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Schmid C, Haziq M, Baese-Berk MM, Murray JM, Jaramillo S. Passive exposure to task-relevant stimuli enhances categorization learning. eLife 2024; 12:RP88406. [PMID: 38265440 PMCID: PMC10945695 DOI: 10.7554/elife.88406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2024] Open
Abstract
Learning to perform a perceptual decision task is generally achieved through sessions of effortful practice with feedback. Here, we investigated how passive exposure to task-relevant stimuli, which is relatively effortless and does not require feedback, influences active learning. First, we trained mice in a sound-categorization task with various schedules combining passive exposure and active training. Mice that received passive exposure exhibited faster learning, regardless of whether this exposure occurred entirely before active training or was interleaved between active sessions. We next trained neural-network models with different architectures and learning rules to perform the task. Networks that use the statistical properties of stimuli to enhance separability of the data via unsupervised learning during passive exposure provided the best account of the behavioral observations. We further found that, during interleaved schedules, there is an increased alignment between weight updates from passive exposure and active training, such that a few interleaved sessions can be as effective as schedules with long periods of passive exposure before active training, consistent with our behavioral observations. These results provide key insights for the design of efficient training schedules that combine active learning and passive exposure in both natural and artificial systems.
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Affiliation(s)
- Christian Schmid
- Institute of Neuroscience, University of OregonEugeneUnited States
| | - Muhammad Haziq
- Institute of Neuroscience, University of OregonEugeneUnited States
| | | | - James M Murray
- Institute of Neuroscience, University of OregonEugeneUnited States
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Khan S, Wong A, Tripp B. Modeling the Role of Contour Integration in Visual Inference. Neural Comput 2023; 36:33-74. [PMID: 38052088 DOI: 10.1162/neco_a_01625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 09/08/2023] [Indexed: 12/07/2023]
Abstract
Under difficult viewing conditions, the brain's visual system uses a variety of recurrent modulatory mechanisms to augment feedforward processing. One resulting phenomenon is contour integration, which occurs in the primary visual (V1) cortex and strengthens neural responses to edges if they belong to a larger smooth contour. Computational models have contributed to an understanding of the circuit mechanisms of contour integration, but less is known about its role in visual perception. To address this gap, we embedded a biologically grounded model of contour integration in a task-driven artificial neural network and trained it using a gradient-descent variant. We used this model to explore how brain-like contour integration may be optimized for high-level visual objectives as well as its potential roles in perception. When the model was trained to detect contours in a background of random edges, a task commonly used to examine contour integration in the brain, it closely mirrored the brain in terms of behavior, neural responses, and lateral connection patterns. When trained on natural images, the model enhanced weaker contours and distinguished whether two points lay on the same versus different contours. The model learned robust features that generalized well to out-of-training-distribution stimuli. Surprisingly, and in contrast with the synthetic task, a parameter-matched control network without recurrence performed the same as or better than the model on the natural-image tasks. Thus, a contour integration mechanism is not essential to perform these more naturalistic contour-related tasks. Finally, the best performance in all tasks was achieved by a modified contour integration model that did not distinguish between excitatory and inhibitory neurons.
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Affiliation(s)
- Salman Khan
- Centre for Theoretical Neuroscience, Department of System Design Engineering
- Vision and Image Processing Group, Department of System Design Engineering
- Waterloo Artificial Intelligence Institute: University of Waterloo, Waterloo, ON, Canada, N2L 3G1
| | - Alexander Wong
- Vision and Image Processing Group, Department of System Design Engineering
- Waterloo Artificial Intelligence Institute: University of Waterloo, Waterloo, ON, Canada, N2L 3G1
| | - Bryan Tripp
- Centre for Theoretical Neuroscience, Department of System Design Engineering
- Vision and Image Processing Group, Department of System Design Engineering
- Waterloo Artificial Intelligence Institute: University of Waterloo, Waterloo, ON, Canada, N2L 3G1
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Schmid C, Haziq M, Baese-Berk MM, Murray JM, Jaramillo S. Passive exposure to task-relevant stimuli enhances categorization learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.04.535463. [PMID: 37066276 PMCID: PMC10104059 DOI: 10.1101/2023.04.04.535463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/27/2023]
Abstract
Learning to perform a perceptual decision task is generally achieved through sessions of effortful practice with feedback. Here, we investigated how passive exposure to task-relevant stimuli, which is relatively effortless and does not require feedback, influences active learning. First, we trained mice in a sound-categorization task with various schedules combining passive exposure and active training. Mice that received passive exposure exhibited faster learning, regardless of whether this exposure occurred entirely before active training or was interleaved between active sessions. We next trained neural-network models with different architectures and learning rules to perform the task. Networks that use the statistical properties of stimuli to enhance separability of the data via unsupervised learning during passive exposure provided the best account of the behavioral observations. We further found that, during interleaved schedules, there is an increased alignment between weight updates from passive exposure and active training, such that a few interleaved sessions can be as effective as schedules with long periods of passive exposure before active training, consistent with our behavioral observations. These results provide key insights for the design of efficient training schedules that combine active learning and passive exposure in both natural and artificial systems.
