scholarly journals Estimating and interpreting nonlinear receptive fields of sensory responses with deep neural network models

2019 ◽  
Author(s):  
Menoua Keshishian ◽  
Hassan Akbari ◽  
Bahar Khalighinejad ◽  
Jose Herrero ◽  
Ashesh D. Mehta ◽  
...  
Keyword(s):  
Neural Network ◽  
Receptive Field ◽  
Sensory Processing ◽  
Deep Neural Network ◽  
Data Driven ◽  
Sensory Response ◽  
Nonlinear Transformations ◽  
Stimulus Response ◽  
Exact Function ◽  
Neurosurgical Patients

AbstractSensory processing by neural circuits includes numerous nonlinear transformations that are critical to perception. Our understanding of these nonlinear mechanisms, however, is hindered by the lack of a comprehensive and interpretable computational framework that can model and explain nonlinear signal transformations. Here, we propose a data-driven framework based on deep neural network regression models that can directly learn any nonlinear stimulus-response mapping. A key component of this approach is an analysis method that reformulates the exact function of the trained neural network as a collection of stimulus-dependent linear functions. This locally linear receptive field interpretation of the network function enables straightforward comparison with conventional receptive field models and uncovers nonlinear encoding properties. We demonstrate the efficacy of this framework by predicting the neural responses recorded invasively from the auditory cortex of neurosurgical patients as they listened to speech. Our method significantly improves the prediction accuracy of auditory cortical responses particularly in nonprimary areas. Moreover, interpreting the functions learned by neural networks uncovered three distinct types of nonlinear transformations of speech that varied considerably in primary and nonprimary auditory regions. By combining two desired properties of a computational sensory-response model; the ability to capture arbitrary stimulus-response mappings and maintaining model interpretability, this data-driven method can lead to better neurophysiological models of the sensory processing.

scholarly journals Estimating and interpreting nonlinear receptive field of sensory neural responses with deep neural network models

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Menoua Keshishian ◽  
Hassan Akbari ◽  
Bahar Khalighinejad ◽  
Jose L Herrero ◽  
Ashesh D Mehta ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Receptive Field ◽  
Network Models ◽  
Linear Functions ◽  
Neural Responses ◽  
Neural Network Models ◽  
Stimulus Response ◽  
Exact Function ◽  
Neurosurgical Patients

Our understanding of nonlinear stimulus transformations by neural circuits is hindered by the lack of comprehensive yet interpretable computational modeling frameworks. Here, we propose a data-driven approach based on deep neural networks to directly model arbitrarily nonlinear stimulus-response mappings. Reformulating the exact function of a trained neural network as a collection of stimulus-dependent linear functions enables a locally linear receptive field interpretation of the neural network. Predicting the neural responses recorded invasively from the auditory cortex of neurosurgical patients as they listened to speech, this approach significantly improves the prediction accuracy of auditory cortical responses, particularly in nonprimary areas. Moreover, interpreting the functions learned by neural networks uncovered three distinct types of nonlinear transformations of speech that varied considerably from primary to nonprimary auditory regions. The ability of this framework to capture arbitrary stimulus-response mappings while maintaining model interpretability leads to a better understanding of cortical processing of sensory signals.

scholarly journals Optimising Performance for NB-IoT UE Devices through Data Driven Models

2021 ◽  
Vol 10 (1) ◽  
pp. 21
Author(s):  
Omar Nassef ◽  
Toktam Mahmoodi ◽  
Foivos Michelinakis ◽  
Kashif Mahmood ◽  
Ahmed Elmokashfi
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Gradient Descent ◽  
Deep Neural Network ◽  
Narrow Band ◽  
Learning Algorithm ◽  
Base Station ◽  
User Equipment ◽  
Data Driven ◽  
Superior Performance

This paper presents a data driven framework for performance optimisation of Narrow-Band IoT user equipment. The proposed framework is an edge micro-service that suggests one-time configurations to user equipment communicating with a base station. Suggested configurations are delivered from a Configuration Advocate, to improve energy consumption, delay, throughput or a combination of those metrics, depending on the user-end device and the application. Reinforcement learning utilising gradient descent and genetic algorithm is adopted synchronously with machine and deep learning algorithms to predict the environmental states and suggest an optimal configuration. The results highlight the adaptability of the Deep Neural Network in the prediction of intermediary environmental states, additionally the results present superior performance of the genetic reinforcement learning algorithm regarding its performance optimisation.

Data-driven prognostics method for turbofan engine degradation using hybrid deep neural network

2021 ◽  
Vol 35 (12) ◽  
pp. 5371-5387
Author(s):  
Bin Xue ◽  
Zhong-bin Xu ◽  
Xing Huang ◽  
Peng-cheng Nie
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Data Driven ◽  
Turbofan Engine

scholarly journals Application of deep neural network reveals novel effects of maternal pre-conception exposure to nicotine on rat pup behavior

2020 ◽  
Author(s):  
Reza Torabi ◽  
Serena Jenkins ◽  
Allonna Harker ◽  
Ian Q. Whishaw ◽  
Robbin Gibb ◽  
...  
Keyword(s):  
Neural Network ◽  
Motor Development ◽  
Deep Neural Network ◽  
Data Driven ◽  
Nicotine Exposure ◽  
Movement Initiation ◽  
Warm Up ◽  
Rat Pups ◽  
The Neural Network ◽  
Data Driven Approach

We present a deep neural network for data-driven analyses of infant rat behavior in an open field task. The network was applied to study the effect of maternal nicotine exposure prior to conception on offspring motor development. The neural network outperformed human expert designed animal locomotion measures in distinguishing rat pups born to nicotine exposed dams versus control dams. Notably, the network discovered novel movement alterations in posture, movement initiation and a stereotypy in warm-up behavior (the initiation of movement along specific dimensions) that were predictive of nicotine exposure. The results suggest that maternal preconception nicotine exposure delays and alters offspring motor development. In summary, we demonstrated that a deep neural network can automatically assess animal behavior with high accuracy, and that it offers a data-driven approach to investigating pharmacological effects on brain development.

Interpretability of deep convolutional neural networks on rolling bearing fault diagnosis

2021 ◽  
Author(s):  
Huixin Yang ◽  
Xiang Li ◽  
Wei Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Deep Neural Network ◽  
Rapid Development ◽  
Saliency Map ◽  
Data Driven ◽  
Experimental Investigations ◽  
Deep Convolutional Neural Networks

Abstract Despite the rapid development of deep learning-based intelligent fault diagnosis methods on rotating machinery, the data-driven approach generally remains a "black box" to researchers, and its internal mechanism has not been sufficiently understood. The weak interpretability significantly impedes further development and applications of the effective deep neural network-based methods. This paper contributes efforts to understanding the mechanical signal processing of deep learning on the fault diagnosis problems. The diagnostic knowledge learned by the deep neural network is visualized using the neuron activation maximization and the saliency map methods. The discriminative features of different machine health conditions are intuitively observed. The relationship between the data-driven methods and the well-established conventional fault diagnosis knowledge is confirmed by the experimental investigations on two datasets. The results of this study can benefit researchers on understanding the complex neural networks, and increase the reliability of the data-driven fault diagnosis model in the real engineering cases.

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