Topic: convolutional neural network

Information retrieval from brain responses to auditory and visual stimuli has shown success through classification of song names and image classes presented to participants while recording EEG signals. Information retrieval in the form of reconstructing auditory stimuli has also shown some success, but here we improve on previous methods by reconstructing music stimuli well enough to be perceived and identified independently. Furthermore, deep learning models were trained on time-aligned music stimuli spectrum for each corresponding one-second window of EEG recording, which greatly reduces fea

While non-invasive Brain-to-Computer interface can accurately classify the lateralization of hand moments, the distinction of fingers activation in the same hand is limited by their local and overlapping representation in the motor cortex. In particular, the low signal-to-noise ratio restrains the opportunity to identify meaningful patterns in a supervised fashion. Here we combined Magnetoencephalography (MEG) recordings with advanced decoding strategy to classify finger movements at single trial level. We recorded eight subjects performing a serial reaction time task, where they pressed four

Neural representations of time are central to our understanding of the world around us. I review cognitive, neurophysiological and theoretical work that converges on three simple ideas. First, the time of past events is remembered via populations of neurons with a continuum of functional time constants. Second, these time constants evenly tile the log time axis. This results in a neural Weber-Fechner scale for time which can support behavioral Weber-Fechner laws and characteristic behavioral effects in memory experiments. Third, these populations appear as dual pairs---one type of population c

Understanding how sensory neurons extract relevant information from natural scenes is a major challenge in neuroscience. Probing sensory systems with simple stimuli give limited insights on natural scene processing, and probing selectivity during natural scene stimulation is difficult. Even in the retina, is not clear how Retinal ganglion cells (RGC) extract specific features from natural scenes and send this information to the brain. Many studies using simple, artificial stimuli have shown responses to local light increase (ON-responses), and/or decrease (OFF), but it is unclear if this selec

Sharp-wave ripples (SWRs) are thought to play a crucial role in memory consolidation, learning, and the formation of long-term memories by facilitating the transfer of information from the hippocampus to neocortical networks. To understand the mechanisms governing SWRs, we have developed a closed-loop stimulation framework tailored for the real-time detection and suppression of SWRs, leveraging the ONIX electrophysiology acquisition system and BONSAI-RX acquisition software. As part of our Bonsai package, we developed a convolutional neural network (CNN) for detection of SWRs recorded with lam