Learning view invariant recognition with partially occluded objects.

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 Neurosci

authors

Tromans JM,Higgins I,Stringer SM

doi

10.3389/fncom.2012.00048

subject

Has Abstract

pub_date

2012-07-25 00:00:00

pages

48

issn

1662-5188

journal_volume

6

pub_type

杂志文章
  • On the role of spatial phase and phase correlation in vision, illusion, and cognition.

    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

    pub_type: 杂志文章

    doi:10.3389/fncom.2015.00045

    authors: Gladilin E,Eils R

    更新日期:2015-04-21 00:00:00

  • Multimodal Neural Network for Rapid Serial Visual Presentation Brain Computer Interface.

    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....

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2016.00130

    authors: Manor R,Mishali L,Geva AB

    更新日期:2016-12-20 00:00:00

  • An Approximation to the Adaptive Exponential Integrate-and-Fire Neuron Model Allows Fast and Predictive Fitting to Physiological Data.

    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

    pub_type: 杂志文章

    doi:10.3389/fncom.2012.00062

    authors: Hertäg L,Hass J,Golovko T,Durstewitz D

    更新日期:2012-09-06 00:00:00

  • MACOP modular architecture with control primitives.

    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...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2013.00099

    authors: Waegeman T,Hermans M,Schrauwen B

    更新日期:2013-07-23 00:00:00

  • Unsupervised Few-Shot Feature Learning via Self-Supervised Training.

    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

    doi:10.3389/fncom.2020.00083

    authors: Ji Z,Zou X,Huang T,Wu S

    更新日期:2020-10-14 00:00:00

  • A model-based approach to predict muscle synergies using optimization: application to feedback control.

    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

    doi:10.3389/fncom.2015.00121

    authors: Sharif Razavian R,Mehrabi N,McPhee J

    更新日期:2015-10-06 00:00:00

  • Neural Coding With Bursts-Current State and Future Perspectives.

    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

    doi:10.3389/fncom.2018.00048

    authors: Zeldenrust F,Wadman WJ,Englitz B

    更新日期:2018-07-06 00:00:00

  • Sex Differences in Fiber Connection between the Striatum and Subcortical and Cortical Regions.

    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

    pub_type: 杂志文章

    doi:10.3389/fncom.2016.00100

    authors: Lei X,Han Z,Chen C,Bai L,Xue G,Dong Q

    更新日期:2016-09-23 00:00:00

  • Neuronal Degeneration Impairs Rhythms Between Connected Microcircuits.

    abstract::Synchronization of neural activity across brain regions is critical to processes that include perception, learning, and memory. After traumatic brain injury (TBI), neuronal degeneration is one possible effect and can alter communication between neural circuits. Consequently, synchronization between neurons may change ...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2020.00018

    authors: Schumm SN,Gabrieli D,Meaney DF

    更新日期:2020-03-03 00:00:00

  • The Effects of Capillary Transit Time Heterogeneity (CTH) on the Cerebral Uptake of Glucose and Glucose Analogs: Application to FDG and Comparison to Oxygen Uptake.

    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...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2016.00103

    authors: Angleys H,Jespersen SN,Østergaard L

    更新日期:2016-10-13 00:00:00

  • Simultaneous learning and filtering without delusions: a Bayes-optimal combination of Predictive Inference and Adaptive Filtering.

    abstract::Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to produce prediction errors for subsequ...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2015.00047

    authors: Kneissler J,Drugowitsch J,Friston K,Butz MV

    更新日期:2015-04-30 00:00:00

  • Computational models of neuron-astrocyte interaction in epilepsy.

    abstract::Astrocytes actively shape the dynamics of neurons and neuronal ensembles by affecting several aspects critical to neuronal function, such as regulating synaptic plasticity, modulating neuronal excitability, and maintaining extracellular ion balance. These pathways for astrocyte-neuron interaction can also enhance the ...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2012.00058

    authors: Volman V,Bazhenov M,Sejnowski TJ

    更新日期:2012-08-13 00:00:00

  • Visual Cortex Inspired CNN Model for Feature Construction in Text Analysis.

    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

    authors: Fu H,Niu Z,Zhang C,Ma J,Chen J

    更新日期:2016-07-14 00:00:00

  • The role of pulvinar in the transmission of information in the visual hierarchy.

    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

    doi:10.3389/fncom.2012.00029

    authors: Cortes N,van Vreeswijk C

    更新日期:2012-05-28 00:00:00

  • Structural Plasticity Denoises Responses and Improves Learning Speed.

    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

    doi:10.3389/fncom.2016.00093

    authors: Spiess R,George R,Cook M,Diehl PU

    更新日期:2016-09-08 00:00:00

  • ARTIE: An Integrated Environment for the Development of Affective Robot Tutors.

    abstract::Over the last decade robotics has attracted a great deal of interest from teachers and researchers as a valuable educational tool from preschool to highschool levels. The implementation of social-support behaviors in robot tutors, in particular in the emotional dimension, can make a significant contribution to learnin...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2016.00077

    authors: Imbernón Cuadrado LE,Manjarrés Riesco Á,De La Paz López F

    更新日期:2016-08-03 00:00:00

  • Simultaneous stability and sensitivity in model cortical networks is achieved through anti-correlations between the in- and out-degree of connectivity.

    abstract::Neuronal networks in rodent barrel cortex are characterized by stable low baseline firing rates. However, they are sensitive to the action potentials of single neurons as suggested by recent single-cell stimulation experiments that reported quantifiable behavioral responses in response to short spike trains elicited i...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2013.00156

    authors: Vasquez JC,Houweling AR,Tiesinga P

    更新日期:2013-11-07 00:00:00

  • Quantized response times are a signature of a neuronal bottleneck in decision.

    abstract::The histograms of response times of optimal YES/NO decisions that are computed from a single sensory Poisson neuron are highly structured. In particular, response times in NO decisions are quantized to a small set of times, while response times in YES decisions have a multimodal structure. Both the times of NO decisio...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2014.00042

    authors: Perona P

    更新日期:2014-04-11 00:00:00

  • Dopamine-signaled reward predictions generated by competitive excitation and inhibition in a spiking neural network model.

