Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Neural Circuits

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Higher visual areas act like domain-general filters with strong selectivity and functional specialization.

Nature communications·2026

Same author

Stresses and fluid flow in lamina cribrosa through anisotropic poroelasticity.

ArXiv·2026

Same author

Closed-loop error damping in human BCI using pre-error motor cortex activity.

bioRxiv : the preprint server for biology·2026

Same author

Mapping the Causal Roles of Non-Primary Motor Areas in Human Reach Planning and Execution.

Human brain mapping·2026

Same author

Motor Cortex Coverage Predicts Signal Strength of a Stentrode Endovascular Brain-Computer Interface.

medRxiv : the preprint server for health sciences·2025

Same author

Signal properties and stability of a chronically implanted endovascular brain computer interface.

medRxiv : the preprint server for health sciences·2025

Search research articles

Related Experiment Video

Updated: Jul 15, 2025

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Published on: May 29, 2017

Neural Data Transformer 2: Multi-context Pretraining for Neural Spiking Activity.

Joel Ye^1,2,3, Jennifer L Collinger^1,3,4,5,6, Leila Wehbe^2,3,7

¹Rehab Neural Engineering Labs, University of Pittsburgh.

Biorxiv : the Preprint Server for Biology

|October 2, 2023

Summary

Neural Data Transformer 2 (NDT2) enables deep learning models to leverage diverse brain-computer interface datasets. This approach overcomes context-dependent data shifts for improved neural decoding and control.

More Related Videos

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Related Experiment Videos

Last Updated: Jul 15, 2025

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Published on: May 29, 2017

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Area of Science:

Neuroscience
Machine Learning
Biomedical Engineering

Background:

Intracortical brain-computer interfaces (iBCIs) generate complex neural population spiking activity.
Current models are limited to single experimental contexts, restricting data volume and deep neural network (DNN) effectiveness.
Context-dependent shifts in neural data distributions pose a challenge for aggregating diverse datasets.

Approach:

Developed Neural Data Transformer 2 (NDT2), a spatiotemporal Transformer model.
Utilized large-scale unsupervised pretraining across heterogeneous motor BCI datasets spanning sessions, subjects, and tasks.
Demonstrated NDT2's ability to leverage diverse data for representation learning.

Key Points:

NDT2 effectively handles context-dependent shifts in neural spiking data.
Pretraining enables models to learn robust representations from large, varied datasets.
Facilitates rapid adaptation of neural decoding models to novel experimental contexts.

Conclusions:

NDT2 opens possibilities for deploying pretrained DNNs for iBCI control.
Enables more effective and generalizable brain-computer interface systems.
Advances the field of neural data analysis and representation learning.