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835 results on '"Chakravarthy, V"'

1. Deep Oscillatory Neural Network

Author

Rohan, Nurani Rajagopal, C, Vigneswaran, Ghosh, Sayan, Rajendran, Kishore, A, Gaurav, and Chakravarthy, V Srinivasa

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

We propose a novel, brain-inspired deep neural network model known as the Deep Oscillatory Neural Network (DONN). Deep neural networks like the Recurrent Neural Networks indeed possess sequence processing capabilities but the internal states of the network are not designed to exhibit brain-like oscillatory activity. With this motivation, the DONN is designed to have oscillatory internal dynamics. Neurons of the DONN are either nonlinear neural oscillators or traditional neurons with sigmoidal or ReLU activation. The neural oscillator used in the model is the Hopf oscillator, with the dynamics described in the complex domain. Input can be presented to the neural oscillator in three possible modes. The sigmoid and ReLU neurons also use complex-valued extensions. All the weight stages are also complex-valued. Training follows the general principle of weight change by minimizing the output error and therefore has an overall resemblance to complex backpropagation. A generalization of DONN to convolutional networks known as the Oscillatory Convolutional Neural Network is also proposed. The two proposed oscillatory networks are applied to a variety of benchmark problems in signal and image/video processing. The performance of the proposed models is either comparable or superior to published results on the same data sets.

Published
2024

2. A Chaotic Associative Memory

Author

Rohan, Nurani Rajagopal, Gupta, Sayan, and Chakravarthy, V. Srinivasa

Subjects
Quantitative Biology - Neurons and Cognition, Nonlinear Sciences - Chaotic Dynamics
Abstract

We propose a novel Chaotic Associative Memory model using a network of chaotic Rossler systems and investigate the storage capacity and retrieval capabilities of this model as a function of increasing periodicity and chaos. In early models of associate memory networks, memories were modeled as fixed points, which may be mathematically convenient but has poor neurobiological plausibility. Since brain dynamics is inherently oscillatory, attempts have been made to construct associative memories using nonlinear oscillatory networks. However, oscillatory associative memories are plagued by the problem of poor storage capacity, though efforts have been made to improve capacity by adding higher order oscillatory modes. The chaotic associative memory proposed here exploits the continuous spectrum of chaotic elements and has higher storage capacity than previously described oscillatory associate memories., Comment: 10 pages, 8 Figures, Submitted to "Chaos: An Interdisciplinary Journal of Nonlinear Science"

Published
2024

8. An end-to-end, interactive Deep Learning based Annotation system for cursive and print English handwritten text

Author

Guruprasad, Pranav, S, Sujith Kumar, C, Vigneswaran, and Chakravarthy, V. Srinivasa

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

With the surging inclination towards carrying out tasks on computational devices and digital mediums, any method that converts a task that was previously carried out manually, to a digitized version, is always welcome. Irrespective of the various documentation tasks that can be done online today, there are still many applications and domains where handwritten text is inevitable, which makes the digitization of handwritten documents a very essential task. Over the past decades, there has been extensive research on offline handwritten text recognition. In the recent past, most of these attempts have shifted to Machine learning and Deep learning based approaches. In order to design more complex and deeper networks, and ensure stellar performances, it is essential to have larger quantities of annotated data. Most of the databases present for offline handwritten text recognition today, have either been manually annotated or semi automatically annotated with a lot of manual involvement. These processes are very time consuming and prone to human errors. To tackle this problem, we present an innovative, complete end-to-end pipeline, that annotates offline handwritten manuscripts written in both print and cursive English, using Deep Learning and User Interaction techniques. This novel method, which involves an architectural combination of a detection system built upon a state-of-the-art text detection model, and a custom made Deep Learning model for the recognition system, is combined with an easy-to-use interactive interface, aiming to improve the accuracy of the detection, segmentation, serialization and recognition phases, in order to ensure high quality annotated data with minimal human interaction., Comment: 17 pages, 8 figures, 2 tables

Published
2023
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13. Oscillatory Network and Deep Value Network Based Memory Replay Model of Hippocampus

