Learning Anticipation via Spiking Networks: Application to Navigation Control
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The document describes a spiking neural network model for navigation control in robots. The network uses spike-timing dependent plasticity (STDP) as an unsupervised learning rule to enable obstacle avoidance and target approaching behaviors. Simulation results show that with STDP learning, the number of collisions decreases for obstacle avoidance. For target approaching, trajectories improve with visual input. The network provides an efficient method for navigation tasks inspired by spatial representations in rat hippocampus neurons.
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Convolutional neural network
Convolutional Neural Network (CNN) is a type of neural network that can take in an input image, assign importance to areas in the image, and distinguish objects in the image. CNNs use convolutional layers and pooling layers, which help introduce translation invariance to allow the network to recognize patterns and objects regardless of their position in the visual field. CNNs have been very effective for tasks involving visual imagery like image classification but may be less effective for natural language processing tasks that rely more on word order and sequence. Recurrent neural networks (RNNs) that can model sequential data may perform better than CNNs for some natural language processing tasks like text classification.
Visualizing and Understanding Convolutional Networks
Large Convolutional Network models have
recently demonstrated impressive classification
performance on the ImageNet benchmark
(Krizhevsky et al., 2012). However
there is no clear understanding of why they
perform so well, or how they might be improved.
In this paper we address both issues.
We introduce a novel visualization technique
that gives insight into the function of intermediate
feature layers and the operation of
the classifier. Used in a diagnostic role, these
visualizations allow us to find model architectures
that outperform Krizhevsky et al. on
the ImageNet classification benchmark. We
also perform an ablation study to discover
the performance contribution from different
model layers. We show our ImageNet model
generalizes well to other datasets: when the
softmax classifier is retrained, it convincingly
beats the current state-of-the-art results on
Caltech-101 and Caltech-256 datasets
A survey based on eeg classification
Electroencephalography (EEG) based brain Computer interface (BCI) needs efficient algorithms to extract discriminative features from raw EEG signals. The issue of selecting optimizing spatial spectral features is key to high performance motor imagery(MI) classification, which is one of the main topics in EEG-based brain computer interfaces. Some novel methods are used first which formulates the selection of features as maximizing mutual information between class labels and features. It then uses an efficient algorithms for pattern feature extraction frame work,to select an effective feature set. The results shows the classification accuracy obtained and is compared with the other existing algorithms
脳とAIの接点から何を学びうるのか@第5回WBAシンポジウム： 銅谷賢治
MRIや２光子顕微鏡、単一細胞RNA-seqなどの実験技術の進歩により、超高次元の脳データから科学的知見や臨床マーカーを得る上で、統計的機械学習などのAI技術の活用は今や不可欠のものとなりつつある。一方で、脳科学の知見を次世代のAI開発にどう活かすことができるだろうか？本講演では、エネルギー効率、データ効率、自律性と社会性に着目し、その脳での原理の解明と次世代AIへの活用の可能性を議論する。
What's hot (20)
ADOPTING AND IMPLEMENTATION OF SELF ORGANIZING FEATURE MAP FOR IMAGE FUSION
ADOPTING AND IMPLEMENTATION OF SELF ORGANIZING FEATURE MAP FOR IMAGE FUSION
TERRIAN IDENTIFICATION USING CO-CLUSTERED MODEL OF THE SWARM INTELLEGENCE & S...
TERRIAN IDENTIFICATION USING CO-CLUSTERED MODEL OF THE SWARM INTELLEGENCE & S...
