BACKGROUND: Clinically approved antidepressants modulate the brain's emotional valence circuits, suggesting that the response of these circuits could serve as a biomarker for screening candidate antidepressant drugs. However, it is necessary that the...
Visual scene category representations emerge very rapidly, yet the computational transformations that enable such invariant categorizations remain elusive. Deep convolutional neural networks (CNNs) perform visual categorization at near human-level ac...
Neural networks : the official journal of the International Neural Network Society
Jul 23, 2018
Multi-view learning (MVL) concentrates on the problem of learning from the data represented by multiple distinct feature sets. The consensus and complementarity principles play key roles in multi-view modeling. By exploiting the consensus principle o...
Neural networks : the official journal of the International Neural Network Society
Jul 17, 2018
Image transformation between multiple domains has become a challenging problem in deep generative networks. This is because, in real-world applications, finding paired images in different domains is an expensive and impractical task. This paper propo...
Neurons in sensory cortex are tuned to diverse features in natural scenes. But what determines which features neurons become selective to? Here we explore the idea that neuronal selectivity is optimized to represent features in the recent sensory pas...
In this study, we evaluated the convolutional neural network (CNN) method for modeling V1 neurons of awake macaque monkeys in response to a large set of complex pattern stimuli. CNN models outperformed all the other baseline models, such as Gabor-bas...
IEEE transactions on neural networks and learning systems
Jun 5, 2018
This paper deals with sparse signal reconstruction by designing a discrete-time projection neural network. Sparse signal reconstruction can be converted into an L -minimization problem, which can also be changed into the unconstrained basis pursuit d...
We present a theoretical study aiming at model fitting for sensory neurons. Conventional neural network training approaches are not applicable to this problem due to lack of continuous data. Although the stimulus can be considered as a smooth time-de...
The robustness of the visual system lies in its ability to perceive degraded images. This is achieved through interacting bottom-up, recurrent, and top-down pathways that process the visual input in concordance with stored prior information. The inte...
IEEE transactions on neural networks and learning systems
May 2, 2018
We present here a learning system using the iCub humanoid robot and the SpiNNaker neuromorphic chip to solve the real-world task of object-specific attention. Integrating spiking neural networks with robots introduces considerable complexity for ques...