Author

Kanagamani, Tamizharasan, Muliya, Madhuvanthi, Chakravarthy, V. Srinivasa, Ravindran, Balaraman, Menon, Ramshekhar N., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Maji, Pradipta, editor, Huang, Tingwen, editor, Pal, Nikhil R., editor, Chaudhury, Santanu, editor, and De, Rajat K., editor

Published
2023
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14. Brain-Inspired Attention Model for Object Counting

Author

Sinha, Abhijeet, Kumari, Sweta, Chakravarthy, V. Srinivasa, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Tanveer, Mohammad, editor, Agarwal, Sonali, editor, Ozawa, Seiichi, editor, Ekbal, Asif, editor, and Jatowt, Adam, editor

Published
2023
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15. A Phenomenological Deep Oscillatory Neural Network Model to Capture the Whole Brain Dynamics in Terms of BOLD Signal

Author

Bandyopadhyay, Anirban, Ghosh, Sayan, Biswas, Dipayan, Surampudi, Raju Bapi, Chakravarthy, V. Srinivasa, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Tanveer, Mohammad, editor, Agarwal, Sonali, editor, Ozawa, Seiichi, editor, Ekbal, Asif, editor, and Jatowt, Adam, editor

Published
2023
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16. A Novel DDOS Attack Detection and Prevention Using DSA-DPI Method

Author

Deeban Chakravarthy, V., Prakash, K L. N. C., Ramana, Kadiyala, Gadekallu, Thippa Reddy, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Gupta, Deepak, editor, Khanna, Ashish, editor, Hassanien, Aboul Ella, editor, Anand, Sameer, editor, and Jaiswal, Ajay, editor

Published
2023
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17. A Novel Technique of Threshold Distance-Based Vehicle Tracking System for Woman Safety

Author

Sekhar, B. V. D. S., Chakravarthy, V. V. S. S. S., Venkataramana, S., Raju, Bh. V. S. R. K., Udayakumar, N., Rao, S. Krishna, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Bhateja, Vikrant, editor, Sunitha, K. V. N., editor, Chen, Yen-Wei, editor, and Zhang, Yu-Dong, editor

Published
2023
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18. Home Automation Using Telegram Bot

Author

Cheerla, Sree Vardhan, Chakravarthy, V. V. N., KishoreBabu, K., GopiRam, V., Bansal, Jagdish Chand, Series Editor, Deep, Kusum, Series Editor, Nagar, Atulya K., Series Editor, Gunjan, Vinit Kumar, editor, Suganthan, P. N., editor, Haase, Jan, editor, and Kumar, Amit, editor

Published
2023
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20. A Computational Model of Levodopa-Induced Toxicity in Substantia Nigra Pars Compacta in Parkinson's Disease

Author

Muddapu, Vignayanandam R., Vijayakumar, Karthik, Ramakrishnan, Keerthiga, and Chakravarthy, V Srinivasa

Subjects
Quantitative Biology - Neurons and Cognition, Quantitative Biology - Tissues and Organs
Abstract

Parkinson's disease (PD) is caused by the progressive loss of dopaminergic cells in substantia nigra pars compacta (SNc). The root cause of this cell loss in PD is still not decisively elucidated. A recent line of thinking traces the cause of PD neurodegeneration to metabolic deficiency. Due to exceptionally high energy demand, SNc neurons exhibit a higher basal metabolic rate and higher oxygen consumption rate, which results in oxidative stress. Recently, we have suggested that the excitotoxic loss of SNc cells might be due to energy deficiency occurring at different levels of neural hierarchy. Levodopa (LDOPA), a precursor of dopamine, which is used as a symptom-relieving treatment for PD, leads to outcomes that are both positive and negative. Several researchers suggested that LDOPA might be harmful to SNc cells due to oxidative stress. The role of LDOPA in the course of PD pathogenesis is still debatable. We hypothesize that energy deficiency can lead to LDOPA-induced toxicity (LIT) in two ways: by promoting dopamine-induced oxidative stress and by exacerbating excitotoxicity in SNc. We present a multiscale computational model of SNc-striatum system, which will help us in understanding the mechanism behind neurodegeneration postulated above and provides insights for developing disease-modifying therapeutics. It was observed that SNc terminals are more vulnerable to energy deficiency than SNc somas. During LDOPA therapy, it was observed that higher LDOPA dosage results in increased loss of somas and terminals in SNc. It was also observed that co-administration of LDOPA and glutathione (antioxidant) evades LDOPA-induced toxicity in SNc neurons. We show that our proposed model was able to capture LDOPA-induced toxicity in SNc, caused by energy deficiency.

