Over the last decade, Low Intensity Focused Ultrasound Stimulation (LIFUS) has emerged as an attractive technology to modulate the activity of deep neural targets without invasive procedures. However, the underlying mechanisms by which ultrasonic waves can ...
Cortical representations of brief, static stimuli become more invariant to identity-preserving transformations along the ventral stream. Likewise, increased invariance along the visual hierarchy should imply greater temporal persistence of temporally struc ...
Epileptic networks, defined as brain regions involved in epileptic brain activity, have been mapped by functional connectivity in simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) recordings. This technique allows to ...
Decoding visual cognition from non-invasive measurements of brain activity has shown valuable applications. Vision-based Brain-Computer Interfaces (BCI) systems extend from spellers to database search and spatial navigation. Despite the high performance of ...
Neurointerfaces have acquired major relevance as both rehabilitative and therapeutic tools for patients with spinal cord injury, limb amputations and other neural disorders. Bidirectional neural interfaces are a key component for the functional control of ...
Neural oscillations in auditory cortex are argued to support parsing and representing speech constituents at their corresponding temporal scales. Yet, how incoming sensory information interacts with ongoing spontaneous brain activity, what features of the ...
Rhythmic auditory stimuli are known to elicit matching activity patterns in neural populations. Furthermore, recent research has established the particular importance of high-gamma brain activity in auditory processing by showing its involvement in auditor ...
In this paper, we trace the history of neural networks applied to natural language understanding tasks, and identify key contributions which the nature of language has made to the development of neural network architectures. We focus on the importance of v ...
Recent advances in Voice Activity Detection (VAD) are driven by artificial and Recurrent Neural Networks (RNNs), however, using a VAD system in battery-operated devices requires further power efficiency. This can be achieved by neuromorphic hardware, which ...
