Abstract:
:This paper investigates how a neural network model of the ventral visual pathway, VisNet, can form separate view invariant representations of a number of objects seen rotating together. In particular, in the current work one of the rotating objects is always partially occluded by the other objects present during training. A key challenge for the model is to link together the separate partial views of the occluded object into a single view invariant representation of that object. We show how this can be achieved by Continuous Transformation (CT) learning, which relies on spatial similarity between successive views of each object. After training, the network had developed cells in the output layer which had learned to respond invariantly to particular objects over most or all views, with each cell responding to only one object. All objects, including the partially occluded object, were individually represented by a unique subset of output cells.
journal_name
Front Comput Neuroscijournal_title
Frontiers in computational neuroscienceauthors
Tromans JM,Higgins I,Stringer SMdoi
10.3389/fncom.2012.00048subject
Has Abstractpub_date
2012-07-25 00:00:00pages
48issn
1662-5188journal_volume
6pub_type
杂志文章abstract::Numerous findings indicate that spatial phase bears an important cognitive information. Distortion of phase affects topology of edge structures and makes images unrecognizable. In turn, appropriately phase-structured patterns give rise to various illusions of virtual image content and apparent motion. Despite a large ...
journal_title:Frontiers in computational neuroscience
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abstract::Brain computer interfaces allow users to preform various tasks using only the electrical activity of the brain. BCI applications often present the user a set of stimuli and record the corresponding electrical response. The BCI algorithm will then have to decode the acquired brain response and perform the desired task....
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abstract::For large-scale network simulations, it is often desirable to have computationally tractable, yet in a defined sense still physiologically valid neuron models. In particular, these models should be able to reproduce physiological measurements, ideally in a predictive sense, and under different input regimes in which n...
journal_title:Frontiers in computational neuroscience
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abstract::Walking, catching a ball and reaching are all tasks in which humans and animals exhibit advanced motor skills. Findings in biological research concerning motor control suggest a modular control hierarchy which combines movement/motor primitives into complex and natural movements. Engineers inspire their research on th...
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abstract::Learning from limited exemplars (few-shot learning) is a fundamental, unsolved problem that has been laboriously explored in the machine learning community. However, current few-shot learners are mostly supervised and rely heavily on a large amount of labeled examples. Unsupervised learning is a more natural procedure...
journal_title:Frontiers in computational neuroscience
pub_type: 杂志文章
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abstract::This paper presents a new model-based method to define muscle synergies. Unlike the conventional factorization approach, which extracts synergies from electromyographic data, the proposed method employs a biomechanical model and formally defines the synergies as the solution of an optimal control problem. As a result,...
journal_title:Frontiers in computational neuroscience
pub_type: 杂志文章
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abstract::Neuronal action potentials or spikes provide a long-range, noise-resistant means of communication between neurons. As point processes single spikes contain little information in themselves, i.e., outside the context of spikes from other neurons. Moreover, they may fail to cross a synapse. A burst, which consists of a ...
journal_title:Frontiers in computational neuroscience
pub_type: 杂志文章,评审
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abstract::The striatum is an important subcortical structure with extensive connections to other regions of the brain. These connections are believed to play important roles in behaviors such as reward-related processes and impulse control, which show significant sex differences. However, little is known about sex differences i...
journal_title:Frontiers in computational neuroscience
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journal_title:Frontiers in computational neuroscience
pub_type: 杂志文章
doi:10.3389/fncom.2020.00018
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abstract::Glucose is the brain's principal source of ATP, but the extent to which cerebral glucose consumption (CMRglc) is coupled with its oxygen consumption (CMRO2) remains unclear. Measurements of the brain's oxygen-glucose index OGI = CMRO2/CMRglc suggest that its oxygen uptake largely suffices for oxidative phosphorylation...
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journal_title:Frontiers in computational neuroscience
pub_type: 杂志文章
doi:10.3389/fncom.2015.00047
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journal_title:Frontiers in computational neuroscience
pub_type: 杂志文章
doi:10.3389/fncom.2012.00058
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abstract::Recently, biologically inspired models are gradually proposed to solve the problem in text analysis. Convolutional neural networks (CNN) are hierarchical artificial neural networks, which include a various of multilayer perceptrons. According to biological research, CNN can be improved by bringing in the attention mod...
journal_title:Frontiers in computational neuroscience
pub_type: 杂志文章
doi:10.3389/fncom.2016.00064
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abstract::VISUAL RECEPTIVE FIELD (RF) ATTRIBUTES IN VISUAL CORTEX OF PRIMATES HAVE BEEN EXPLAINED MAINLY FROM CORTICAL CONNECTIONS: visual RFs progress from simple to complex through cortico-cortical pathways from lower to higher levels in the visual hierarchy. This feedforward flow of information is paired with top-down proces...
journal_title:Frontiers in computational neuroscience
pub_type: 杂志文章
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abstract::Despite an abundance of computational models for learning of synaptic weights, there has been relatively little research on structural plasticity, i.e., the creation and elimination of synapses. Especially, it is not clear how structural plasticity works in concert with spike-timing-dependent plasticity (STDP) and wha...
journal_title:Frontiers in computational neuroscience
pub_type: 杂志文章
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doi:10.3389/fncom.2013.00156
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journal_title:Frontiers in computational neuroscience
pub_type: 杂志文章
doi:10.3389/fncom.2014.00042
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abstract::Dopaminergic neurons in the mammalian substantia nigra display characteristic phasic responses to stimuli which reliably predict the receipt of primary rewards. These responses have been suggested to encode reward prediction-errors similar to those used in reinforcement learning. Here, we propose a model of dopaminerg...
journal_title:Frontiers in computational neuroscience
pub_type: 杂志文章
doi:10.3389/fncom.2011.00021
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journal_title:Frontiers in computational neuroscience
pub_type: 杂志文章
doi:10.3389/fncom.2013.00082
更新日期:2013-07-01 00:00:00
abstract::Animals try to make sense of sensory information from multiple modalities by categorizing them into perceptions of individual or multiple external objects or internal concepts. For example, the brain constructs sensory, spatial representations of the locations of visual and auditory stimuli in the visual and auditory ...
journal_title:Frontiers in computational neuroscience
pub_type: 杂志文章
doi:10.3389/fncom.2016.00062
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journal_title:Frontiers in computational neuroscience
pub_type: 杂志文章
doi:10.3389/fncom.2015.00110
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abstract::A wide range of blind source separation methods have been used in motor control research for the extraction of movement primitives from EMG and kinematic data. Popular examples are principal component analysis (PCA), independent component analysis (ICA), anechoic demixing, and the time-varying synergy model (d'Avella ...
journal_title:Frontiers in computational neuroscience
pub_type: 杂志文章
doi:10.3389/fncom.2013.00185
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journal_title:Frontiers in computational neuroscience
pub_type: 杂志文章
doi:10.3389/fncom.2011.00041
更新日期:2011-10-12 00:00:00
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journal_title:Frontiers in computational neuroscience
pub_type: 杂志文章
doi:10.3389/fncom.2013.00115
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journal_title:Frontiers in computational neuroscience
pub_type: 杂志文章,评审
doi:10.3389/fncom.2014.00097
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