Microscopy images segmentation algorithm based on shearlet neural network
Microscopy images segmentation algorithm based on shearlet neural network
Efficient Neural Network Architecture for Image Classfication
Efficient Neural Network Architecture for Image Classfication
HUMAN ACTION RECOGNITION IN VIDEOS USING STABLE FEATURES
HUMAN ACTION RECOGNITION IN VIDEOS USING STABLE FEATURES
CONVOLUTIONAL NEURAL NETWORK BASED FEATURE EXTRACTION FOR IRIS RECOGNITION
CONVOLUTIONAL NEURAL NETWORK BASED FEATURE EXTRACTION FOR IRIS RECOGNITION
Random Neural Network (Erol) by Engr. Edgar Carrillo II
Random Neural Network (Erol) by Engr. Edgar Carrillo II
The 5th WBA Hackathon Orientation -- Cerenaut Part
The 5th WBA Hackathon Orientation -- Cerenaut Part
Visualizaing and understanding convolutional networks
Visualizaing and understanding convolutional networks
Project on collision avoidance in static and dynamic environment
Project on collision avoidance in static and dynamic environment
Visualizing and Understanding Convolutional Networks
Visualizing and Understanding Convolutional Networks
Similar to Learning Anticipation via Spiking Networks: Application to Navigation Control
Unsupervised representation learning for gaze estimation
This document summarizes a research paper on unsupervised representation learning for gaze estimation. The paper proposes an unsupervised learning framework that uses a large amount of unlabeled eye image data to learn a gaze representation. This representation is used to train a gaze redirection network and support few-shot gaze estimation with only a small number of labeled samples. The method learns the representation using a feature extractor network and differences in representations between aligned image pairs. Evaluation on three datasets shows the approach can accurately estimate gaze using as few as 10-100 labeled samples per person.
Design and Development of Intelligent Navigation Control Systems for Autonomo...
Design and Development of Intelligent Navigation Control Systems for Autonomo...IJEID :: International Journal of Excellence Innovation and Development
This document describes the design and development of an intelligent navigation control system for autonomous robots using neural networks. It proposes using a neural network approach to allow robots to learn how to navigate autonomously and avoid obstacles. The system uses ultrasonic sensors and an artificial neural network trained with backpropagation to allow the robot to detect obstacles and learn navigation over time. The neural network controller was simulated in MATLAB Simulink and implemented on an Arduino platform. Test results demonstrated the robot's ability to intelligently navigate different environments using the neural network approach.Wearable Accelerometer Optimal Positions for Human Motion Recognition(LifeTec...
Wearable Accelerometer Optimal Positions for Human Motion Recognition. The 2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech 2020), March 10-11, 2020
Deep Learning in Recommender Systems - RecSys Summer School 2017
This is the presentation accompanying my tutorial about deep learning methods in the recommender systems domain. The tutorial consists of a brief general overview of deep learning and the introduction of the four most prominent research direction of DL in recsys as of 2017. Presented during RecSys Summer School 2017 in Bolzano, Italy.
Human action recognition with kinect using a joint motion descriptor
- We proposed a novel descriptor for motion of skeleton joints.
- Proposed descriptor proved to outperform the state-of-the-art descriptors such as HON4D and the one proposed by Chen et al 2013.
- Our proposed approached proved to be effective for periodic actions (e.g., Waving, Walking, Jogging, Side-Boxing, etc).
- Grouping was effective for actions with unique joints trajectories (e.g., Tennis serving, Side kicking , etc).
- Grouping joints into eight groups is always effective with actions of MSR3D dataset.
Artificial Neural Network based Mobile Robot Navigation
This document presents a neural network based navigation system for mobile robots. It uses an artificial neural network (ANN) trained with Backpropagation Through Time (BPTT) to plan paths and navigate around obstacles. The input to the ANN is the state of the robot described using polar coordinates relative to the target position and orientation. Obstacles are also included as inputs by dividing the area in front of the robot into regions. The cost function for training is extended with a potential field to repel the robot from obstacles. Simulation results showed the robot could successfully navigate a maze and reach the target while avoiding multiple obstacles.
