Person: Kreiman, Gabriel
Loading...
Email Address
AA Acceptance Date
Birth Date
Research Projects
Organizational Units
Job Title
Last Name
Kreiman
First Name
Gabriel
Name
Kreiman, Gabriel
47 results
Search Results
Now showing 1 - 10 of 47
Publication Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning(2016) Lotter, William; Kreiman, Gabriel; Cox, DavidWhile great strides have been made in using deep learning algorithms to solve supervised learning tasks, the problem of unsupervised learning - leveraging unlabeled examples to learn about the structure of a domain - remains a difficult unsolved challenge. Here, we explore prediction of future frames in a video sequence as an unsupervised learning rule for learning about the structure of the visual world. We describe a predictive neural network ("PredNet") architecture that is inspired by the concept of "predictive coding" from the neuroscience literature. These networks learn to predict future frames in a video sequence, with each layer in the network making local predictions and only forwarding deviations from those predictions to subsequent network layers. We show that these networks are able to robustly learn to predict the movement of synthetic (rendered) objects, and that in doing so, the networks learn internal representations that are useful for decoding latent object parameters (e.g. pose) that support object recognition with fewer training views. We also show that these networks can scale to complex natural image streams (car-mounted camera videos), capturing key aspects of both egocentric movement and the movement of objects in the visual scene, and the representation learned in this setting is useful for estimating the steering angle. Altogether, these results suggest that prediction represents a powerful framework for unsupervised learning, allowing for implicit learning of object and scene structure.Publication Temporal stability of visually selective responses in intracranial field potentials recorded from human occipital and temporal lobes(American Physiological Society, 2012) Bansal, A. K.; Singer, Jedediah; Anderson, W. S.; Golby, Alexandra; Madsen, Joseph; Kreiman, GabrielThe cerebral cortex needs to maintain information for long time periods while at the same time being capable of learning and adapting to changes. The degree of stability of physiological signals in the human brain in response to external stimuli over temporal scales spanning hours to days remains unclear. Here, we quantitatively assessed the stability across sessions of visually selective intracranial field potentials (IFPs) elicited by brief flashes of visual stimuli presented to 27 subjects. The interval between sessions ranged from hours to multiple days. We considered electrodes that showed robust visual selectivity to different shapes; these electrodes were typically located in the inferior occipital gyrus, the inferior temporal cortex, and the fusiform gyrus. We found that IFP responses showed a strong degree of stability across sessions. This stability was evident in averaged responses as well as single-trial decoding analyses, at the image exemplar level as well as at the category level, across different parts of visual cortex, and for three different visual recognition tasks. These results establish a quantitative evaluation of the degree of stationarity of visually selective IFP responses within and across sessions and provide a baseline for studies of cortical plasticity and for the development of brain-machine interfaces.Publication Robust Selectivity to Two-Object Images in Human Visual Cortex(Elsevier BV, 2010) Agam, Yigal; Liu, Hesheng; Papanastassiou, Alexander; Buia, Calin; Golby, Alexandra; Madsen, Joseph; Kreiman, GabrielWe can recognize objects in complex images in a fraction of a second. Neuronal responses in macaque areas V4 and inferior temporal cortex to preferred stimuli are typically suppressed by the addition of other objects within the receptive field (see, however, [16, 17]). How can this suppression be reconciled with rapid visual recognition in complex scenes? Certain "special categories" could be unaffected by other objects, but this leaves the problem unsolved for other categories. Another possibility is that serial attentional shifts help ameliorate the problem of distractor objects. Yet, psychophysical studies, scalp recordings, and neurophysiological recordings suggest that the initial sweep of visual processing contains a significant amount of information. We recorded intracranial field potentials in human visual cortex during presentation of flashes of two-object images. Visual selectivity from temporal cortex during the initial approximately 200 ms was largely robust to the presence