Published
2020

23. Basal Ganglia System as an Engine for Exploration

Author

Chakravarthy, V. Srinivasa, Balasubramani, Pragathi Priyadharsini, Migliore, Michele, Section editor, Linster, Christiane, Section editor, Cavarretta, Francesco, Section editor, Jaeger, Dieter, editor, and Jung, Ranu, editor

Published
2022
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24. Brain Tumor Classification Using Decision Tree and Neural Network Classifiers

Author

Venkata Subbarao, M., Sudheer Kumar, T., Chowdary, P. S. R., Chakravarthy, V. V. S. S. S., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Bhateja, Vikrant, editor, Khin Wee, Lai, editor, Lin, Jerry Chun-Wei, editor, Satapathy, Suresh Chandra, editor, and Rajesh, T. M., editor

Published
2022
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25. Markovian Queueing Model with Server Breakdown, Single Working Vacation, and Catastrophe

Author

Seenivasan, M., Chakravarthy, V. J., Abinaya, R., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Sengodan, Thangaprakash, editor, Murugappan, M., editor, and Misra, Sanjay, editor

Published
2022
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27. Unified Flow Based Approach in Software-Defined Networks

Author

Chakravarthy, V. Deeban, Chandra, Dara Sujith, Pavan, Seemakurthi Sri Sathya, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Tuba, Milan, editor, Akashe, Shyam, editor, and Joshi, Amit, editor

Published
2022
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28. Estimation and Simulation of Flows into an off-Taking Canal Using ANSYS

Author

Chakravarthy, V., Prasanna, S. V. S. N. D. L., Suresh Kumar, N., di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Rao, Chintalacheruvu Madhusudana, editor, Patra, K. C., editor, Jhajharia, D., editor, and Kumari, Sangeeta, editor

Published
2022
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29. An End-to-End, Interactive Deep Learning Based Annotation System for Cursive and Print English Handwritten Text

Author

Guruprasad, Pranav, Sujith Kumar, S., Vigneswaran, C., Chakravarthy, V. Srinivasa, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Kumar, Amit, editor, Senatore, Sabrina, editor, and Gunjan, Vinit Kumar, editor

Published
2022
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38. A study of microbiology in diabetic foot infections

Author

Karvande RK, Merchant T, Shinde PH, Takalkar YP, Chaudhari S, Chakravarthy V, Deshmukh VK, and Admane K

Subjects
diabetes, antibiotics, microbiology, bacteria, foot, Medicine (General), R5-920
Abstract

Background: Diabetic foot ulcers and infections are a common complication of diabetes. Holistic patient care requires dissemination of knowledge about prevention to avoid amputation and associated morbidity, early diagnosis and apt treatment. Healthcare costs of diabetic ulcers are high as complete treatment requires long term monitoring and high cost of rehabilitation in amputees. Methods: The study design is retrospective observational study. All data was retrospectively procured from the Medical Record Department. From January 2019 to December 2019, two hundred and twenty patients with diabetic foot infections who had been treated with oral or parenteral antibiotics in the previous 5 to 15 days were chosen. Results: The gram negative bacteria (88%) were most commonly isolated. Gram positive organisms contributed to 11% and candida spp. to 1% of swab culture. All patients with Staphylococci in wound swab were 100% sensitivity to vancomycin & linezolid. Teicoplanin, vancomycin, and clindamycin had 100% sensitivity against MRSA in wound swab. Patients with E. coli in wound swab had maximum sensitivity to colistin. Ceftriaxone which has been used conventionally was found less sensitive for E. coli and Klebsiella. Conclusion: Piperacillin tazobactam combination for gram negative coverage in accordance to sensitivity pattern of community acquired infection is recommended because conventionally used ceftriaxone is less sensitive. For gram positive diabetic foot ulcers/ infections which were seen to be hospital acquired, linezolid is recommended for full course.