Pedestrian dead reckoning indoor localization based on os-elm
Smartphone-based pedestrian dead-reckoning (PDR) has become promising in indoor localization since it locates users with a smartphone only. However, existing PDR approaches are still facing the problem of accumulated localization errors due to low-cost noisy sensors and complicated human movements.ThispaperpresentsanovelPDRindoorlocalizationalgorithmcombinedwithonlinesequential extreme learning machine (OS-ELM). By analyzing the process of PDR localization, this paper first formulatestheprocessofPDRlocalizationasanapproximationfunction,andthen,asliding-window-based scheme is designed to preprocess the obtained inertial sensor data and thus to generate the feature dataset. At last, the OS-ELM-based PDR algorithm is proposed to address the localization problem of pedestrians. Due to the fact of universal approximation capability and extreme learning speed within OS-ELM, our algorithmcanadapttolocalizationenvironmentdynamicallyandreducethelocalizationerrorstoalowscale. Inaddition,bytakingthemovementhabitsofpedestrianintotheprocessofextremelearning,ouralgorithm can predict the position of pedestrian regardless of holding postures. To evaluate the performance of the proposed algorithm, this paper implements OS-ELM-based PDR on a real android-based smartphone and comparesitwiththestate-of-the-artapproaches.Extensiveexperimentresultsdemonstratetheeffectiveness of the proposed algorithm in various different postures and the practicability in indoor localization.
Robot navigation in unknown environment with obstacle recognition using laser...
Robot navigation in unknown and dynamic environments may result in aimless wandering, corner traps and repetitive path loops. To address these issues, this paper presents the solution by comparing the standard deviation of the distance ranges of the obstacles appeared in the robot navigation path. For the similar obstacles, The standard deviations of distance range vectors, obtained from the laser range finder sensor of the robot at similar pose, are very close to each other. Therefore, the measurements of odometer sensor are also combined with the standard deviation to recognize the location of the obstacles. A novel algorithm, with obstacle detection feature, is presented for robot navigation in unknown and dynamic environments. The algorithm checks the similarity of the distance range vectors of the obstacles in the path and uses this information in combination with the odometer measurements to identify the obstacles and their locations. The experimental work is carried out using Gazebo simulator.
Smart Room Gesture Control
Technical presentation of the gesture based NUI I developed for the Aigaio smart conference room in IIT Demokritos
Demo In Greek:
https://www.youtube.com/watch?v=5C_p7MHKA4g
Computational Tools for Extracting, Representing and Analyzing Facial Features
ABSTRACT: In this work, we present a computer aided system for interactive extraction of anthropometrical points (landmarks) on 3D human face meshes, and a methodology for a statistical analysis of the anthropometrical points. In the developed software, we employed real time rendering techniques and interactive picking through collisions detection and haptic feedback, to allow intuitive user interaction with the virtual model. We also exploit the geometric information of the meshes by computing and displaying the curvatures and shape index, to give to the user a better understanding of the 3D data by using color maps. The proposed method was tested on a database of 35 faces from healthy Mexican individuals, obtained with a low cost structured light stereovision system. One of the objectives of this study, was to determine the statistical variability of a set of 19 face landmarks, which define facial features of the eyes, mouth, nose, cheeks and chin. To validate the reliability of the hand-extracted landmarks, a Technical Error of Measurement (TEM) analysis was performed. After the points were extracted, a rigid registration of the landmarks, to those from a reference head model, was applied by determining an optimal rigid transformation consisting of a unit quaternion and a translation vector obtained from the cross-covariance matrix. To obtain the mean landmarks set and the modes of variation, a Principal Components Analysis (PCA) based on the covariance matrix was employed. Finally, we approximated the average facial shape of the population under study, by deforming the reference head model through cage-based registration. In such approach, the high resolution model is attached to a rough mesh or cage, which encloses the detailed model, by using Mean Value Coordinates (MVC) each vertex of the detailed mesh is represented as a linear combination of the cage mesh vertices, which allow detail preserving deformation of the high polygonal mesh by displacement of the vertices from the coarse mesh, then the cage is iteratively deformed through Laplacian deformation in order to minimize the squared distance between corresponding landmarks. Besides working with points, and linear and angular measurements, the developed software also allows interactively to select paths and contours on the mesh surface, obtaining geodesic distances and areas; and even working on images. Finally, our work can be extended for the analysis of other complex anatomical structures, and potentially it may have other applications such as facial recognition on forensics, random face generation for avateering in virtual environments, and building compact 3D face databases.