of other objects. We could train linear decoders on the responses to isolated objects and decode information in two-object images. These observations are compatible with parallel, hierarchical, and feed-forward theories of rapid visual recognition and may provide a neural substrate to begin to unravel rapid recognition in natural scenes.Publication Robustness and Variability of Neuronal Coding by Amplitude Sensitive Afferents in the Weakly Electric Fish Eigenmannia(American Physiological Society, 2000) Kreiman, Gabriel; Krahe, Rüdiger; Metzner, Walter; Koch, Christof; Gabbiani, FabrizioWe investigated the variability of P-receptor afferent spike trains in the weakly electric fish, Eigenmannia, to repeated presentations of random electric field AMs (RAMs) and quantified its impact on the encoding of time-varying stimuli. A new measure of spike timing jitter was developed using the notion of spike train distances recently introduced by Victor and Purpura. This measure of variability is widely applicable to neuronal responses, irrespective of the type of stimuli used (deterministic vs. random) or the reliability of the recorded spike trains. In our data, the mean spike count and its variance measured in short time windows were poorly correlated with the reliability of P-receptor afferent spike trains, implying that such measures provide unreliable indices of trial-to-trial variability. P-receptor afferent spike trains were considerably less variable than those of Poisson model neurons. The average timing jitter of spikes lay within 1–2 cycles of the electric organ discharge (EOD). At low, but not at high firing rates, the timing jitter was dependent on the cutoff frequency of the stimulus and, to a lesser extent, on its contrast. When spikes were artificially manipulated to increase jitter, information conveyed by P-receptor afferents was degraded only for average jitters considerably larger than those observed experimentally. This suggests that the intrinsic variability of single spike trains lies outside of the range where it might degrade the information conveyed, yet still allows for improvement in coding by averaging across multiple afferent fibers. Our results were summarized in a phenomenological model of P-receptor afferents, incorporating both their linear transfer properties and the variability of their spike trains. This model complements an earlier one proposed by Nelson et al. for P-receptor afferents of Apteronotus. Because of their relatively high precision with respect to the EOD cycle frequency, P-receptor afferent spike trains possess the temporal resolution necessary to support coincidence detection operations at the next stage in the amplitude-coding pathway.Publication Amygdala-enriched genes identified by microarray technology are restricted to specific amygdaloid subnuclei(Proceedings of the National Academy of Sciences, 2001) Zirlinger, M.; Kreiman, Gabriel; Anderson, D. J.Microarray technology represents a potentially powerful method for identifying cell type- and regionally restricted genes expressed in the brain. Here we have combined a microarray analysis of differential gene expression among five selected brain regions, including the amygdala, cerebellum, hippocampus, olfactory bulb, and periaqueductal gray, with in situ hybridization. On average, 0.3% of the 34,000 genes interrogated were highly enriched in each of the five regions, relative to the others. In situ hybridization performed on a subset of amygdala-enriched genes confirmed in most cases the overall region-specificity predicted by the microarray data and identified additional sites of brain expression not examined on the microarrays. Strikingly, the majority of these genes exhibited boundaries of expression within the amygdala corresponding to cytoarchitectonically defined subnuclei. These results define a unique set of molecular markers for amygdaloid subnuclei and provide tools to genetically dissect their functional roles in different emotional behaviors.Publication Neuroscience: Literary inspiration(Nature Publishing Group, 2011) Kreiman, GabrielPublication f-divergence cutoff index to simultaneously identify differential expression in the integrated transcriptome and proteome(Oxford University Press (OUP), 2016) Tang, Shaojun; Hemberg, Martin; Cansizoglu, Ertugrul; Belin, Stephane; Kosik, Kenneth; Kreiman, Gabriel; Steen, Hanno; Steen, JudithThe ability to integrate 'omics' (i.e., transcriptomics and proteomics) is becoming increasingly important to the understanding of regulatory mechanisms. There are currently no tools available to identify differentially expressed genes (DEGs)across different 'omics'data types or multi-dimensional data including time courses. We