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James V Haxby - One of the best experts on this subject based on the ideXlab platform.

  • partially Distributed Representations of objects and faces in ventral temporal cortex
    Journal of Cognitive Neuroscience, 2005
    Co-Authors: Alice J Otoole, Fang Jiang, Herve Abdi, James V Haxby
    Abstract:

    Object and face Representations in ventral temporal (VT) cortex were investigated by combining object confusability data from a computational model of object classification with neural response confusability data from a functional neuroimaging experiment. A pattern-based classification algorithm learned to categorize individual brain maps according to the object category being viewed by the subject. An identical algorithm learned to classify an image-based, view-dependent represen- tation of the stimuli. High correlations were found between the confusability of object categories and the confusability of brain activity maps. This occurred even with the inclusion of multiple views of objects, and when the object classification model was tested with high spatial frequency "line drawings" of the stimuli. Consistent with a Distributed Representation of objects in VT cortex, the data indicate that object categories with shared image-based attributes have shared neural structure.

  • The Representation of Objects in the Human Occipital and Temporal Cortex
    Journal of Cognitive Neuroscience, 2000
    Co-Authors: Alumit Ishai, Alex Martin, Leslie G Ungerleider, James V Haxby
    Abstract:

    Recently, we identified, using fMRI, three bilateral regions in the ventral temporal cortex that responded preferentially to faces, houses, and chairs [Ishai, A., Ungerleider, L. G., Martin, A., Schouten, J. L., & Haxby, J. Y. (1999). Distributed Representation of objects in the human ventral visual pathway. Proceedings of the National Academy of Sciences, U.S.A., 96, 9379-9384]. Here, we report differential patterns of activation, similar to those seen in the ventral temporal cortex, in bilateral regions of the ventral occipital cortex. We also found category-related responses in the dorsal occipital cortex and in the superior temporal sulcus. Moreover, rather than activating discrete, segregated areas, each category was associated with its own differential pattern of response across a broad expanse of cortex. The Distributed patterns of response were similar across tasks (passive viewing, delayed matching) and presentation formats (photographs, line drawings). We propose that the Representation of objects in the ventral visual pathway, including both occipital and temporal regions, is not restricted to small, highly selective patches of cortex but, instead, is a Distributed Representation of information about object form. Within this Distributed system, the Representation of faces appears to be less extensive as compared to the Representations of nonface objects.

  • Distributed Representation of objects in the human ventral visual pathway
    Proceedings of the National Academy of Sciences of the United States of America, 1999
    Co-Authors: Alumit Ishai, Jennifer L Schouten, Alex Martin, Leslie G Ungerleider, James V Haxby
    Abstract:

    Brain imaging and electrophysiological recording studies in humans have reported discrete cortical regions in posterior ventral temporal cortex that respond preferentially to faces, buildings, and letters. These findings suggest a category-specific anatomically segregated modular organization of the object vision pathway. Here we present data from a functional MRI study in which we found three distinct regions of ventral temporal cortex that responded preferentially to faces and two categories of other objects, namely houses and chairs, and had a highly consistent topological arrangement. Although the data could be interpreted as evidence for separate modules, we found that each category also evoked significant responses in the regions that responded maximally to other stimuli. Moreover, each category was associated with its own differential pattern of response across ventral temporal cortex. These results indicate that the Representation of an object is not restricted to a region that responds maximally to that object, but rather is Distributed across a broader expanse of cortex. We propose that the functional architecture of the ventral visual pathway is not a mosaic of category-specific modules but instead is a continuous Representation of information about object form that has a highly consistent and orderly topological arrangement.

Alfredo Fontanini - One of the best experts on this subject based on the ideXlab platform.

