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179 results on '"Lesica, Nicholas A."'

1. Ch23

Author

Lesica, Nicholas A.

Published
2017

2. Harnessing the Power of Artificial Intelligence in Otolaryngology and the Communication Sciences

Author

Wilson, Blake S, Tucci, Debara L, Moses, David A, Chang, Edward F, Young, Nancy M, Zeng, Fan-Gang, Lesica, Nicholas A, Bur, Andrés M, Kavookjian, Hannah, Mussatto, Caroline, Penn, Joseph, Goodwin, Sara, Kraft, Shannon, Wang, Guanghui, Cohen, Jonathan M, Ginsburg, Geoffrey S, Dawson, Geraldine, and Francis, Howard W

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Neurosciences, Psychology, Artificial Intelligence, Communication, Humans, Otolaryngology, Machine learning, Artificial intelligence, Deep learning, Human communication, Hearing, Speech production, Speech perception, Auditory prostheses, Auditory system, Hearing aids, Hearing loss, Cochlear implants, Neural prostheses, Neuroprostheses, Brain-computer interfaces, Laryngeal pathology, Thyroid pathology, Clinical Sciences, Otorhinolaryngology, Biological psychology
Abstract

Use of artificial intelligence (AI) is a burgeoning field in otolaryngology and the communication sciences. A virtual symposium on the topic was convened from Duke University on October 26, 2020, and was attended by more than 170 participants worldwide. This review presents summaries of all but one of the talks presented during the symposium; recordings of all the talks, along with the discussions for the talks, are available at https://www.youtube.com/watch?v=ktfewrXvEFg and https://www.youtube.com/watch?v=-gQ5qX2v3rg . Each of the summaries is about 2500 words in length and each summary includes two figures. This level of detail far exceeds the brief summaries presented in traditional reviews and thus provides a more-informed glimpse into the power and diversity of current AI applications in otolaryngology and the communication sciences and how to harness that power for future applications.

Published
2022

5. Hyperdimensional Brain-Inspired Learning for Phoneme Recognition With Large-Scale Inferior Colliculus Neural Activities

Author

Ni, Yang, Yang, Ye, Chen, Hanning, Wang, Xianhui, Lesica, Nicholas, Zeng, Fan-gang, and Imani, Mohsen

Abstract

Objective: Develop a novel and highly efficient framework that decodes Inferior Colliculus (IC) neural activities for phoneme recognition. Methods: We propose using Hyperdimensional Computing (HDC) to support an efficient phoneme recognition algorithm, in contrast to widely applied Deep Neural Networks (DNN). The high-dimensional representation and operations in HDC are rooted in human brain functionalities and naturally parallelizable, showing the potential for efficient neural activity analysis. Our proposed method includes a spatial and temporal-aware HDC encoder that effectively captures global and local patterns. As part of our framework, we deploy the lightweight HDC-based algorithm on a highly customizable and flexible hardware platform, i.e., Field Programmable Gate Arrays (FPGA), for optimal algorithm speedup. To evaluate our method, we record IC neural activities on gerbils while playing the sound of different phonemes. Results: We compare our proposed method with multiple baseline machine learning algorithms in recognition quality and learning efficiency, across different hardware platforms. The results show that our method generally achieves better classification quality than the best-performing baseline. Compared to the Deep Residual Neural Network (i.e., ResNet), our method shows a speedup up to 74×, 67×, 210× on CPU, GPU, and FPGA respectively. We achieve up to 15% (10%) higher accuracy in consonant (vowel) classification than ResNet. Conclusion: By leveraging brain-inspired HDC for IC neural activity encoding and phoneme classification, we achieve orders of magnitude runtime speedup while improving accuracy in various challenging task settings. Significance: Decoding IC neural activities is an important step to enhance understanding about human auditory system. However, these responses from the central auditory system are noisy and contain high variance, demanding large-scale datasets and iterative model fine-tuning. The proposed HDC-based framework is more scalable and viable for future real-world deployment thanks to its fast training and overall better quality.

Published
2024
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10. A Conversation about Healthy Eating

Author

Lesica, Nicholas A.

Subjects
Family & Relationships / Activities, Health & Fitness, Medical / Diet Therapy
Abstract

What constitutes a healthy diet? Mainstream media and advertisers would like you to think that the answer to this question is complicated and controversial. But science, fortunately, tells us otherwise. A Conversation about Healthy Eating brings together all the relevant science about healthy eating in one place, and it’s exactly that – a conversation; an informal discussion between a scientist and a friend about their eating habits, keeping the science firmly rooted in everyday life. The conversation moves from topics such as metabolism and digestion to gut bacteria, hormones, neuroscience and the immune system. All of these concepts are explained in accessible terms to help you understand the roles they play in maintaining a healthy diet. The conversation leads to the conclusion that staying lean and healthy simply requires avoiding the overconsumption of processed foods. While this is, of course, easier said than done, science also provides clear recommendations for how you can adapt your environment and lifestyle to make it possible. Rather than simply presenting you with the principles of healthy eating, this book will help you to develop a comprehensive understanding of the science behind the principles, including the evolutionary facts that affect the way we eat today. This understanding will allow you to ignore the noise in the media and to move forward with a healthy lifestyle that work for you.

