• A Hierarchical Probabilistic Model for Rapid Object Categorization in Natural Scenes

      He, Xiaofu; Yang, Zhiyong; Tsien, Joe Z.; Brain & Behavior Discovery Institute; Department of Ophthalmology; Department of Neurology (2011-05-25)
      Humans can categorize objects in complex natural scenes within 100â 150 ms. This amazing ability of rapid categorization has motivated many computational models. Most of these models require extensive training to obtain a decision boundary in a very high dimensional (e.g., â ¼6,000 in a leading model) feature space and often categorize objects in natural scenes by categorizing the context that co-occurs with objects when objects do not occupy large portions of the scenes. It is thus unclear how humans achieve rapid scene categorization.
    • Neural population-level memory traces in the mouse hippocampus.

      Chen, Guifen; Wang, Lei Phillip; Tsien, Joe Z.; Brain & Behavior Discovery Institute; Department of Neurology (2009-12-17)
      One of the fundamental goals in neurosciences is to elucidate the formation and retrieval of brain's associative memory traces in real-time. Here, we describe real-time neural ensemble transient dynamics in the mouse hippocampal CA1 region and demonstrate their relationships with behavioral performances during both learning and recall. We employed the classic trace fear conditioning paradigm involving a neutral tone followed by a mild foot-shock 20 seconds later. Our large-scale recording and decoding methods revealed that conditioned tone responses and tone-shock association patterns were not present in CA1 during the first pairing, but emerged quickly after multiple pairings. These encoding patterns showed increased immediate-replay, correlating tightly with increased immediate-freezing during learning. Moreover, during contextual recall, these patterns reappeared in tandem six-to-fourteen times per minute, again correlating tightly with behavioral recall. Upon traced tone recall, while various fear memories were retrieved, the shock traces exhibited a unique recall-peak around the 20-second trace interval, further signifying the memory of time for the expected shock. Therefore, our study has revealed various real-time associative memory traces during learning and recall in CA1, and demonstrates that real-time memory traces can be decoded on a moment-to-moment basis over any single trial.
    • NMDA Receptors Are Not Required for Pattern Completion During Associative Memory Recall

      Mei, Bing; Li, Fei; Gu, Yiran; Cui, Zhenzhong; Tsien, Joe Z.; Brain & Behavior Discovery Institute; Department of Neurology (2011-04-29)
      Pattern completion, the ability to retrieve complete memories initiated by subsets of external cues, has been a major focus of many computation models. A previously study reports that such pattern completion requires NMDA receptors in the hippocampus. However, such a claim was derived from a non-inducible gene knockout experiment in which the NMDA receptors were absent throughout all stages of memory processes as well as animal's adult life. This raises the critical question regarding whether the previously described results were truly resulting from the requirement of the NMDA receptors in retrieval. Here, we have examined the role of the NMDA receptors in pattern completion via inducible knockout of NMDA receptors limited to the memory retrieval stage. By using two independent mouse lines, we found that inducible knockout mice, lacking NMDA receptor in either forebrain or hippocampus CA1 region at the time of memory retrieval, exhibited normal recall of associative spatial reference memory regardless of whether retrievals took place under full-cue or partial-cue conditions. Moreover, systemic antagonism of NMDA receptor during retention tests also had no effect on full-cue or partial-cue recall of spatial water maze memories. Thus, both genetic and pharmacological experiments collectively demonstrate that pattern completion during spatial associative memory recall does not require the NMDA receptor in the hippocampus or forebrain.
    • Predicting Impaired Extinction of Traumatic Memory and Elevated

      Nalloor, Rebecca Ipe; Bunting, Kristopher M.; Vazdarjanova, Almira; Brain & Behavior Discovery Institute; Department of Neurology (2011-05-18)
      Background: Emotionally traumatic experiences can lead to debilitating anxiety disorders,
    • Robust Action Recognition Using Multi-Scale Spatial-Temporal Concatenations of Local Features as Natural Action Structures