Lecture 4 neural networks
This document provides information about neural networks from Parveen Malik, an Assistant Professor at KIIT University. It defines a neural network as a massively parallel distributed processor made up of simple processing units that has a natural ability to store experiential knowledge and make it available for use, similar to the human brain. Neural networks can be used for applications like object detection, image captioning, time series modelling, and more. The document also discusses the structure and function of biological neurons and their equivalence to artificial neurons in neural networks.
Bci
This document summarizes a project on developing a brain-computer interface system to help people with disabilities control external devices. It lists the supervisors and team members working on the project. It then outlines the agenda which includes defining the problem, objectives, motivations, system architecture, implementation, and future work. It notes disability statistics in Egypt and the objective is to help people overcome disabilities. The motivations include the technology now being more successful and dealing with new techniques. The system architecture involves acquiring brain signals, preprocessing, feature extraction, classification, and decision steps. The implementation uses an EMOTIV headset and explores preprocessing, feature extraction using wavelets, Fourier transforms and PCA, and classification using neural networks. Future work involves adding more
論文読み会@AIST (Deep Virtual Stereo Odometry [ECCV2018])
論文読み会でDVSO (Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry ) [ECCV2018(oral)]を紹介した時の資料です.
Bci
controlling arm using brain signal
using emotive headseat and worked on 3 classes (open arm ,closed arm and closed hand)
khelchandra project on ai
This document presents a method for mobile robot path planning using artificial neural networks and fuzzy logic. It introduces the problem of planning a collision-free path for a robot from an initial to goal location amidst obstacles. An artificial neural network is trained to choose a path from multiple options, while a fuzzy logic system is used for obstacle avoidance when all paths are blocked. The combination of neural networks and fuzzy logic provides a computationally efficient solution that overcomes limitations of individual approaches. The results demonstrate increased performance over traditional computational geometry methods.
Henrik Christensen - Vision for Co-robot Applications
The document discusses a vision for co-robot applications where robots can work collaboratively with humans. It outlines challenges for perception tasks as robots move from controlled settings to unstructured environments. Specifically, challenges include handling objects with and without textures, dealing with background clutter, object discontinuities, and meeting real-time constraints. Approaches discussed include using 2D visual information from monocular cameras and 3D information from RGB-D cameras for object pose estimation and tracking.
Henrik Christensen - Vision for co-robot applications
The document discusses a vision for co-robot applications where robots can work collaboratively with humans. It outlines challenges for perception tasks as robots move from controlled settings to unstructured environments. Specifically, challenges include handling objects with and without textures, dealing with background clutter, object discontinuities, and meeting real-time constraints. Approaches discussed include using 2D visual information from monocular cameras and 3D information from RGB-D cameras to estimate object poses for pick-and-place tasks.
Lecture 06: Features and Uncertainty
1. The document discusses various topics related to robotics including vision, midterm exams, locomotion, control, mapping, and homework.
2. Key concepts that are reviewed include line detection, calculating uncertainty from sensor measurements, propagating uncertainty in features, and different algorithms for mapping like watershed segmentation.
3. Exercises are assigned related to writing controllers for different robots, dealing with obstacles, and improving control parameters. Students are divided into groups to work on a final project to implement a controller.
Communityday2013
This document discusses different approaches to gesture detection using the Kinect sensor. It describes heuristic-based gesture detection, which uses experience-based techniques to reconstruct missing information and compute gesture outcomes. This approach is easy to implement but does not scale well for complex gestures. The document also describes exemplar matching-based gesture detection, which defines gestures as pre-recorded animations and uses techniques like dynamic time warping to compare input to exemplars. This approach handles complex gestures better but requires more resources. The document also notes challenges like user posture and provides recommendations on the appropriate approach based on the complexity of the gestures.
Lucio marcenaro tue summer_school
Here are the key points about discounting:
- Discounting refers to exponentially decreasing the value of rewards received in the future, compared to rewards in the present. This is done with a discount factor γ between 0 and 1.