Published
2022
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39. Flip Flop Neural Networks: Modelling Memory for Efficient Forecasting

Author

Sujith Kumar, S., Vigneswaran, C., Srinivasa Chakravarthy, V., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Li, Yong, Series Editor, and Gopi, E. S., editor

Published
2021
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40. Recent Trends and Challenges in Blockchain Technology

Author

Shyam Mohan, J. S., Challa, Nagendra Panini, Chakravarthy, V. V. Kalyan, Kumar, G. P. Siva, Rao, R. Subba, Raju, P. Venkata Rama, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Sekhar, G.T. Chandra, editor, Behera, H. S., editor, Nayak, Janmenjoy, editor, Naik, Bighnaraj, editor, and Pelusi, Danilo, editor

Published
2021
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41. Pattern Recognition of Time-Varying Signals Using Ensemble Classifiers

Author

Subbarao, M. Venkata, Terlapu, Sudheer Kumar, Chakravarthy, V. V. S. S. S., Satapaty, Suresh Chandra, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Chowdary, P. Satish Rama, editor, Chakravarthy, V.V.S.S.S., editor, Anguera, Jaume, editor, Satapathy, Suresh Chandra, editor, and Bhateja, Vikrant, editor

Published
2021
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42. Pattern Recovery in Linear Arrays Using Grasshopper Optimization Algorithm

Author

Chakravarthy, V. V. S. S. S., Chowdary, P. Satish Rama, Anguera, Jaume, Mokara, Divya, Satapathy, Suresh Chandra, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Chowdary, P. Satish Rama, editor, Chakravarthy, V.V.S.S.S., editor, Anguera, Jaume, editor, Satapathy, Suresh Chandra, editor, and Bhateja, Vikrant, editor

Published
2021
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43. Convolutional Elman Jordan Neural Network for Reconstruction and Classification Using Attention Window

Author

Kumari, Sweta, Aravindakshan, S., Jain, Umangi, Srinivasa Chakravarthy, V., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Sharma, Manoj Kumar, editor, Dhaka, Vijaypal Singh, editor, Perumal, Thinagaran, editor, Dey, Nilanjan, editor, and Tavares, João Manuel R. S., editor

Published
2021
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44. Elman and Jordan Recurrence in Convolutional Neural Networks Using Attention Window

Author

Kumari, Sweta, Aravindakshan, S., Srinivasa Chakravarthy, V., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Gupta, Deepak, editor, Khanna, Ashish, editor, Bhattacharyya, Siddhartha, editor, Hassanien, Aboul Ella, editor, Anand, Sameer, editor, and Jaiswal, Ajay, editor

Published
2021
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45. An AI-Based Detection System for Mudrabharati: A Novel Unified Fingerspelling System for Indic Scripts

Author

Amal Jude Ashwin, F., Chakravarthy, V. Srinivasa, Kopparapu, Sunil Kumar, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ekštein, Kamil, editor, Pártl, František, editor, and Konopík, Miloslav, editor

Published
2021
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46. Modeling hippocampal spatial cells in rodents navigating in 3D environments.

Author

Aziz, Azra, Patil, Bharat K., Lakshmikanth, Kailash, Sreeharsha, Peesapati S. S., Mukhopadhyay, Ayan, and Chakravarthy, V. Srinivasa

Subjects
THETA rhythm, ARTIFICIAL neural networks, GRID cells, RODENTS, HIPPOCAMPUS (Brain)
Abstract

Studies on the neural correlates of navigation in 3D environments are plagued by several issues that need to be solved. For example, experimental studies show markedly different place cell responses in rats and bats, both navigating in 3D environments. In this study, we focus on modelling the spatial cells in rodents in a 3D environment. We propose a deep autoencoder network to model the place and grid cells in a simulated agent navigating in a 3D environment. The input layer to the autoencoder network model is the HD layer, which encodes the agent's HD in terms of azimuth (θ) and pitch angles (ϕ). The output of this layer is given as input to the Path Integration (PI) layer, which computes displacement in all the preferred directions. The bottleneck layer of the autoencoder model encodes the spatial cell-like responses. Both grid cell and place cell-like responses are observed. The proposed model is verified using two experimental studies with two 3D environments. This model paves the way for a holistic approach using deep neural networks to model spatial cells in 3D navigation. [ABSTRACT FROM AUTHOR]

Published
2024
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