      Zhu, Xiaoyuan; Li, Meng; Li, Xiaojian; Yang, Zhiyong; Tsien, Joe Z.; Brain & Behavior Discovery Institute; Department of Neurology; Department of Ophthalmology (2012-10-4)
      Human and many other animals can detect, recognize, and classify natural actions in a very short time. How this is achieved by the visual system and how to make machines understand natural actions have been the focus of neurobiological studies and computational modeling in the last several decades. A key issue is what spatial-temporal features should be encoded and what the characteristics of their occurrences are in natural actions. Current global encoding schemes depend heavily on segmenting while local encoding schemes lack descriptive power. Here, we propose natural action structures, i.e., multi-size, multi-scale, spatial-temporal concatenations of local features, as the basic features for representing natural actions. In this concept, any action is a spatial-temporal concatenation of a set of natural action structures, which convey a full range of information about natural actions. We took several steps to extract these structures. First, we sampled a large number of sequences of patches at multiple spatial-temporal scales. Second, we performed independent component analysis on the patch sequences and classified the independent components into clusters. Finally, we compiled a large set of natural action structures, with each corresponding to a unique combination of the clusters at the selected spatial-temporal scales. To classify human actions, we used a set of informative natural action structures as inputs to two widely used models. We found that the natural action structures obtained here achieved a significantly better recognition performance than low-level features and that the performance was better than or comparable to the best current models. We also found that the classification performance with natural action structures as features was slightly affected by changes of scale and artificially added noise. We concluded that the natural action structures proposed here can be used as the basic encoding units of actions and may hold the key to natural action understanding.
    • Statistics of eye movements in scene categorization and scene memorization

      Chen, Xin; Wan, Weibing; Yang, Zhiyong; Brain & Behavior Discovery Institute; Department of Ophthalmology; Vision Discovery Institute (2012-07-16)
    • Statistics of natural scene structures and scene categorization

      Chen, Xin; Wan, Weibing; Yong, Zhiyong; Brain & Behavior Discovery Institute; Department of Ophthalmology; Vision Discovery Institute (2012-07-16)
    • Temporal Dynamics of Distinct CA1 Cell Populations during Unconscious State Induced by Ketamine

      Kuang, Hui; Lin, Longnian; Tsien, Joe Z.; Brain & Behavior Discovery Institute (2010-12-8)
      Ketamine is a widely used dissociative anesthetic which can induce some psychotic-like symptoms and memory deficits in some patients during the post-operative period. To understand its effects on neural population dynamics in the brain, we employed large-scale in vivo ensemble recording techniques to monitor the activity patterns of simultaneously recorded hippocampal CA1 pyramidal cells and various interneurons during several conscious and unconscious states such as awake rest, running, slow wave sleep, and ketamine-induced anesthesia. Our analyses reveal that ketamine induces distinct oscillatory dynamics not only in pyramidal cells but also in at least seven different types of CA1 interneurons including putative basket cells, chandelier cells, bistratified cells, and O-LM cells. These emergent unique oscillatory dynamics may very well reflect the intrinsic temporal relationships within the CA1 circuit. It is conceivable that systematic characterization of network dynamics may eventually lead to better understanding of how ketamine induces unconsciousness and consequently alters the conscious mind.
    • A visual code book--structured probability distributions in natural scenes

      Wan, Weibing; Yong, Zhiyong; Brain & Behavior Discovery Institute; Department of Ophthalmology; Vision Discovery Institute (2012-07-16)
    • What the â Moonwalkâ Illusion Reveals about the Perception of Relative Depth from Motion

      Kromrey, Sarah; Bart, Evgeniy; Hegéd, Jay; Brain & Behavior Discovery Institute; Vision Discovery Institute; Department of Ophthalmology (2011-06-22)
      When one visual object moves behind another, the object farther from the viewer is progressively occluded and/or disoccluded by the nearer object. For nearly half a century, this dynamic occlusion cue has beenthought to be sufficient by itself for determining the relative depth of the two objects. This view is consistent with the self-evident geometric fact that the surface undergoing dynamic occlusion is always farther from the viewer than the occluding surface. Here we use a contextual manipulation ofa previously known motion illusion, which we refer to as theâ Moonwalkâ illusion, to demonstrate that the visual system cannot determine relative depth from dynamic occlusion alone. Indeed, in the Moonwalk illusion, human observers perceive a relative depth contrary to the dynamic occlusion cue. However, the perception of the expected relative depth is restored by contextual manipulations unrelated to dynamic occlusion. On the other hand, we show that an Ideal Observer can determine using dynamic occlusion alone in the same Moonwalk stimuli, indicating that the dynamic occlusion cue is, in principle, sufficient for determining relative depth. Our results indicate that in order to correctly perceive relative depth from dynamic occlusion, the human brain, unlike the Ideal Observer, needs additionalsegmentation information that delineate the occluder from the occluded object. Thus, neural mechanisms of object segmentation must, in addition to motion mechanisms that extract information about relative depth, play a crucial role in the perception of relative depth from motion.