- Discounting encourages agents to prefer sooner rewards over later rewards. It helps algorithms converge by ensuring the value of future rewards is finite.
- With discounting, the utility of a sequence of rewards is calculated as:
U = R1 + γR2 + γ^2R3 + ...
- Discounting leads to stationary preferences over reward sequences, meaning the preferences don't change over time. The two ways to define utilities under stationary preferences are additive utility and discounted utility.
Similar to Learning Anticipation via Spiking Networks: Application to Navigation Control (20)
Unsupervised representation learning for gaze estimation
Unsupervised representation learning for gaze estimation
Design and Development of Intelligent Navigation Control Systems for Autonomo...
Design and Development of Intelligent Navigation Control Systems for Autonomo...
Wearable Accelerometer Optimal Positions for Human Motion Recognition(LifeTec...
Wearable Accelerometer Optimal Positions for Human Motion Recognition(LifeTec...
Deep Learning in Recommender Systems - RecSys Summer School 2017
Deep Learning in Recommender Systems - RecSys Summer School 2017
Human action recognition with kinect using a joint motion descriptor
Human action recognition with kinect using a joint motion descriptor
Artificial Neural Network based Mobile Robot Navigation
Artificial Neural Network based Mobile Robot Navigation
Pedestrian dead reckoning indoor localization based on os-elm
Pedestrian dead reckoning indoor localization based on os-elm
Robot navigation in unknown environment with obstacle recognition using laser...
Robot navigation in unknown environment with obstacle recognition using laser...
Computational Tools for Extracting, Representing and Analyzing Facial Features
Computational Tools for Extracting, Representing and Analyzing Facial Features
論文読み会@AIST (Deep Virtual Stereo Odometry [ECCV2018])
論文読み会@AIST (Deep Virtual Stereo Odometry [ECCV2018])
Henrik Christensen - Vision for Co-robot Applications
Henrik Christensen - Vision for Co-robot Applications
Henrik Christensen - Vision for co-robot applications
Henrik Christensen - Vision for co-robot applications
More from Seonghyun Kim
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,
나아가 뇌의 정보처리 메커니즘을 밝히고, 다양한 뇌 질환 치료제의 타겟을 연구하는데 아주 중요한 역할을 할 수 있습니다.
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.
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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
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Korean-optimized Word Representations for Out of Vocabulary Problems caused b...
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In this presentation include the prototype drug SAR on thus or with their examples .
Syllabus of Second Year B. Pharmacy
2019 PATTERN.
The debris of the ‘last major merger’ is dynamically young
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
20240520 Planning a Circuit Simulator in JavaScript.pptx
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(Work in progress.)
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Deep Software Variability and Frictionless ReproducibilityUniversity of Rennes, INSA Rennes, Inria/IRISA, CNRS
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
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Exposé invité Journées Nationales du GDR GPL 2024
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Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
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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.
