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Wang J, Qian L, Wang S, Shi L, Wang Z. Directional Preference in Avian Midbrain Saliency Computing Nucleus Reflects a Well-Designed Receptive Field Structure. Animals (Basel) 2022; 12:1143. [PMID: 35565569 PMCID: PMC9105111 DOI: 10.3390/ani12091143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 04/18/2022] [Accepted: 04/26/2022] [Indexed: 11/20/2022] Open
Abstract
Neurons responding sensitively to motions in several rather than all directions have been identified in many sensory systems. Although this directional preference has been demonstrated by previous studies to exist in the isthmi pars magnocellularis (Imc) of pigeon (Columba livia), which plays a key role in the midbrain saliency computing network, the dynamic response characteristics and the physiological basis underlying this phenomenon are unclear. Herein, dots moving in 16 directions and a biologically plausible computational model were used. We found that pigeon Imc's significant responses for objects moving in preferred directions benefit the long response duration and high instantaneous firing rate. Furthermore, the receptive field structures predicted by a computational model, which captures the actual directional tuning curves, agree with the real data collected from population Imc units. These results suggested that directional preference in Imc may be internally prebuilt by elongating the vertical axis of the receptive field, making predators attack from the dorsal-ventral direction and conspecifics flying away in the ventral-dorsal direction, more salient for avians, which is of great ecological and physiological significance for survival.
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Affiliation(s)
- Jiangtao Wang
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China; (J.W.); (L.Q.); (S.W.)
| | - Longlong Qian
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China; (J.W.); (L.Q.); (S.W.)
| | - Songwei Wang
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China; (J.W.); (L.Q.); (S.W.)
| | - Li Shi
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China; (J.W.); (L.Q.); (S.W.)
- Department of Automation, Tsinghua University, Beijing 100084, China
| | - Zhizhong Wang
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China; (J.W.); (L.Q.); (S.W.)
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Retinal Drug Delivery: Rethinking Outcomes for the Efficient Replication of Retinal Behavior. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10124258] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
The retina is a highly organized structure that is considered to be "an approachable part of the brain." It is attracting the interest of development scientists, as it provides a model neurovascular system. Over the last few years, we have been witnessing significant development in the knowledge of the mechanisms that induce the shape of the retinal vascular system, as well as knowledge of disease processes that lead to retina degeneration. Knowledge and understanding of how our vision works are crucial to creating a hardware-adaptive computational model that can replicate retinal behavior. The neuronal system is nonlinear and very intricate. It is thus instrumental to have a clear view of the neurophysiological and neuroanatomic processes and to take into account the underlying principles that govern the process of hardware transformation to produce an appropriate model that can be mapped to a physical device. The mechanistic and integrated computational models have enormous potential toward helping to understand disease mechanisms and to explain the associations identified in large model-free data sets. The approach used is modulated and based on different models of drug administration, including the geometry of the eye. This work aimed to review the recently used mathematical models to map a directed retinal network.
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Feng W, Halm-Lutterodt NV, Tang H, Mecum A, Mesregah MK, Ma Y, Li H, Zhang F, Wu Z, Yao E, Guo X. Automated MRI-Based Deep Learning Model for Detection of Alzheimer’s Disease Process. Int J Neural Syst 2020; 30:2050032. [DOI: 10.1142/s012906572050032x] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
In the context of neuro-pathological disorders, neuroimaging has been widely accepted as a clinical tool for diagnosing patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI). The advanced deep learning method, a novel brain imaging technique, was applied in this study to evaluate its contribution to improving the diagnostic accuracy of AD. Three-dimensional convolutional neural networks (3D-CNNs) were applied with magnetic resonance imaging (MRI) to execute binary and ternary disease classification models. The dataset from the Alzheimer’s disease neuroimaging initiative (ADNI) was used to compare the deep learning performances across 3D-CNN, 3D-CNN-support vector machine (SVM) and two-dimensional (2D)-CNN models. The outcomes of accuracy with ternary classification for 2D-CNN, 3D-CNN and 3D-CNN-SVM were [Formula: see text]%, [Formula: see text]% and [Formula: see text]% respectively. The 3D-CNN-SVM yielded a ternary classification accuracy of 93.71%, 96.82% and 96.73% for NC, MCI and AD diagnoses, respectively. Furthermore, 3D-CNN-SVM showed the best performance for binary classification. Our study indicated that ‘NC versus MCI’ showed accuracy, sensitivity and specificity of 98.90%, 98.90% and 98.80%; ‘NC versus AD’ showed accuracy, sensitivity and specificity of 99.10%, 99.80% and 98.40%; and ‘MCI versus AD’ showed accuracy, sensitivity and specificity of 89.40%, 86.70% and 84.00%, respectively. This study clearly demonstrates that 3D-CNN-SVM yields better performance with MRI compared to currently utilized deep learning methods. In addition, 3D-CNN-SVM proved to be efficient without having to manually perform any prior feature extraction and is totally independent of the variability of imaging protocols and scanners. This suggests that it can potentially be exploited by untrained operators and extended to virtual patient imaging data. Furthermore, owing to the safety, noninvasiveness and nonirradiative properties of the MRI modality, 3D-CNN-SMV may serve as an effective screening option for AD in the general population. This study holds value in distinguishing AD and MCI subjects from normal controls and to improve value-based care of patients in clinical practice.
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Affiliation(s)
- Wei Feng
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, You’anmenwai, Xitoutiao No.10, Beijing, P. R. China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, P. R. China
| | - Nicholas Van Halm-Lutterodt
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, P. R. China
- Department of Orthopaedics and Neurosurgery, Keck Medical Center of USC, Los Angeles, CA, USA
| | - Hao Tang
- School of Computer Science and Technology, University of the Chinese Academy of Sciences, Beijing, P. R. China
| | - Andrew Mecum
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Mohamed Kamal Mesregah
- Department of Orthopaedics and Neurosurgery, Keck Medical Center of USC, Los Angeles, CA, USA
| | - Yuan Ma
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, You’anmenwai, Xitoutiao No.10, Beijing, P. R. China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, P. R. China
| | - Haibin Li
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, You’anmenwai, Xitoutiao No.10, Beijing, P. R. China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, P. R. China
| | - Feng Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, You’anmenwai, Xitoutiao No.10, Beijing, P. R. China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, P. R. China
| | - Zhiyuan Wu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, You’anmenwai, Xitoutiao No.10, Beijing, P. R. China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, P. R. China
| | - Erlin Yao
- School of Computer Science and Technology, University of the Chinese Academy of Sciences, Beijing, P. R. China
| | - Xiuhua Guo
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, You’anmenwai, Xitoutiao No.10, Beijing, P. R. China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, P. R. China
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