The Experts below are selected from a list of 8250 Experts worldwide ranked by ideXlab platform
Uri Hasson - One of the best experts on this subject based on the ideXlab platform.
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cross modal and non monotonic representations of Statistical Regularity are encoded in local neural response patterns
NeuroImage, 2018Co-Authors: Samuel A Nastase, Ben Davis, Uri HassonAbstract:Abstract Current neurobiological models assign a central role to predictive processes calibrated to environmental statistics. Neuroimaging studies examining the encoding of stimulus uncertainty have relied almost exclusively on manipulations in which stimuli were presented in a single sensory modality, and further assumed that neural responses vary monotonically with uncertainty. This has left a gap in theoretical development with respect to two core issues: (i) are there cross-modal brain systems that encode input uncertainty in way that generalizes across sensory modalities, and (ii) are there brain systems that track input uncertainty in a non-monotonic fashion? We used multivariate pattern analysis to address these two issues using auditory, visual and audiovisual inputs. We found signatures of cross-modal encoding in frontoparietal, orbitofrontal, and association cortices using a searchlight cross-classification analysis where classifiers trained to discriminate levels of uncertainty in one modality were tested in another modality. Additionally, we found widespread systems encoding uncertainty non-monotonically using classifiers trained to discriminate intermediate levels of uncertainty from both the highest and lowest uncertainty levels. These findings comprise the first comprehensive report of cross-modal and non-monotonic neural sensitivity to Statistical regularities in the environment, and suggest that conventional paradigms testing for monotonic responses to uncertainty in a single sensory modality may have limited generalizability.
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cross modal and non monotonic representations of Statistical Regularity are encoded in local neural response patterns
bioRxiv, 2018Co-Authors: Samuel A Nastase, Ben Davis, Uri HassonAbstract:Current models of brain function assign a central role to predictive processes calibrated to the structure of the environment. Although several neuroimaging studies have examined how the human brain encodes the uncertainty of incoming stimuli, most have relied exclusively on experimental manipulations of uncertainty in which stimuli were presented in a single sensory modality, and further assumed that neural responses vary monotonically with uncertainty. This has left a gap in theoretical development with respect to two core issues: i) are there cross-modal brain systems that encode input uncertainty in way that generalizes across sensory modalities, and ii) are there brain systems that track input uncertainty in a non-monotonic fashion? Here we directly addressed the issues of cross-modal and non-monotonic processing by quantifying neural sensitivity to uncertainty in auditory, visual and audiovisual inputs using multivariate pattern analysis. We found signatures of cross-modal encoding in frontoparietal, orbitofrontal, and association cortices using a searchlight cross-classification analysis where classifiers trained to discriminate levels of uncertainty in one modality were tested in another modality. Additionally, we found widespread systems encoding uncertainty non-monotonically using classifiers trained to discriminate intermediate levels of uncertainty from both the highest and lowest uncertainty levels. These findings comprise the first comprehensive report of cross-modal and non-monotonic neural sensitivity to Statistical regularities in the environment, and suggest that conventional paradigms testing for monotonic responses to uncertainty in a single sensory modality may have limited generalizability.
Michael M Halassa - One of the best experts on this subject based on the ideXlab platform.
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thalamic regulation of switching between cortical representations enables cognitive flexibility
Nature Neuroscience, 2018Co-Authors: Rajeev V Rikhye, Aditya Gilra, Michael M HalassaAbstract:Interactions between the prefrontal cortex (PFC) and mediodorsal thalamus are critical for cognitive flexibility, yet the underlying computations are unknown. To investigate frontothalamic substrates of cognitive flexibility, we developed a behavioral task in which mice switched between different sets of learned cues that guided attention toward either visual or auditory targets. We found that PFC responses reflected both the individual cues and their meaning as task rules, indicating a hierarchical cue-to-rule transformation. Conversely, mediodorsal thalamus responses reflected the Statistical Regularity of cue presentation and were required for switching between such experimentally specified cueing contexts. A subset of these thalamic responses sustained context-relevant PFC representations, while another suppressed the context-irrelevant ones. Through modeling and experimental validation, we find that thalamic-mediated suppression may not only reduce PFC representational interference but could also preserve unused cortical traces for future use. Overall, our study provides a computational foundation for thalamic engagement in cognitive flexibility. Rikhye et al. recorded prefrontal and thalamic populations from mice performing attention selection across different contexts. By encoding context, the thalamus both enhances and suppresses prefrontal representations in a context-appropriate manner.
Samuel A Nastase - One of the best experts on this subject based on the ideXlab platform.
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cross modal and non monotonic representations of Statistical Regularity are encoded in local neural response patterns
NeuroImage, 2018Co-Authors: Samuel A Nastase, Ben Davis, Uri HassonAbstract:Abstract Current neurobiological models assign a central role to predictive processes calibrated to environmental statistics. Neuroimaging studies examining the encoding of stimulus uncertainty have relied almost exclusively on manipulations in which stimuli were presented in a single sensory modality, and further assumed that neural responses vary monotonically with uncertainty. This has left a gap in theoretical development with respect to two core issues: (i) are there cross-modal brain systems that encode input uncertainty in way that generalizes across sensory modalities, and (ii) are there brain systems that track input uncertainty in a non-monotonic fashion? We used multivariate pattern analysis to address these two issues using auditory, visual and audiovisual inputs. We found signatures of cross-modal encoding in frontoparietal, orbitofrontal, and association cortices using a searchlight cross-classification analysis where classifiers trained to discriminate levels of uncertainty in one modality were tested in another modality. Additionally, we found widespread systems encoding uncertainty non-monotonically using classifiers trained to discriminate intermediate levels of uncertainty from both the highest and lowest uncertainty levels. These findings comprise the first comprehensive report of cross-modal and non-monotonic neural sensitivity to Statistical regularities in the environment, and suggest that conventional paradigms testing for monotonic responses to uncertainty in a single sensory modality may have limited generalizability.
