Advances in neural decoding are reshaping neuromodulation research. Join the Frontiers in Neuroscience Research Topic From Neural Decoding to Neuromodulation and help translate sensorimotor–affective circuit biomarkers into clinical tools.
Submit by 31 May 2026 your manuscript summary→ fro.ntiers.in/RT76200#neuroscience#neuromodulation#neuraldecoding
📢New #ResearchTopic📢
Interested in the field of Adaptive Neural decoding? Check out our recent Topic on "Adaptive Neural Decoding: Spatiotemporal Processing for Brain-Computer Interfaces", led by Profs. Nicoladie Tam and George Zouridakis.
More info: fro.ntiers.in/67908
Interested in contributing? fro.ntiers.in/81XB#neuroscience#neuraldecoding#BCI
ALT The experiment setup. A The 16-electrode array was inserted into the exposed axial nerve cord (ANC). Tactile and electrical stimulation were delivered into three different locations: directly onto the ANC, proximal, and distal to the electrodes’ placements. The carbon electrode array recorded spikes. B Representative spikes are shown on the right
[1/7] 🎉 Our paper 'Get more from less: Differential neural decoding for effective reconstruction of perceived naturalistic stimuli from noisy and scarce neural data' has been accepted for a talk at #cogsci2024! w/ @umuguc#neuraldecoding
Paper: escholarship.org/uc/item/7vt…
Excited to present at #AREADNE2024 in Milos 🌞!
Check out my poster on "Neural Decoding of Temporal Features of Zebra Finch Song" 🧠🎶. Watch the neural activity in action!
Can't wait for your comments and discussions!
#Neuroscience#AI#ZebraFinche#NeuralDecoding#Auditory
MindGPT - a neural decoder, translating visual signals into natural language using fMRI data. This breakthrough hints at a future where we can understand brain responses to visuals without invasive methods.
#MindGPT#NeuralDecoding#Innovation
⭐ @b_cratos focus on Machine Learning ⭐
"Machine Learning for Neural Decoding" - Research article
💡Learn more about it during our upcoming #Webinar with Paolo Viviani, Andres Agudelo-Toro and Brian Dekleva, PhD.
👇 lnkd.in/ePUmzwEZ#machinelearning#neuraldecoding
Scientists led by Krishna Shenoy, a Howard Hughes Medical Institute investigator at Stamford University, have turned brain signals into legible text via a brain-computer interface.
More at: go.nature.com/3eJOlDi#ThisIsOR#ORMS#NeuralDecoding
Our latest paper on brain-machine interface (BMI) decoding with entire spiking activity (ESA) and deep learning has just been published in J. Neural Eng. #BMI#NeuralDecoding#DeepLearning
Latest paper with @ahmadi_nur@CBouganis published in J. Neural Eng. doi.org/10.1088/1741-2552/ab…
We show here how using deep learning (QRNN) on the entire spiking activity (envelope signal) can outperform all combinations of commonly used signals and methods for neural decoding.