Using Artificial Intelligence with MATLAB in Neuroscience

Neuroscientists use MATLAB and Simulink to process and analyze experimental data, drive experiments, and simulate models of brain circuits. With MATLAB and Simulink, you can:

  • Analyze neural time-series data from electrode signal recordings
  • Understand structural and functional image data from neuroimaging and microscopy studies
  • Use machine and deep learning to classify, predict, and cluster using models trained with neuroscience data
  • Process and generate live data streams, including brain-computer interface (BCI) and behavioral control systems

Neuroscientists using MATLAB can also access a rich library of third-party tools purpose-built for neuroscience applications. These include freely-shared community toolboxes and commercially-supported partner products offering hardware and cloud connectivity.

Many new physical brain connections have been discovered in the recent neuroscience and brain studies. Not to mention the discovery of information about how the nervous system directs and processes information. Meanwhile, advances in computer algorithms, software, and hardware have brought machine learning to previously unimaginable levels. Learning how the brain processes information will make it easier for programmers to translate the concept of thinking from the challenging world of biology into new forms of machine learning in the digital world.

MATLAB’s Innovations for Neuroscience e-book

Neural Data Science

Use MATLAB to work with datasets containing multiple trials, subjects, and data modalities, using built-in libraries of algorithms for statistics, machine learning, and deep learning.

Scale MATLAB processing to run on all cores and GPU cards on individual computers and workstations using Parallel Computing Toolbox.

Access MATLAB Parallel Server to easily scale to remote clusters across one or more compute nodes.

Sustainable Neuroscience Software with Research Software Engineering and Team Programming e-book

Neural Time Series

Use MATLAB to visualize and analyze neuroscience time-series data, including spike, field, and scalp recordings and behavioral monitoring records.

Apply preprocessing and extract data features in the time, frequency, and time-frequency domains using MATLAB algorithms and interactive apps for signal processing and wavelet analysis.

Apply deep learning techniques suited for time-series data, such as long short-term memory (LSTM) networks.

Neuroimaging, Microscopy, and Ethology

Use MATLAB to visualize and analyze neuroscience image and video data at the neuron, brain, and subject scales.

Access 2-D and 3-D image data in common file formats, such as NIfTI and TIFF, and work with datasets too large to fit in memory. Align images across imaging sessions and subjects. Analyze brain regions and cellular structures with morphological operations and algorithms for image segmentation. Build custom image processing workflows using interactive tools for specifying points and regions-of-interest (ROIs).

Interactively label image data with the Image Labeler and Video Labeler apps. Apply deep learning techniques to labeled datasets to classify or quantify whole images, identifiable regions or structures, or individual pixels.

Experiment Control and Brain-Computer Interfaces (BCIs)

Use MATLAB to stream data to and from a wide range of hardware devices, including data acquisition systems, cameras, EEG systems, neural recording systems, brain stimulators, and two-photon microscopes.

Use Simulink Real-Time and HDL Coder to control real-time hardware and FPGA hardware, respectively, to control experiments or BCIs with guaranteed submillisecond precision.

Use Stateflow to design control logic for behavioral tasks, BCI systems, and other experiments. Run Stateflow charts in MATLAB, or target execution on real-time or FPGA hardware.

Read the blog post titled “What is Elon Musk’s goal with NeuraLink?”

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