Browsing by Author "Han, Zhixian"

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    A spatial map: a propitious choice for constraining the binding problem
    (Frontiers Media, 2024-07-02) Han, Zhixian; Sereno, Anne B.; Medicine, School of Medicine
    Many studies have shown that the human visual system has two major functionally distinct cortical visual pathways: a ventral pathway, thought to be important for object recognition, and a dorsal pathway, thought to be important for spatial cognition. According to our and others previous studies, artificial neural networks with two segregated pathways can determine objects' identities and locations more accurately and efficiently than one-pathway artificial neural networks. In addition, we showed that these two segregated artificial cortical visual pathways can each process identity and spatial information of visual objects independently and differently. However, when using such networks to process multiple objects' identities and locations, a binding problem arises because the networks may not associate each object's identity with its location correctly. In a previous study, we constrained the binding problem by training the artificial identity pathway to retain relative location information of objects. This design uses a location map to constrain the binding problem. One limitation of that study was that we only considered two attributes of our objects (identity and location) and only one possible map (location) for binding. However, typically the brain needs to process and bind many attributes of an object, and any of these attributes could be used to constrain the binding problem. In our current study, using visual objects with multiple attributes (identity, luminance, orientation, and location) that need to be recognized, we tried to find the best map (among an identity map, a luminance map, an orientation map, or a location map) to constrain the binding problem. We found that in our experimental simulations, when visual attributes are independent of each other, a location map is always a better choice than the other kinds of maps examined for constraining the binding problem. Our findings agree with previous neurophysiological findings that show that the organization or map in many visual cortical areas is primarily retinotopic or spatial.
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    Exploring neural architectures for simultaneously recognizing multiple visual attributes
    (Springer Nature, 2024-12-03) Han, Zhixian; Sereno, Anne B.; Psychology, School of Science
    Much experimental evidence in neuroscience has suggested a division of higher visual processing into a ventral pathway specialized for object recognition and a dorsal pathway specialized for spatial recognition. Previous computational studies have suggested that neural networks with two segregated pathways (branches) have better performance in visual recognition tasks than neural networks with a single pathway (branch). One previously proposed possibility is that two pathways increase the learning efficiency of a network by allowing separate networks to process information about different visual attributes separately. However, most of these previous studies were limited, considering recognition of only two visual attributes, identity and location, simultaneously with a restricted number of classes in each attribute. We investigate whether it is always advantageous to use two-pathway networks when recognizing other visual attributes as well as examine whether the advantage of using two-pathway networks would be different when there are a different number of classes in each attribute. We find that it is always advantageous to use segregated pathways to process different visual attributes separately, with this advantage increasing with a greater number of classes. Thus, using a computational approach, we demonstrate that it is computationally advantageous to have separate pathways if the amount of variations of a given visual attribute is high or that attribute needs to be finely discriminated. Hence, when the size of the computer vision model is limited, designing a segregated pathway (branch) for a given visual attribute should only be used when it is computationally advantageous to do so.
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    Understanding Cortical Streams from a Computational Perspective
    (MIT Press, 2024) Han, Zhixian; Sereno, Anne B.; Psychology, School of Science
    The two visual cortical streams hypothesis, which suggests object properties (what) are processed separately from spatial properties (where), has a longstanding history, and much evidence has accumulated to support its conjectures. Nevertheless, in the last few decades, conflicting evidence has mounted that demands some explanation and modification. For example, existence of (1) shape activities (fMRI) or shape selectivities (physiology) in dorsal stream, similar to ventral stream; likewise, spatial activations (fMRI) or spatial selectivities (physiology) in ventral stream, similar to dorsal stream; (2) multiple segregated subpathways within a stream. In addition, the idea of segregation of various aspects of multiple objects in a scene raises questions about how these properties of multiple objects are then properly re-associated or bound back together to accurately perceive, remember, or make decisions. We will briefly review the history of the two-stream hypothesis, discuss competing accounts that challenge current thinking, and propose ideas on why the brain has segregated pathways. We will present ideas based on our own data using artificial neural networks (1) to reveal encoding differences for what and where that arise in a two-pathway neural network, (2) to show how these encoding differences can clarify previous conflicting findings, and (3) to elucidate the computational advantages of segregated pathways. Furthermore, we will discuss whether neural networks need to have multiple subpathways for different visual attributes. We will also discuss the binding problem (how to correctly associate the different attributes of each object together when there are multiple objects each with multiple attributes in a scene) and possible solutions to the binding problem. Finally, we will briefly discuss problems and limitations with existing models and potential fruitful future directions.