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Affiliation(s)
- Christian Schmid
- Institute of Neuroscience, University of Oregon. Eugene, OR 97403
| | - Muhammad Haziq
- Institute of Neuroscience, University of Oregon. Eugene, OR 97403
| | | | - James M Murray
- Institute of Neuroscience, University of Oregon. Eugene, OR 97403
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Li Y, Wang T, Yang Y, Dai W, Wu Y, Li L, Han C, Zhong L, Li L, Wang G, Dou F, Xing D. Cascaded normalizations for spatial integration in the primary visual cortex of primates. Cell Rep 2022; 40:111221. [PMID: 35977486 DOI: 10.1016/j.celrep.2022.111221] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 04/19/2022] [Accepted: 07/25/2022] [Indexed: 11/03/2022] Open
Abstract
Spatial integration of visual information is an important function in the brain. However, neural computation for spatial integration in the visual cortex remains unclear. In this study, we recorded laminar responses in V1 of awake monkeys driven by visual stimuli with grating patches and annuli of different sizes. We find three important response properties related to spatial integration that are significantly different between input and output layers: neurons in output layers have stronger surround suppression, smaller receptive field (RF), and higher sensitivity to grating annuli partially covering their RFs. These interlaminar differences can be explained by a descriptive model composed of two global divisions (normalization) and a local subtraction. Our results suggest suppressions with cascaded normalizations (CNs) are essential for spatial integration and laminar processing in the visual cortex. Interestingly, the features of spatial integration in convolutional neural networks, especially in lower layers, are different from our findings in V1.
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Affiliation(s)
- Yang Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Tian Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; College of Life Sciences, Beijing Normal University, Beijing 100875, China
| | - Yi Yang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Weifeng Dai
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Yujie Wu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Lianfeng Li
- China Academy of Launch Vehicle Technology, Beijing 100076, China
| | - Chuanliang Han
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Lvyan Zhong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Liang Li
- Beijing Institute of Basic Medical Sciences, Beijing 100005, China
| | - Gang Wang
- Beijing Institute of Basic Medical Sciences, Beijing 100005, China
| | - Fei Dou
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; College of Life Sciences, Beijing Normal University, Beijing 100875, China
| | - Dajun Xing
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
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Voina D, Recanatesi S, Hu B, Shea-Brown E, Mihalas S. Single Circuit in V1 Capable of Switching Contexts during Movement Using an Inhibitory Population as a Switch. Neural Comput 2022; 34:541-594. [PMID: 35016220 DOI: 10.1162/neco_a_01472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 09/21/2021] [Indexed: 11/04/2022]
Abstract
As animals adapt to their environments, their brains are tasked with processing stimuli in different sensory contexts. Whether these computations are context dependent or independent, they are all implemented in the same neural tissue. A crucial question is what neural architectures can respond flexibly to a range of stimulus conditions and switch between them. This is a particular case of flexible architecture that permits multiple related computations within a single circuit. Here, we address this question in the specific case of the visual system circuitry, focusing on context integration, defined as the integration of feedforward and surround information across visual space. We show that a biologically inspired microcircuit with multiple inhibitory cell types can switch between visual processing of the static context and the moving context. In our model, the VIP population acts as the switch and modulates the visual circuit through a disinhibitory motif. Moreover, the VIP population is efficient, requiring only a relatively small number of neurons to switch contexts. This circuit eliminates noise in videos by using appropriate lateral connections for contextual spatiotemporal surround modulation, having superior denoising performance compared to circuits where only one context is learned. Our findings shed light on a minimally complex architecture that is capable of switching between two naturalistic contexts using few switching units.
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Affiliation(s)
- Doris Voina
- Applied Mathematics, University of Washington, Seattle, WA 98195 U.S.A.
| | - Stefano Recanatesi
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, U.S.A.
| | - Brian Hu
- Allen Institute for Brain Science, Seattle, WA 98109 U.S.A
| | - Eric Shea-Brown
- Applied Mathematics, University of Washington, Seattle, WA 98195, U.S.A., and Allen Institute for Brain Science, Seattle, WA 98109, U.S.A.
| | - Stefan Mihalas
- Applied Mathematics, University of Washington, Seattle, WA 98195, U.S.A., and Allen Institute for Brain Science, Seattle, WA 98109, U.S.A.
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