Published
2017

14. An LGN Inspired Detect/Transmit Framework for High Fidelity Relay of Visual Information with Limited Bandwidth

Author

Lesica, Nicholas A., Stanley, Garrett B., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, De Gregorio, Massimo, editor, Di Maio, Vito, editor, Frucci, Maria, editor, and Musio, Carlo, editor

Published
2005
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19. Harnessing the Power of Artificial Intelligence in Otolaryngology and the Communication Sciences.

Author

Wilson, Blake S., Tucci, Debara L., Moses, David A., Chang, Edward F., Young, Nancy M., Zeng, Fan-Gang, Lesica, Nicholas A., Bur, Andrés M., Kavookjian, Hannah, Mussatto, Caroline, Penn, Joseph, Goodwin, Sara, Kraft, Shannon, Wang, Guanghui, Cohen, Jonathan M., Ginsburg, Geoffrey S., Dawson, Geraldine, and Francis, Howard W.

Abstract

Use of artificial intelligence (AI) is a burgeoning field in otolaryngology and the communication sciences. A virtual symposium on the topic was convened from Duke University on October 26, 2020, and was attended by more than 170 participants worldwide. This review presents summaries of all but one of the talks presented during the symposium; recordings of all the talks, along with the discussions for the talks, are available at https://www.youtube.com/watch?v=ktfewrXvEFg and https://www.youtube.com/watch?v=-gQ5qX2v3rg. Each of the summaries is about 2500 words in length and each summary includes two figures. This level of detail far exceeds the brief summaries presented in traditional reviews and thus provides a more-informed glimpse into the power and diversity of current AI applications in otolaryngology and the communication sciences and how to harness that power for future applications. [ABSTRACT FROM AUTHOR]

Published
2022
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20. Temporal precision in the neural code and the timescales of natural vision

Author

Butts, Daniel A., Weng, Chong, Jin, Jianzhong, Yeh, Chun-I, Lesica, Nicholas A., Alonso, Jose-Manuel, and Stanley, Garrett B.

Subjects
Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Author(s): Daniel A. Butts (corresponding author) [1, 2]; Chong Weng [3]; Jianzhong Jin [3]; Chun-I Yeh [3, 4]; Nicholas A. Lesica [1]; Jose-Manuel Alonso [3]; Garrett B. Stanley [1] The [...]

Published
2007

26. Stimulus relevance modulates contrast adaptation in visual cortex

Author

Keller, Andreas J, Houlton, Rachael, Kampa, Björn M, Lesica, Nicholas A, Mrsic-Flogel, Thomas D, Keller, Georg B, Helmchen, Fritjof, Keller, Andreas J, Houlton, Rachael, Kampa, Björn M, Lesica, Nicholas A, Mrsic-Flogel, Thomas D, Keller, Georg B, and Helmchen, Fritjof

Published
2017

50. One hundred ways to process time, frequency, rate and scale in the central auditory system: a pattern-recognition meta-analysis.

Author

Hemery, Edgar, Aucouturier, Jean-Julien, Lesica, Nicholas A., and Sen Song

Subjects
AUDITORY pathways, PATTERN recognition systems, FEATURE extraction, NEURONS, NEURAL circuitry
Abstract

The mammalian auditory system extracts features from the acoustic environment based on the responses of spatially distributed sets of neurons in the subcortical and cortical auditory structures. The characteristic responses of these neurons (linearly approximated by their spectro-temporal receptive fields, or STRFs) suggest that auditory representations are formed, as early as in the inferior colliculi, on the basis of a time, frequency, rate (temporal modulations) and scale (spectral modulations) analysis of sound. However, how these four dimensions are integrated and processed in subsequent neural networks remains unclear. In this work, we present a new methodology to generate computational insights into the functional organization of such processes. We first propose a systematic framework to explore more than a hundred different computational strategies proposed in the literature to process the output of a generic STRF model. We then evaluate these strategies on their ability to compute perceptual distances between pairs of environmental sounds. Finally, we conduct a meta-analysis of the dataset of all these algorithms' accuracies to examine whether certain combinations of dimensions and certain ways to treat such dimensions are, on the whole, more computationally effective than others. We present an application of this methodology to a dataset of ten environmental sound categories, in which the analysis reveals that (1) models are most effective when they organize STRF data into frequency groupings--which is consistent with the known tonotopic organization of receptive fields in auditory structures -, and that (2) models that treat STRF data as time series are no more effective than models that rely only on summary statistics along time--which corroborates recent experimental evidence on texture discrimination by summary statistics. [ABSTRACT FROM AUTHOR]

Published
2015
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