  • spatially Distributed Representation of taste quality in the gustatory insular cortex of behaving mice
    Current Biology, 2021
    Co-Authors: Ke Chen, Joshua F Kogan, Alfredo Fontanini
    Abstract:

    Summary Visual, auditory, and somatosensory cortices are topographically organized, with neurons responding to similar sensory features clustering in adjacent portions of the cortex. Such topography has not been observed in the piriform cortex, whose responses to odorants are sparsely Distributed across the cortex. The spatial organization of taste responses in the gustatory insular cortex (GC) is currently debated, with conflicting evidence from anesthetized rodents pointing to alternative and mutually exclusive models. Here, we rely on calcium imaging to determine how taste and task-related variables are represented in the superficial layers of GC of alert, licking mice. Our data show that the various stimuli evoke sparse responses from a combination of broadly and narrowly tuned neurons. Analysis of the distribution of responses over multiple spatial scales demonstrates that taste Representations are Distributed across the cortex, with no sign of spatial clustering or topography. Altogether, data presented here support the idea that the Representation of taste qualities in GC of alert mice is sparse and Distributed, analogous to the Representation of odorants in piriform cortex.

  • spatially Distributed Representation of taste quality in the gustatory insular cortex of awake behaving mice
    bioRxiv, 2020
    Co-Authors: Ke Chen, Joshua F Kogan, Alfredo Fontanini
    Abstract:

    Visual, auditory and somatosensory cortices are topographically organized, with neurons responding to similar sensory features clustering in adjacent portions of the cortex. Such topography has not been observed in the piriform cortex, whose responses to odorants are sparsely Distributed across the cortex. The spatial organization of taste responses in the gustatory insular cortex (GC) is currently debated, with conflicting evidence from anesthetized rodents pointing to alternative and mutually exclusive models. Here, we rely on calcium imaging to determine how taste and task-related variables are represented in the superficial layers of GC of alert, licking mice. Our data show that the various stimuli evoke sparse responses from a combination of broadly and narrowly tuned neurons. Analysis of the distribution of responses over multiple spatial scales demonstrates that taste Representations are Distributed across the cortex, with no sign of spatial clustering or topography. Altogether, data presented here support the idea that the Representation of taste qualities in GC of alert mice is sparse and Distributed, analogous to the Representation of odorants in piriform cortex.

Michele Migliore - One of the best experts on this subject based on the ideXlab platform.

  • sparse Distributed Representation of odors in a large scale olfactory bulb circuit
    PLOS Computational Biology, 2013
    Co-Authors: Thomas S Mctavish, Michael L Hines, Gordon M Shepherd, Cesare Valenti, Michele Migliore
    Abstract:

    In the olfactory bulb, lateral inhibition mediated by granule cells has been suggested to modulate the timing of mitral cell firing, thereby shaping the Representation of input odorants. Current experimental techniques, however, do not enable a clear study of how the mitral-granule cell network sculpts odor inputs to represent odor information spatially and temporally. To address this critical step in the neural basis of odor recognition, we built a biophysical network model of mitral and granule cells, corresponding to 1/100th of the real system in the rat, and used direct experimental imaging data of glomeruli activated by various odors. The model allows the systematic investigation and generation of testable hypotheses of the functional mechanisms underlying odor Representation in the olfactory bulb circuit. Specifically, we demonstrate that lateral inhibition emerges within the olfactory bulb network through recurrent dendrodendritic synapses when constrained by a range of balanced excitatory and inhibitory conductances. We find that the spatio-temporal dynamics of lateral inhibition plays a critical role in building the glomerular-related cell clusters observed in experiments, through the modulation of synaptic weights during odor training. Lateral inhibition also mediates the development of sparse and synchronized spiking patterns of mitral cells related to odor inputs within the network, with the frequency of these synchronized spiking patterns also modulated by the sniff cycle.

Degen Huang - One of the best experts on this subject based on the ideXlab platform.

  • an approach to improve kernel based protein protein interaction extraction by learning from large scale network data
    Methods, 2015
    Co-Authors: Rui Guo, Zhenchao Jiang, Degen Huang
    Abstract:

    Abstract Protein–Protein Interaction extraction (PPIe) from biomedical literatures is an important task in biomedical text mining and has achieved desirable results on the annotated datasets. However, the traditional machine learning methods on PPIe suffer badly from vocabulary gap and data sparseness, which weakens classification performance. In this work, an approach capturing external information from the web-based data is introduced to address these problems and boost the existing methods. The approach involves three kinds of word Representation techniques: Distributed Representation, vector clustering and Brown clusters. Experimental results show that our method outperforms the state-of-the-art methods on five publicly available corpora. Our code and data are available at: http://chaoslog.com/improving-kernel-based-protein-protein-interaction-extraction-by-unsupervised-word-Representation-codes-and-data.html .