present a model capable of simultaneously identifying DEGs from continuous and discrete transcriptomic, proteomic and integrated proteogenomic data. We show that our algorithm can be used across multiple diverse sets of data and can unambiguously find genes that show functional modulation, developmental changes or misregulation. Applying our model to several proteogenomics datasets, we identified a number of important genes that showed distinctive regulation patterns. The package is available at R Bioconductor and also at http://software.steenlab.org/fCI/.Publication Decrease in gamma-band activity tracks sequence learning(Frontiers Media S.A., 2015) Madhavan, Radhika; Millman, Daniel; Tang, Hanlin; Crone, Nathan E.; Lenz, Fredrick A.; Tierney, Travis S.; Madsen, Joseph; Kreiman, Gabriel; Anderson, William S.Learning novel sequences constitutes an example of declarative memory formation, involving conscious recall of temporal events. Performance in sequence learning tasks improves with repetition and involves forming temporal associations over scales of seconds to minutes. To further understand the neural circuits underlying declarative sequence learning over trials, we tracked changes in intracranial field potentials (IFPs) recorded from 1142 electrodes implanted throughout temporal and frontal cortical areas in 14 human subjects, while they learned the temporal-order of multiple sequences of images over trials through repeated recall. We observed an increase in power in the gamma frequency band (30–100 Hz) in the recall phase, particularly in areas within the temporal lobe including the parahippocampal gyrus. The degree of this gamma power enhancement decreased over trials with improved sequence recall. Modulation of gamma power was directly correlated with the improvement in recall performance. When presenting new sequences, gamma power was reset to high values and decreased again after learning. These observations suggest that signals in the gamma frequency band may play a more prominent role during the early steps of the learning process rather than during the maintenance of memory traces.Publication How cortical neurons help us see: visual recognition in the human brain(American Society for Clinical Investigation, 2010) Blumberg, Julie; Kreiman, GabrielThrough a series of complex transformations, the pixel-like input to the retina is converted into rich visual perceptions that constitute an integral part of visual recognition. Multiple visual problems arise due to damage or developmental abnormalities in the cortex of the brain. Here, we provide an overview of how visual information is processed along the ventral visual cortex in the human brain. We discuss how neurophysiological recordings in macaque monkeys and in humans can help us understand the computations performed by visual cortex.Publication From Neurons to Circuits: Linear Estimation of Local Field Potentials(Society for Neuroscience, 2009) Rasch, M.; Logothetis, N. K.; Kreiman, GabrielExtracellular physiological recordings are typically separated into two frequency bands: local field potentials (LFPs) (a circuit property) and spiking multiunit activity (MUA). Recently, there has been increased interest in LFPs because of their correlation with functional magnetic resonance imaging blood oxygenation level-dependent measurements and the possibility of studying local processing and neuronal synchrony. To further understand the biophysical origin of LFPs, we asked whether it is possible to estimate their time course based on the spiking activity from the same electrode or nearby electrodes. We used “signal estimation theory” to show that a linear filter operation onthe activity of one or a few neurons can explain a significant fraction ofthe LFPtime course inthe macaque monkey primary visual cortex. The linear filter used to estimate the LFPs had a stereotypical shape characterized by a sharp downstroke at negative time lags and a slower positive upstroke for positive time lags. The filter was similar across different neocortical regions and behavioral conditions, including spontaneous activity and visual stimulation. The estimations had a spatial resolution of 1 mm and a temporal resolution of 200 ms. By considering a causal filter, we observed a temporal asymmetry such that the positive time lags in the filter contributed more to the LFP estimation than the negative time lags. Additionally, we showed that spikes occurring within 10 ms of spikes from nearby neurons yielded better estimation accuracies than nonsynchronous spikes. In summary, our results suggest that at least some circuit-level local properties of the field potentials can be predicted from the activity of one or a few neurons.