1,636 research outputs found
Are Face and Object Recognition Independent? A Neurocomputational Modeling Exploration
Are face and object recognition abilities independent? Although it is
commonly believed that they are, Gauthier et al.(2014) recently showed that
these abilities become more correlated as experience with nonface categories
increases. They argued that there is a single underlying visual ability, v,
that is expressed in performance with both face and nonface categories as
experience grows. Using the Cambridge Face Memory Test and the Vanderbilt
Expertise Test, they showed that the shared variance between Cambridge Face
Memory Test and Vanderbilt Expertise Test performance increases monotonically
as experience increases. Here, we address why a shared resource across
different visual domains does not lead to competition and to an inverse
correlation in abilities? We explain this conundrum using our
neurocomputational model of face and object processing (The Model, TM). Our
results show that, as in the behavioral data, the correlation between
subordinate level face and object recognition accuracy increases as experience
grows. We suggest that different domains do not compete for resources because
the relevant features are shared between faces and objects. The essential power
of experience is to generate a "spreading transform" for faces that generalizes
to objects that must be individuated. Interestingly, when the task of the
network is basic level categorization, no increase in the correlation between
domains is observed. Hence, our model predicts that it is the type of
experience that matters and that the source of the correlation is in the
fusiform face area, rather than in cortical areas that subserve basic level
categorization. This result is consistent with our previous modeling
elucidating why the FFA is recruited for novel domains of expertise (Tong et
al., 2008)
A Cognitive Model for Generalization during Sequential Learning
Traditional artificial neural network models of learning suffer from
catastrophic interference. They are commonly trained to perform only
one specific task, and, when trained on a new task, they forget the original
task completely. It has been shown that the foundational neurocomputational principles embodied by the Leabra cognitive modeling framework,
specifically fast lateral inhibition and a local synaptic plasticity model
that incorporates both correlational and error-based components, are sufficient to largely overcome this limitation during the sequential learning
of multiple motor skills. Evidence has also provided that Leabra is able
to generalize the subsequences of motor skills, when doing so is appropriate. In this paper, we provide a detailed analysis of the extent of
generalization possible with Leabra during sequential learning of multiple tasks. For comparison, we measure the generalization exhibited by
the backpropagation of error learning algorithm. Furthermore, we demonstrate the applicability of sequential learning to a pair of movement tasks
using a simulated robotic arm
Competition on presynaptic resources enhances the discrimination of interfering memories
Evidence suggests that hippocampal adult neurogenesis is critical for discriminating considerably interfering memories. During adult neurogenesis, synaptic competition modifies the weights of synaptic connections nonlocally across neurons, thus providing a different form of unsupervised learning from Hebb’s local plasticity rule. However, how synaptic competition achieves separating similar memories largely remains unknown. Here, we aim to link synaptic competition with such pattern separation. In synaptic competition, adult-born neurons are integrated into the existing neuronal pool by competing with mature neurons for synaptic connections from the entorhinal cortex. We show that synaptic competition and neuronal maturation play distinct roles in separating interfering memory patterns. Furthermore, we demonstrate that a feedforward neural network trained by a competition-based learning rule can outperform a multilayer perceptron trained by the backpropagation algorithm when only a small number of samples are available. Our results unveil the functional implications and potential applications of synaptic competition in neural computation.journal articl
Online Multi-Stage Deep Architectures for Feature Extraction and Object Recognition
Multi-stage visual architectures have recently found success in achieving high classification accuracies over image datasets with large variations in pose, lighting, and scale. Inspired by techniques currently at the forefront of deep learning, such architectures are typically composed of one or more layers of preprocessing, feature encoding, and pooling to extract features from raw images. Training these components traditionally relies on large sets of patches that are extracted from a potentially large image dataset. In this context, high-dimensional feature space representations are often helpful for obtaining the best classification performances and providing a higher degree of invariance to object transformations. Large datasets with high-dimensional features complicate the implementation of visual architectures in memory constrained environments. This dissertation constructs online learning replacements for the components within a multi-stage architecture and demonstrates that the proposed replacements (namely fuzzy competitive clustering, an incremental covariance estimator, and multi-layer neural network) can offer performance competitive with their offline batch counterparts while providing a reduced memory footprint. The online nature of this solution allows for the development of a method for adjusting parameters within the architecture via stochastic gradient descent. Testing over multiple datasets shows the potential benefits of this methodology when appropriate priors on the initial parameters are unknown. Alternatives to batch based decompositions for a whitening preprocessing stage which take advantage of natural image statistics and allow simple dictionary learners to work well in the problem domain are also explored. Expansions of the architecture using additional pooling statistics and multiple layers are presented and indicate that larger codebook sizes are not the only step forward to higher classification accuracies. Experimental results from these expansions further indicate the important role of sparsity and appropriate encodings within multi-stage visual feature extraction architectures
Design and performance analysis of artificial neural network for hand motion detection from EMG signals
Besides prosthetic device control and neuromuscular disease identification, electromyography (EMG)
signals can also be applied in the field of human computer interaction (HCI) system. This article represents the
classification of Electromygraphy (EMG) signal for the detection of different predefined hand motions (left,
right, up and down) using artificial neural network (ANN). The neural network is of backpropagation type,
trained by Levenberg-Marquardt training algorithm. Before the classification process, the EMG signals have
been pre-processed for extracting some features from them. The conventional and most effective time and timefrequency based features are extracted and normalized. The neural network has been trained with the normalized
feature set with supervised learning method. The obtained results show that the designed network can
successfully classify the hand motions from the EMG signals with the success rate of 88.4%. The performance
of the designed network has also been compared to similar research work, whereby it certainly shows the
outperformance
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