubled the accuracy of BoVW, and combining BoVW with GWO improved accuracy over BoVW alone. The project concludes more work is needed to better combine remote sensing data and deep learning for classification.
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Particle Swarm Optimizer (PSO) is such a complex stochastic process so that analysis on the stochastic
behavior of the PSO is not easy. The choosing of parameters plays an important role since it is critical in
the performance of PSO. As far as our investigation is concerned, most of the relevant researches are
based on computer simulations and few of them are based on theoretical approach. In this paper,
theoretical approach is used to investigate the behavior of PSO. Firstly, a state of PSO is defined in this
paper, which contains all the information needed for the future evolution. Then the memory-less property of
the state defined in this paper is investigated and proved. Secondly, by using the concept of the state and
suitably dividing the whole process of PSO into countable number of stages (levels), a stationary Markov
chain is established. Finally, according to the property of a stationary Markov chain, an adaptive method
for parameter selection is proposed.
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파이콘 한국 2020) 파이썬으로 구현하는 신경세포 기반의 인공 뇌 시뮬레이터
* 파이콘 한국 2020의 발표자료입니다.
현대 인공 신경망의 뿌리가 되었던 뇌 과학!
이 발표에서는 인공 신경망에 대한 뇌 과학적 접근과,
뇌 세포의 발화를 모사하는 파이썬 기반의 뉴로모픽 신경망 모델에 대한 사례를 공유할 예정입니다.
뉴로모픽 신경망은 단순히 기존의 딥러닝에서 셀 구조만을 변경한 것이 아닙니다.
실제로 실험을 수행하기 어려운 생물학적 한계점을 뇌 시뮬레이션을 통해서 극복할 수 있으며,
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이번 발표를 통해, 기계학습을 연구하고 있는 많은 연구자 분들에게 새로운 아이디어에 대한 영감이 될 수 있기를 희망합니다.
Backpropagation and the brain review
This document discusses backpropagation and how it relates to learning in the brain. It introduces some key concepts:
1) Synaptic plasticity allows connections between neurons to be modified through learning, but it does not explain how these modifications coordinate across a neural network.
2) Backpropagation provides a method for neural networks to learn through calculating error signals that are sent backward through the network to update synaptic weights. However, backpropagation is not biologically plausible.
3) A new method called neural gradient representation uses activity differences between neural patterns to update synapses in a way that may be more consistent with biological circuits compared to backpropagation. This provides a potential solution for how the brain could learn using feedback without literally implementing backpropagation.
Theories of error back propagation in the brain review
The document discusses several theories of how error backpropagation may occur in the brain. It begins by introducing artificial neural networks and backpropagation. It then discusses questions around unrealistic models of neurons in artificial networks compared to real neurons.
The rest of the document summarizes different proposed models of biological backpropagation:
- Temporal-error models propose that errors are represented locally over time through anti-Hebbian and Hebbian plasticity rules rather than being explicitly computed.
- Predictive coding models represent errors explicitly using error nodes that receive inhibition from value nodes and compute differences.
- Dendritic error models propose that pyramidal neurons compute errors in their apical dendrites, relating these models to predictive
Enriching Word Vectors with Subword Information
1) The document proposes a new word vector model that represents words as the sum of their character n-gram vectors to better capture morphological information.
2) It tests this model on nine languages and shows it outperforms previous models on word similarity and analogy tasks.
3) Representing words as combinations of character n-grams allows the model to learn representations for out-of-vocabulary words.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
The document discusses BERT, which stands for Bidirectional Encoder Representations from Transformers. BERT uses bidirectional Transformers to pre-train deep contextual representations of language. It was trained on two unsupervised prediction tasks using large text corpora. Experimental results showed that BERT achieved state-of-the-art results on eleven natural language understanding tasks, including question answering and textual inference. The document outlines the model architecture of BERT and the pre-training and fine-tuning methods used.
Korean-optimized Word Representations for Out of Vocabulary Problems caused b...
Korean-optimized Word Representations for Out of Vocabulary Problems caused by Misspelling using Sub-character Information paper review
챗봇의 역사
The document discusses the history and development of artificial intelligence and chatbots from early examples like Pygmalion and Galatea in ancient times to modern chatbot builders. It describes important early AI programs and chatbots like Colossus, the Turing test, ELIZA, ALICE, and Mitsuku. It also discusses the Korean chatbot Shimshim and provides examples of conversations with ELIZA, ALICE, and Mitsuku. Finally, it lists many popular modern chatbot builders used to create conversational agents.
More from Seonghyun Kim (13)
한국어 개체명 인식 과제에서의 의미 모호성 연구
한국어 개체명 인식 과제에서의 의미 모호성 연구
Theories of error back propagation in the brain review
Theories of error back propagation in the brain review
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Korean-optimized Word Representations for Out of Vocabulary Problems caused b...
Korean-optimized Word Representations for Out of Vocabulary Problems caused b...
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Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/Unveiling the Energy Potential of Marshmallow Deposits.pdf
Unveiling the Energy Potential of Marshmallow Deposits: A Revolutionary
Breakthrough in Sustainable Energy Science
Bob Reedy - Nitrate in Texas Groundwater.pdf
Presented at June 6-7 Texas Alliance of Groundwater Districts Business Meeting
Orion Air Quality Monitoring Systems - CWS
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
原版制作(carleton毕业证书)卡尔顿大学毕业证硕士文凭原版一模一样
原版纸张【微信：741003700 】【(carleton毕业证书)卡尔顿大学毕业证】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
3D Hybrid PIC simulation of the plasma expansion (ISSS-14)
3D Particle-In-Cell (PIC) algorithm,
Plasma expansion in the dipole magnetic field.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
Phenomics assisted breeding in crop improvement
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
Leaf Initiation, Growth and Differentiation.pdf
Leaf initiation, growth and differentiation, genetic control of leaf development.
