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Mukherjee A, Paul A, Roy R, Ghosh K. The role of extrinsic and intrinsic factors in perceptual filling-in of the blind-spot with variegated color and texture stimuli. Vision Res 2024; 222:108452. [PMID: 38968753 DOI: 10.1016/j.visres.2024.108452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 06/18/2024] [Accepted: 06/25/2024] [Indexed: 07/07/2024]
Abstract
Vision scientists dedicated their efforts to unraveling the mechanism of filling-in at the blind-spot (BS) through numerous psychophysical experiments. The prevalent interpretation, emphasizing active filling-in, has spurred extensive research endeavors. In a parallel vein, a pertinent study highlighted the predominance of the nasal Visual Field (VF) over the temporal one and postulated the role of the Cortical Magnification Factor (CMF) in explaining the asymmetry of filling-in. In this study, we first replicated this experiment and then conducted BS-specific psychophysical experiments employing various bi-colored and bi-textured (patterned) stimuli. We observed that nasal dominance is not persistent in the context of the spread of perception for BS filling-in. We posit that the visual information processing priority index (VIPPI), comprising the CMF (an intrinsic factor unaffected by stimulus characteristics) and relative luminance (an extrinsic factor dependent on stimulus characteristics), governs the spread of perception for filling-in in case of diverse neighborhoods of the BS.
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Affiliation(s)
- Amrita Mukherjee
- Indian Institute of Information Technology Allahabad, India; Indian Statistical Institute, Kolkata, India
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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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Kirchner JW. Impulse Response Functions for Nonlinear, Nonstationary, and Heterogeneous Systems, Estimated by Deconvolution and Demixing of Noisy Time Series. SENSORS 2022; 22:s22093291. [PMID: 35590982 PMCID: PMC9105515 DOI: 10.3390/s22093291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/10/2022] [Accepted: 04/22/2022] [Indexed: 11/16/2022]
Abstract
Impulse response functions (IRFs) are useful for characterizing systems’ dynamic behavior and gaining insight into their underlying processes, based on sensor data streams of their inputs and outputs. However, current IRF estimation methods typically require restrictive assumptions that are rarely met in practice, including that the underlying system is homogeneous, linear, and stationary, and that any noise is well behaved. Here, I present data-driven, model-independent, nonparametric IRF estimation methods that relax these assumptions, and thus expand the applicability of IRFs in real-world systems. These methods can accurately and efficiently deconvolve IRFs from signals that are substantially contaminated by autoregressive moving average (ARMA) noise or nonstationary ARIMA noise. They can also simultaneously deconvolve and demix the impulse responses of individual components of heterogeneous systems, based on their combined output (without needing to know the outputs of the individual components). This deconvolution–demixing approach can be extended to characterize nonstationary coupling between inputs and outputs, even if the system’s impulse response changes so rapidly that different impulse responses overlap one another. These techniques can also be extended to estimate IRFs for nonlinear systems in which different input intensities yield impulse responses with different shapes and amplitudes, which are then overprinted on one another in the output. I further show how one can efficiently quantify multiscale impulse responses using piecewise linear IRFs defined at unevenly spaced lags. All of these methods are implemented in an R script that can efficiently estimate IRFs over hundreds of lags, from noisy time series of thousands or even millions of time steps.
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Affiliation(s)
- James W. Kirchner
- Department of Environmental Systems Science, ETH Zurich, CH-8092 Zürich, Switzerland;
- Swiss Federal Research Institute WSL, CH-8903 Birmensdorf, Switzerland
- Department of Earth and Planetary Science, University of California, Berkeley, CA 94720-4767, USA
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Corticothalamic feedback sculpts visual spatial integration in mouse thalamus. Nat Neurosci 2021; 24:1711-1720. [PMID: 34764474 DOI: 10.1038/s41593-021-00943-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 09/15/2021] [Indexed: 11/08/2022]
Abstract
En route from the retina to the cortex, visual information passes through the dorsolateral geniculate nucleus (dLGN) of the thalamus, where extensive corticothalamic (CT) feedback has been suggested to modulate spatial processing. How this modulation arises from direct excitatory and indirect inhibitory CT feedback pathways remains enigmatic. Here, we show that in awake mice, retinotopically organized cortical feedback sharpens receptive fields (RFs) and increases surround suppression in the dLGN. Guided by a network model indicating that widespread inhibitory CT feedback is necessary to reproduce these effects, we targeted the visual sector of the thalamic reticular nucleus (visTRN) for recordings. We found that visTRN neurons have large RFs, show little surround suppression and exhibit strong feedback-dependent responses to large stimuli. These features make them an ideal candidate for mediating feedback-enhanced surround suppression in the dLGN. We conclude that cortical feedback sculpts spatial integration in the dLGN, likely via recruitment of neurons in the visTRN.