    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

    authors: Chorley P,Seth AK

    更新日期:2011-05-18 00:00:00

  • A high-capacity model for one shot association learning in the brain.

    abstract::We present a high-capacity model for one-shot association learning (hetero-associative memory) in sparse networks. We assume that basic patterns are pre-learned in networks and associations between two patterns are presented only once and have to be learned immediately. The model is a combination of an Amit-Fusi like ...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2014.00140

    authors: Einarsson H,Lengler J,Steger A

    更新日期:2014-11-07 00:00:00

  • Generation of Granule Cell Dendritic Morphologies by Estimating the Spatial Heterogeneity of Dendritic Branching.

    abstract::Biological realism of dendritic morphologies is important for simulating electrical stimulation of brain tissue. By adding point process modeling and conditional sampling to existing generation strategies, we provide a novel means of reproducing the nuanced branching behavior that occurs in different layers of granule...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2020.00023

    authors: Chou ZZ,Yu GJ,Berger TW

    更新日期:2020-04-09 00:00:00

  • Revealing the Computational Meaning of Neocortical Interarea Signals.

    abstract::To understand the function of the neocortex, which is a hierarchical distributed network, it is useful giving meaning to the signals transmitted between these areas from the computational viewpoint. The overall anatomical structure or organs related to this network, including the neocortex, thalamus, and basal ganglia...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2020.00074

    authors: Yamakawa H

    更新日期:2020-08-18 00:00:00

  • Spectral Entropy Based Neuronal Network Synchronization Analysis Based on Microelectrode Array Measurements.

    abstract::Synchrony and asynchrony are essential aspects of the functioning of interconnected neuronal cells and networks. New information on neuronal synchronization can be expected to aid in understanding these systems. Synchronization provides insight in the functional connectivity and the spatial distribution of the informa...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2016.00112

    authors: Kapucu FE,Välkki I,Mikkonen JE,Leone C,Lenk K,Tanskanen JM,Hyttinen JA

    更新日期:2016-10-18 00:00:00

  • Interareal coupling reduces encoding variability in multi-area models of spatial working memory.

    abstract::Persistent activity observed during delayed-response tasks for spatial working memory (Funahashi et al., 1989) has commonly been modeled by recurrent networks whose dynamics is described as a bump attractor (Compte et al., 2000). We examine the effects of interareal architecture on the dynamics of bump attractors in s...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2013.00082

    authors: Kilpatrick ZP

    更新日期:2013-07-01 00:00:00

  • Causal Inference for Cross-Modal Action Selection: A Computational Study in a Decision Making Framework.

    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

    authors: Daemi M,Harris LR,Crawford JD

    更新日期:2016-06-23 00:00:00

  • Modeling spontaneous activity across an excitable epithelium: Support for a coordination scenario of early neural evolution.

    abstract::Internal coordination models hold that early nervous systems evolved in the first place to coordinate internal activity at a multicellular level, most notably the use of multicellular contractility as an effector for motility. A recent example of such a model, the skin brain thesis, suggests that excitable epithelia u...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2015.00110

    authors: de Wiljes OO,van Elburg RA,Biehl M,Keijzer FA

    更新日期:2015-09-15 00:00:00

  • Model selection for the extraction of movement primitives.

    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

    authors: Endres DM,Chiovetto E,Giese MA

    更新日期:2013-12-20 00:00:00

  • Inferring single neuron properties in conductance based balanced networks.

    abstract::Balanced states in large networks are a usual hypothesis for explaining the variability of neural activity in cortical systems. In this regime the statistics of the inputs is characterized by static and dynamic fluctuations. The dynamic fluctuations have a Gaussian distribution. Such statistics allows to use reverse c...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2011.00041

    authors: Pool RR,Mato G

    更新日期:2011-10-12 00:00:00

  • A micro-pool model for decision-related signals in visual cortical areas.

    abstract::The study of sensory signaling in the visual cortex has been greatly advanced by the recording of neural activity simultaneously with the performance of a specific psychophysical task. Individual nerve cells may also increase their firing leading up to the particular choice or decision made on a single psychophysical ...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2013.00115

    authors: Parker AJ

    更新日期:2013-08-13 00:00:00

  • Fast convergence of learning requires plasticity between inferior olive and deep cerebellar nuclei in a manipulation task: a closed-loop robotic simulation.

    abstract::The cerebellum is known to play a critical role in learning relevant patterns of activity for adaptive motor control, but the underlying network mechanisms are only partly understood. The classical long-term synaptic plasticity between parallel fibers (PFs) and Purkinje cells (PCs), which is driven by the inferior oli...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章,评审

    doi:10.3389/fncom.2014.00097

    authors: Luque NR,Garrido JA,Carrillo RR,D'Angelo E,Ros E

    更新日期:2014-08-15 00:00:00