Browsing by Subject "Neurological models"

Now showing 1 - 4 of 4
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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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    Orexinergic Neurotransmission in Temperature Responses to Methamphetamine and Stress: Mathematical Modeling as a Data Assimilation Approach
    (PLoS, 2015-05-20) Behrouzvaziri, Abolhassan; Fu, Daniel; Tan, Patrick; Yoo, Yeonjoo; Zaretskaia, Maria V.; Rusyniak, Daniel E.; Molkov, Yaroslav I.; Zaretsky, Dmitry V.; Department of Emergency Medicine, IU School of Medicine
    Experimental Data Orexinergic neurotransmission is involved in mediating temperature responses to methamphetamine (Meth). In experiments in rats, SB-334867 (SB), an antagonist of orexin receptors (OX1R), at a dose of 10 mg/kg decreases late temperature responses (t>60 min) to an intermediate dose of Meth (5 mg/kg). A higher dose of SB (30 mg/kg) attenuates temperature responses to low dose (1 mg/kg) of Meth and to stress. In contrast, it significantly exaggerates early responses (t<60 min) to intermediate and high doses (5 and 10 mg/kg) of Meth. As pretreatment with SB also inhibits temperature response to the stress of injection, traditional statistical analysis of temperature responses is difficult. Mathematical Modeling We have developed a mathematical model that explains the complexity of temperature responses to Meth as the interplay between excitatory and inhibitory nodes. We have extended the developed model to include the stress of manipulations and the effects of SB. Stress is synergistic with Meth on the action on excitatory node. Orexin receptors mediate an activation of on both excitatory and inhibitory nodes by low doses of Meth, but not on the node activated by high doses (HD). Exaggeration of early responses to high doses of Meth involves disinhibition: low dose of SB decreases tonic inhibition of HD and lowers the activation threshold, while the higher dose suppresses the inhibitory component. Using a modeling approach to data assimilation appears efficient in separating individual components of complex response with statistical analysis unachievable by traditional data processing methods.
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    Passage number affects differentiation of sensory neurons from human induced pluripotent stem cells
    (Springer Nature, 2022-09-23) Cantor, Erica L.; Shen, Fei; Jiang, Guanglong; Tan, Zhiyong; Cunningham, Geneva M.; Wu, Xi; Philips, Santosh; Schneider, Bryan P.; Medicine, School of Medicine
    Induced pluripotent stem cells (iPSCs) are a valuable resource for neurological disease-modeling and drug discovery due to their ability to differentiate into neurons reflecting the genetics of the patient from which they are derived. iPSC-derived cultures, however, are highly variable due to heterogeneity in culture conditions. We investigated the effect of passage number on iPSC differentiation to optimize the generation of sensory neurons (iPSC-dSNs). Three iPSC lines reprogrammed from the peripheral blood of three donors were differentiated into iPSC-dSNs at passage numbers within each of the following ranges: low (5-10), intermediate (20-26), and high (30-38). Morphology and pluripotency of the parent iPSCs were assessed prior to differentiation. iPSC-dSNs were evaluated based on electrophysiological properties and expression of key neuronal markers. All iPSC lines displayed similar morphology and were similarly pluripotent across passage numbers. However, the expression levels of neuronal markers and sodium channel function analyses indicated that iPSC-dSNs differentiated from low passage numbers better recapitulated the sensory neuron phenotype than those differentiated from intermediate or high passage numbers. Our results demonstrate that lower passage numbers may be better suited for differentiation into peripheral sensory neurons.
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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.