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Liu X, Li H, Wang Y, Lei T, Wang J, Spillmann L, Andolina IM, Wang W. From Receptive to Perceptive Fields: Size-Dependent Asymmetries in Both Negative Afterimages and Subcortical On and Off Post-Stimulus Responses. J Neurosci 2021; 41:7813-7830. [PMID: 34326144 PMCID: PMC8445057 DOI: 10.1523/jneurosci.0300-21.2021] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 07/11/2021] [Accepted: 07/13/2021] [Indexed: 11/21/2022] Open
Abstract
Negative afterimages are perceptual phenomena that occur after physical stimuli disappear from sight. Their origin is linked to transient post-stimulus responses of visual neurons. The receptive fields (RFs) of these subcortical ON- and OFF-center neurons exhibit antagonistic interactions between central and surrounding visual space, resulting in selectivity for stimulus polarity and size. These two features are closely intertwined, yet their relationship to negative afterimage perception remains unknown. Here we tested whether size differentially affects the perception of bright and dark negative afterimages in humans of both sexes, and how this correlates with neural mechanisms in subcortical ON and OFF cells. Psychophysically, we found a size-dependent asymmetry whereby dark disks produce stronger and longer-lasting negative afterimages than bright disks of equal contrast at sizes >0.8°. Neurophysiological recordings from retinal and relay cells in female cat dorsal lateral geniculate nucleus showed that subcortical ON cells exhibited stronger sustained post-stimulus responses to dark disks, than OFF cells to bright disks, at sizes >1°. These sizes agree with the emergence of center-surround antagonism, revealing stronger suppression to opposite-polarity stimuli for OFF versus ON cells, particularly in dorsal lateral geniculate nucleus. Using a network-based retino-geniculate model, we confirmed stronger antagonism and temporal transience for OFF-cell post-stimulus rebound responses. A V1 population model demonstrated that both strength and duration asymmetries can be propagated to downstream cortical areas. Our results demonstrate how size-dependent antagonism impacts both the neuronal post-stimulus response and the resulting afterimage percepts, thereby supporting the idea of perceptual RFs reflecting the underlying neuronal RF organization of single cells.SIGNIFICANCE STATEMENT Visual illusions occur when sensory inputs and perceptual outcomes do not match, and provide a valuable tool to understand transformations from neural to perceptual responses. A classic example are negative afterimages that remain visible after a stimulus is removed from view. Such perceptions are linked to responses in early visual neurons, yet the details remain poorly understood. Combining human psychophysics, neurophysiological recordings in cats and retino-thalamo-cortical computational modeling, our study reveals how stimulus size and the receptive-field structure of subcortical ON and OFF cells contributes to the parallel asymmetries between neural and perceptual responses to bright versus dark afterimages. Thus, this work provides a deeper link from the underlying neural mechanisms to the resultant perceptual outcomes.
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Affiliation(s)
- Xu Liu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Hui Li
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Ye Wang
- State Key Laboratory of Media Convergence and Communication, Neuroscience and Intelligent Media Institute, Communication University of China, Beijing, 100024, China
| | - Tianhao Lei
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Jijun Wang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, 200030, China
| | - Lothar Spillmann
- Department of Neurology, University of Freiburg, Freiburg, 79085, Germany
| | - Ian Max Andolina
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai, 200031, China
- Shanghai Center for Brain and Brain-inspired Intelligence Technology, Shanghai, 200031, China
| | - Wei Wang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai, 200031, China
- Shanghai Center for Brain and Brain-inspired Intelligence Technology, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
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