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The 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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Learning Anticipation via Spiking Networks: Application to Navigation Control
- 1. Learning Anticipation via Spiking Networks: Application to Navigation Control Paolo Arena et al. 2009 IEEE Transactions on Neural Network
- 2. Navigation Techniques in Robotics
- 3. Spatial Information Processing ▪ Rat has the navigation cells that represent the place[1], grid[2], boundary[3] and direction[4] information in the environment [1] O’keefe and Dostrovsky, 1971, Brain Res [3] Lever et al., 2009, J Neurosci. [5] Moser et al., 2008, Annu Rev Neurosci [2] Fyhn et al., 2004, Science [4] Taube et al., 1990, J Neurosci. [6] Zhang et al., 2013, Philos Trans R Soc Lond B Biol Sci. Place cell [5] Head direction ( °) Firingrate(Hz) Preferred direction Head direction cell [4] Boundary vector cell [7] Grid cell [5] Rat trajectory Cell spikes
- 4. Neural Network Architecture ▪ Artificial neural network ▫ Output is calculated by summation of inputs with weight vectors ▫ The weight vector is modulated to get the right output ▪ Spiking neural network ▫ Output is the result of neural activity by presynaptic inputs ▫ The synaptic weight is modulated by biological learning rule
- 5. Spike-timing Dependent Plasticity (STDP) ▪ Excitatory postsynaptic current (EPSC) depends on the relative timing of pre- and postsynaptic action potentials ▪ Unsupervised learning rule [7] Bi and Poo, 1998, J Neurosci. Δt (tpost – tpre) ChangeinEPSCamplitude(%) Pre Post [7]
- 6. Learning Factors ▪ Classical conditioning provided the source of inspiration for the implementation of navigation task algorithm Unconditioned stimuli Unconditioned response Conditioned stimuli Conditioned response
- 7. Method ▪ The Spiking Network Model ▫ Sensory neurons are neurons connected to sensors ▫ Motor neurons drive robot motors ▫ Interneurons ▫ Each single neurons are modeled by Izhikevich model v: membrane potential u: recovery variable I: input current [8] Izhikevich, IEEE, 2004 20 ms a = 0.02 b = -0.1 c = -55 d = 6 i j [8]
- 8. Method ▪ STDP modification function Δt (tpre – tpost) ഥ𝒈 𝒎𝒂𝒙 𝜏+ = 20 𝜏− = 10 𝐴+ = 0.02 𝐴− = −0.02 [9] Song et al., 2000, Nat Neurosci. [9]
- 9. Method ▪ Robot simulation ▫ Dual-drive wheeled virtual robot (right motor: RM, left motor: LM) ▫ Two collision sensor (US) ▫ Two rand finder sensor ([0°, -45°], [0°, 45°], CS) ▫ Two proximity sensor (for target) Left range finder sensor Right range finder sensor Range of proximity sensor Camera view (black line)
- 10. Method ▪ Robot experiment ▫ Real roving robot ▫ Lynxmotion 4WD2 Robot ▫ Four infrared distance sensors (14 to 80 cm) ▫ Collision (threshold at 23 cm) ▫ RF04 USB radio telemetry module
- 11. Method ▪ Network for obstacle avoidance ▫ The robot moves (0.3 r.u. for each simulation step, time window: 300 ms) in an environment filled with randomly placed obstacles Collision sensor
- 12. Method Proximity target sensor ▪ The network for target approaching ▫ The barycenter of red object is provided to the network of nine neurons as a visual input
- 13. Method ▪ The network for navigation with visual cue
- 14. Result – Obstacle Avoidance ▪ Obstacle avoidance Robot is driven by UR Robot is driven by CR Robot is driven by CR Obstacleavoidancenumber
- 15. Result - Obstacle Avoidance ▪ The number of collision and wrong decision is decreased by STDP learning rule NumberofcollisionDistancebetweenrobotand closestobstacles
- 16. Result – Synaptic Weight Change ▪ Obstacle avoidance Potentiation Potentiation Depression Depression
- 17. Result – Obstacle Avoidance ▪ Obstacle avoidance ▫ Trajectories obtained in the case in which the robot faces one obstacle Before learning End of learning phase
- 18. Result – Synaptic Weight Change ▪ Obstacle avoidance ▫ Robot experiments
- 19. Result – Target Approaching ▪ Target approaching Without learning After learning (without visual input) (with visual input) Long run simulation Range of proximity sensor
- 20. Result – Target Approaching (without visual input) (with visual input) Before learning End of learning phase Two targets
- 21. Result - Navigation with Visual Cue Cases of training environments Markers Case (a) Solving maze based on case (a)
- 22. Conclusion ▪ Biological spiking neural network shows the efficiency for the dynamic systems instead of a static relationship between input and output ▪ STDP unsupervised learning rule is one of a simple way for navigation algorithm ▪ The spiking cells are suitable for investigate the temporal code of neural activity
- 23. Discussion ▪ The design of neural network is not considered for neuromorphic ▪ These networks cannot recall the specific spatial memory (place)