scholarly journals Developing topographic visual domain organization in a recurrent neural network with biological constraints

2021 ◽  
Vol 21 (9) ◽  
pp. 2767
Author(s):  
Nicholas M Blauch ◽  
Marlene Behrmann ◽  
David C Plaut
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Domain Organization ◽  
Biological Constraints ◽  
Visual Domain

scholarly journals Spiking recurrent neural networks represent task-relevant neural sequences in rule-dependent computation

2021 ◽  
Author(s):  
Xiaohe Xue ◽  
Michael M. Halassa ◽  
Zhe S. Chen
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Working Memory ◽  
Recurrent Neural Network ◽  
Cortical Neurons ◽  
Gradient Methods ◽  
Cognitive Tasks ◽  
Brain Functions ◽  
Neural Representations ◽  
Biological Constraints

AbstractPrefrontal cortical neurons play in important roles in performing rule-dependent tasks and working memory-based decision making. Motivated by experimental data, we develop an excitatory-inhibitory spiking recurrent neural network (SRNN) to perform a rule-dependent two-alternative forced choice (2AFC) task. We imposed several important biological constraints onto the SRNN, and adapted the spike frequency adaptation (SFA) and SuperSpike gradient methods to update the network parameters. These proposed strategies enabled us to train the SRNN efficiently and overcome the vanishing gradient problem during error back propagation through time. The trained SRNN produced rule-specific tuning in single-unit representations, showing rule-dependent population dynamics that strongly resemble experimentally observed data in rodent and monkey. Under varying test conditions, we further manipulated the parameters or configuration in computer simulation setups and investigated the impacts of rule-coding error, delay duration, weight connectivity and sparsity, and excitation/inhibition (E/I) balance on both task performance and neural representations. Overall, our modeling study provides a computational framework to understand neuronal representations at a fine timescale during working memory and cognitive control.Author SummaryWorking memory and decision making are fundamental cognitive functions of the brain, but the circuit mechanisms of these brain functions remain incompletely understood. Neuroscientists have trained animals (rodents or monkeys) to perform various cognitive tasks while simultaneously recording the neural activity from specific neural circuits. To complement the experimental investigations, computational modeling may provide an alternative way to examine the neural representations of neuronal assemblies during task behaviors. Here we develop and train a spiking recurrent neural network (SRNN) consisting of balanced excitatory and inhibitory neurons to perform the rule-dependent working memory tasks Our computer simulations produce qualitatively similar results as the experimental findings. Moreover, the imposed biological constraints on the trained network provide additional channel to investigate cell type-specific population responses, cortical connectivity and robustness. Our work provides a computational platform to investigate neural representations and dynamics of cortical circuits a fine timescale during complex cognitive tasks.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

Molecular Generation Targeting Desired Electronic Properties via Deep Generative Models

2019 ◽  
Author(s):  
Qi Yuan ◽  
Alejandro Santana-Bonilla ◽  
Martijn Zwijnenburg ◽  
Kim Jelfs
Keyword(s):  
Neural Network ◽  
Electronic Properties ◽  
Transfer Learning ◽  
Recurrent Neural Network ◽  
Chemical Space ◽  
Generative Models ◽  
Molecular Features ◽  
Donor Acceptor ◽  
Homo Lumo ◽  
Training Sets

<p>The chemical space for novel electronic donor-acceptor oligomers with targeted properties was explored using deep generative models and transfer learning. A General Recurrent Neural Network model was trained from the ChEMBL database to generate chemically valid SMILES strings. The parameters of the General Recurrent Neural Network were fine-tuned via transfer learning using the electronic donor-acceptor database from the Computational Material Repository to generate novel donor-acceptor oligomers. Six different transfer learning models were developed with different subsets of the donor-acceptor database as training sets. We concluded that electronic properties such as HOMO-LUMO gaps and dipole moments of the training sets can be learned using the SMILES representation with deep generative models, and that the chemical space of the training sets can be efficiently explored. This approach identified approximately 1700 new molecules that have promising electronic properties (HOMO-LUMO gap <2 eV and dipole moment <2 Debye), 6-times more than in the original database. Amongst the molecular transformations, the deep generative model has learned how to produce novel molecules by trading off between selected atomic substitutions (such as halogenation or methylation) and molecular features such as the spatial extension of the oligomer. The method can be extended as a plausible source of new chemical combinations to effectively explore the chemical space for targeted properties.</p>

IMPLEMENTASI CHATBOT PADA PENDAFTARAN MAHASISWA BARU MENGGUNAKAN RECURRENT NEURAL NETWORK

2019 ◽  
Vol 24 (2) ◽  
pp. 91-101
Author(s):  
Tjut Awaliyah Zuraiyah ◽  
Dian Kartika Utami ◽  
Degi Herlambang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network

Chatbot adalah perangkat lunak yang dapat berkomunikasi dengan manusia menggunakan bahasa alami. Model percakapan menggunakan kecerdasan buatan agar mampu memahami ucapan pengguna dan memberi tanggapan yang relevan dengan masalah yang dibahas oleh pengguna. Pendaftaran mahasiswa baru memerlukan banyak informasi mengenai prosedur pendaftaran di perguruan tinggi. Website pendaftaran online di Universitas Pakuan masih sebatas berisi informasi umum. Penelitian ini bertujuan untuk membuat suatu aplikasi Chatbot otomatis yang dapat berkomunikasi dengan manusia mengenai informasi pendaftaran mahasiswa baru di Universitas Pakuan menggunakan Recurrent Neural Network (RNN) untuk klasifikasi teks. Aplikasi Chatbot diimplementasikan menggunakan bahasa pemrograman Python dan Telegram API. Tahapan pada implementasi Chatbot terdiri dari preprocessing, transformasi data ke format .JSON, pelatihan data, bag of word dan full connection. Pengujian aplikasi Chatbot menggunakan data sebanyak 251 kalimat pertanyaan tentang pendaftaran mahasiswa baru di Universitas Pakuan. Hasil pengujian menunjukkan bahwa Chatbot dapat menjawab pertanyaan mengenai pendaftaran mahasiswa baru dengan akurasi sebesar 88%, presisi sebesar 95% dan recall sebesar 92%.

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