BREEDING METHODS FOR DISEASE RESISTANCE.pptx
Plant breeding for disease resistance is a strategy to reduce crop losses caused by disease. Plants have an innate immune system that allows them to recognize pathogens and provide resistance. However, breeding for long-lasting resistance often involves combining multiple resistance genes
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...
In this book, we use conservation of energy techniques on a fluid element to derive the Modified Bernoulli equation of flow with viscous or friction effects. We derive the general equation of flow/ velocity and then from this we derive the Pouiselle flow equation, the transition flow equation and the turbulent flow equation. In the situations where there are no viscous effects , the equation reduces to the Bernoulli equation. From experimental results, we are able to include other terms in the Bernoulli equation. We also look at cases where pressure gradients exist. We use the Modified Bernoulli equation to derive equations of flow rate for pipes of different cross sectional areas connected together. We also extend our techniques of energy conservation to a sphere falling in a viscous medium under the effect of gravity. We demonstrate Stokes equation of terminal velocity and turbulent flow equation. We look at a way of calculating the time taken for a body to fall in a viscous medium. We also look at the general equation of terminal velocity.
Shallowest Oil Discovery of Turkiye.pptx
The Petroleum System of the Çukurova Field - the Shallowest Oil Discovery of Türkiye, Adana
Nucleic Acid-its structural and functional complexity.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
THEMATIC APPERCEPTION TEST(TAT) cognitive abilities, creativity, and critic...
THEMATIC APPERCEPTION TEST(TAT) cognitive abilities, creativity, and critic...Abdul Wali Khan University Mardan,kP,Pakistan
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skillsThe use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptx
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
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Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...
Unveiling the Energy Potential of Marshmallow Deposits.pdf
Unveiling the Energy Potential of Marshmallow Deposits.pdf
3D Hybrid PIC simulation of the plasma expansion (ISSS-14)
3D Hybrid PIC simulation of the plasma expansion (ISSS-14)
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...
Nucleic Acid-its structural and functional complexity.
Nucleic Acid-its structural and functional complexity.
THEMATIC APPERCEPTION TEST(TAT) cognitive abilities, creativity, and critic...
THEMATIC APPERCEPTION TEST(TAT) cognitive abilities, creativity, and critic...
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptx
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptx
The hippocampo-cortical loop: Spatio-temporal learning and goal-oriented planning in navigation
- 1. The hippocampo-cortical loop: Spatio-temporal learning and goal-oriented planning in navigation Neural Networks Hirel et al., 2013
- 3. Spatial Navigation ▪ Prefrontal cortex to represent task space (i.e. goals and values of cues and action options) Verschure et al., 2014
- 4. Place Cell ▪ Place cell show location-specific firing Hippocampal CA1 Medial entorhin al cortex (MEC) O’keefe and Dostrovsky, 1971
- 5. Place Cell ▪ Place cell has ‘forward sweeps’ (journey-dependent activity) that is related to the specific route for goal-directed navigation Grieves et al., 2016, eLIFE
- 6. Goal-directed Navigation ▪ ‘Tree search’ model for goal-directed navigation model ▪ A rat faces a maze, in which different turns lead to states and rewards Daw, 2012, IEEE
- 7. Place Cell and Goal ▪ Place cell also related to the reward location Hok et al., 2007
- 8. Goal-related Cell in mPFC ▪ Cells with spatial correlates have been found in the mPFC of the rat performing a goal-oriented task Hok et al., 2005
- 9. Research Questaion & Aims ▪ How the neural network performs spatial information processing and computation? ▫ Place cell could be crucial for goal-directed navigation ▫ mPFC cells process the spatial information of the goal location ▪ To learn: ▫ Current location and motor controls ▫ Before place, current place and predicted place
- 10. Method ▪ Experiment environment ▫ Prometheus simulation ▫ Visual sensor (20 landmarks) and sound sensor (2 s) ▪ Neuron model ▫ Artificial neural network model ▫ Gaussian-like activity curve model as a node in the neural network ▫ Hebbian learning Goal
- 11. Method ▪ Neural network model RE: Nucleus reuniens S: Subiculum gc: granule cell mc: Mossy cells A: Amygdala PER: perihinal cortex POR: Postrhinal cortex S: Basal ganglia
- 12. Method ▪ Neural network model To idendify current locationTo send sensor data Actions To match current location and actions Nucleus accumbens
- 13. Results ▪ DG neurons provides last state ▪ CA3 neurons predicted current location (EC neurons) by DG activities
- 14. Results ▪ Neural networks could successfully predicted sound responses as a secondary place field
- 15. Results
- 16. Conclusion ▪ Place cells successfully predicted the goal location using neural network model with Hebbian learning paradigm ▪ Place cell represents secondary place field
- 17. Discussion ▪ A robot that was controlled by the neural network model successfully encoded the path to the goal location ▪ However, the path was not optimal Hier et al., 2011
- 18. Discussion ▪ What is the role of CA1 place cells in this neural networks? ▫ CA1 just received the synaptic input from CA3 and EC neuron ▪ Cognitive map algorithm in this neural network model will be adapted to neuromorphic neural network model