  • Improving Kernel-based protein-protein interaction extraction by unsupervised word Representation
    2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2014
    Co-Authors: Lishuang Li, Zhenchao Jiang, Degen Huang
    Abstract:

    As an important branch of biomedical information extraction, Protein-Protein Interaction extraction (PPIe) from biomedical literatures has been widely researched, and machine learning methods have achieved great success for this task. However, the word feature generally adopted in the existing methods suffers badly from vocabulary gap and data sparseness, weakening the classification performance. In this paper, the unsupervised word Representation approach is introduced to address these problems. Three word Representation methods are adopted to improve the performance of PPIe: Distributed Representation, vector clustering and Brown clusters Representation. Experimental results show that our method outperforms the state-of-the-art methods on five publicly available corpora.

Yoshua Bengio - One of the best experts on this subject based on the ideXlab platform.

  • Distributed Representation prediction for generalization to new words
    iroumontrealca, 2006
    Co-Authors: Hugo Larochelle, Yoshua Bengio
    Abstract:

    Learning Distributed Representations of symbols (e.g. words) has been used in several Natural Language Processing systems. Such Representations can capture semantic or syntactic similarities between words, which permit to fight the curse of dimensionality when considering sequences of such words. Unfortunately, because these representa- tions are learned only for a previously determined vocabulary of words, it is not clear how to obtain Representations for new words. We present here an approach which gets around this problem by considering the Distributed repre- sentations as predictions from low-level or domain-knowledge features of words. We report experiments on a Part Of Speech tagging task, which demonstrates the success of this approach in learning meaningful Representations and in providing improved accuracy, especially for new words.

  • A Neural Probabilistic Language Model
    Journal of Machine Learning Research, 2003
    Co-Authors: Yoshua Bengio, Réjean Ducharme, Pascal Vincent, Christian Jauvin
    Abstract:

    A goal of statistical language modeling is to learn the joint probability function of sequences of words in a language. This is intrinsically difficult because of the curse of dimensionality: a word sequence on which the model will be tested is likely to be different from all the word sequences seen during training. Traditional but very successful approaches based on n-grams obtain generalization by concatenating very short overlapping sequences seen in the training set. We propose to fight the curse of dimensionality by learning a Distributed Representation for words which allows each training sentence to inform the model about an exponential number of semantically neighboring sentences. The model learns simultaneously (1) a Distributed Representation for each word along with (2) the probability function for word sequences, expressed in terms of these Representations. Generalization is obtained because a sequence of words that has never been seen before gets high probability if it is made of words that are similar (in the sense of having a nearby Representation) to words forming an already seen sentence. Training such large models (with millions of parameters) within a reasonable time is itself a significant challenge. We report on experiments using neural networks for the probability function, showing on two text corpora that the proposed approach significantly improves on state-of-the-art n-gram models, and that the proposed approach allows to take advantage of longer contexts.

  • a neural probabilistic language model
    Neural Information Processing Systems, 2000
    Co-Authors: Yoshua Bengio, Réjean Ducharme, Pascal Vincent
    Abstract:

    A goal of statistical language modeling is to learn the joint probability function of sequences of words. This is intrinsically difficult because of the curse of dimensionality: we propose to fight it with its own weapons. In the proposed approach one learns simultaneously (1) a Distributed Representation for each word (i.e. a similarity between words) along with (2) the probability function for word sequences, expressed with these Representations. Generalization is obtained because a sequence of words that has never been seen before gets high probability if it is made of words that are similar to words forming an already seen sentence. We report on experiments using neural networks for the probability function, showing on two text corpora that the proposed approach very significantly improves on a state-of-the-art trigram model.