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cross modal and non monotonic representations of Statistical Regularity are encoded in local neural response patterns
bioRxiv, 2018Co-Authors: Samuel A Nastase, Ben Davis, Uri HassonAbstract:Current models of brain function assign a central role to predictive processes calibrated to the structure of the environment. Although several neuroimaging studies have examined how the human brain encodes the uncertainty of incoming stimuli, most have relied exclusively on experimental manipulations of uncertainty in which stimuli were presented in a single sensory modality, and further assumed that neural responses vary monotonically with uncertainty. This has left a gap in theoretical development with respect to two core issues: i) are there cross-modal brain systems that encode input uncertainty in way that generalizes across sensory modalities, and ii) are there brain systems that track input uncertainty in a non-monotonic fashion? Here we directly addressed the issues of cross-modal and non-monotonic processing by quantifying neural sensitivity to uncertainty in auditory, visual and audiovisual inputs using multivariate pattern analysis. We found signatures of cross-modal encoding in frontoparietal, orbitofrontal, and association cortices using a searchlight cross-classification analysis where classifiers trained to discriminate levels of uncertainty in one modality were tested in another modality. Additionally, we found widespread systems encoding uncertainty non-monotonically using classifiers trained to discriminate intermediate levels of uncertainty from both the highest and lowest uncertainty levels. These findings comprise the first comprehensive report of cross-modal and non-monotonic neural sensitivity to Statistical regularities in the environment, and suggest that conventional paradigms testing for monotonic responses to uncertainty in a single sensory modality may have limited generalizability.
Baihan Lin - One of the best experts on this subject based on the ideXlab platform.
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neural networks as model selection with incremental mdl normalization
International Joint Conference on Artificial Intelligence, 2019Co-Authors: Baihan LinAbstract:If we consider the neural network optimization process as a model selection problem, the implicit space can be constrained by the normalizing factor, the minimum description length of the optimal universal code. Inspired by the adaptation phenomenon of biological neuronal firing, we propose a class of reparameterization of the activation in the neural network that take into account the Statistical Regularity in the implicit space under the Minimum Description Length (MDL) principle. We introduce an incremental version of computing this universal code as normalized maximum likelihood and demonstrated its flexibility to include data prior such as top-down attention and other oracle information and its compatibility to be incorporated into batch normalization and layer normalization. The empirical results showed that the proposed method outperforms existing normalization methods in tackling the limited and imbalanced data from a non-stationary distribution benchmarked on computer vision and reinforcement learning tasks. As an unsupervised attention mechanism given input data, this biologically plausible normalization has the potential to deal with other complicated real-world scenarios as well as reinforcement learning setting where the rewards are sparse and non-uniform. Further research is proposed to discover these scenarios and explore the behaviors among different variants.
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Constraining Implicit Space with Minimum Description Length: An Unsupervised Attention Mechanism across Neural Network Layers
2019Co-Authors: Baihan LinAbstract:Inspired by the adaptation phenomenon of neuronal firing, we propose the Regularity normalization (RN) as an unsupervised attention mechanism (UAM) which computes the Statistical Regularity in the implicit space of neural networks under the Minimum Description Length (MDL) principle. Treating the neural network optimization process as a partially observable model selection problem, UAM constrains the implicit space by a normalization factor, the universal code length. We compute this universal code incrementally across neural network layers and demonstrated the flexibility to include data priors such as top-down attention and other oracle information. Empirically, our approach outperforms existing normalization methods in tackling limited, imbalanced and non-stationary input distribution in image classification, classic control, procedurally-generated reinforcement learning, generative modeling, handwriting generation and question answering tasks with various neural network architectures. Lastly, UAM tracks dependency and critical learning stages across layers and recurrent time steps of deep networks.
Rajeev V Rikhye - One of the best experts on this subject based on the ideXlab platform.
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thalamic regulation of switching between cortical representations enables cognitive flexibility
Nature Neuroscience, 2018Co-Authors: Rajeev V Rikhye, Aditya Gilra, Michael M HalassaAbstract:Interactions between the prefrontal cortex (PFC) and mediodorsal thalamus are critical for cognitive flexibility, yet the underlying computations are unknown. To investigate frontothalamic substrates of cognitive flexibility, we developed a behavioral task in which mice switched between different sets of learned cues that guided attention toward either visual or auditory targets. We found that PFC responses reflected both the individual cues and their meaning as task rules, indicating a hierarchical cue-to-rule transformation. Conversely, mediodorsal thalamus responses reflected the Statistical Regularity of cue presentation and were required for switching between such experimentally specified cueing contexts. A subset of these thalamic responses sustained context-relevant PFC representations, while another suppressed the context-irrelevant ones. Through modeling and experimental validation, we find that thalamic-mediated suppression may not only reduce PFC representational interference but could also preserve unused cortical traces for future use. Overall, our study provides a computational foundation for thalamic engagement in cognitive flexibility. Rikhye et al. recorded prefrontal and thalamic populations from mice performing attention selection across different contexts. By encoding context, the thalamus both enhances and suppresses prefrontal representations in a context-appropriate manner.