Latest Features

Explore the latest MATLAB features relevant for neuroscience

Analyze neural, physiological, and behavioral time-series data.

European Data File Format: Read, write, and explore EDF/EDF+ file format data
Deep Learning: Apply LSTM networks, 1-D convolutional layers, and CNNs with time-frequency analysis for signal classification and prediction tasks
Time-Frequency Analysis: Compute short-time Fourier or continuous wavelet transforms and time-varying coherence between signals
Multiresolution Analysis: Separate signal components with wavelet-based or data-adaptive multiresolution analysis techniques
Feature Extraction: Automatically extract deep features from time-series data using a wavelet scattering framework
Signal Labeling: Label signals automatically and interactively, and visualize labeled signals, with the Signal Labeler app

Analyze images, volumes, and videos at the neuron, brain, and subject scales.

Blockwise Big Image Representation: Process and display N-D images that are too large to fit in memory, including labeled and multiple resolution images, by representation as smaller blocks
Neuroimaging and Microscopy Data: Access image slices and volumes from 3-D NIfTI and TIFF files
Volumetric Data: View labeled volumetric data interactively with the Volume Viewer app, extract image slices, including at oblique angles, and apply over 70 process 3-D image processing functions
Deep Learning: Apply 2D and 3D CNN models for object detection and semantic segmentation, GAN models for image style transfer, and LSTM models for video classification
Image Labeling: Apply and view labels to image data via ROI objects and the interactive Image Labeler and Video Labeler apps

Create, train, and run predictive models for neuroscience data.

Deep Learning Customization: Build custom training loops and custom layers more easily with automatic differentiation and nested layers
Framework Interoperability: Import models and layers from the Tensorflow and Tensorflow-Keras frameworks; and import/export from and to other frameworks via the ONNX model format
Deep Learning Experiments: Compare networks trained under various conditions with the Experiment Manager app
Machine Learning: Discover clusters and noise in data with the DBSCAN algorithm and the Cluster Data live task

Create, share, and scale data analyses.

Graphics: Flexible data distribution plots, including swarm and box charts; and export graphics for use in scientific publications
Live Editor and Tasks: Create rich documents combining code, text, figures, interactive controls, and animations; and complete interactive tasks for common data analysis operations
Unlimited Scaling: MATLAB Parallel Server supports unlimited parallel computing for every user on campus
Cloud Computing: Apply your campus license to run and scale MATLAB in the cloud, connect to remote data sources such as Amazon S3, and run MATLAB in standard or customized containers

Process live signals for brain recordings, behavioral control systems, and BCIs.

Stateflow: Graphically design state machine logic for behavioral control systems, runnable in MATLAB or Simulink
MATLAB Coder: Translate over 3500 MATLAB and toolbox functions to ANSI C or C++ code for faster performance and real-time applications
HDL Coder: Target FPGA hardware for video processing and closed-loop experiments using high-level MATLAB or Simulink programming
Multithreading: Call MATLAB asynchronously from user-created threads using the C++ engine API
Performance: Run existing MATLAB code over two times faster

Students and Researchers

Your university may already provide full access to all products through a Campus-Wide License.

Talk to our neuroscience experts about your application requirements.

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