@article {5255, title = {BrainBERT: Self-supervised representation learning for Intracranial Electrodes}, year = {2023}, month = {02/2023}, address = {Kigali, Rwanda, Africa}, abstract = {

We create a reusable Transformer, BrainBERT, for intracranial recordings bringing modern representation learning approaches to neuroscience. Much like in NLP and speech recognition, this Transformer enables classifying complex concepts, i.e., decoding neural data, with higher accuracy and with much less data by being pretrained in an unsupervised manner on a large corpus of unannotated neural recordings. Our approach generalizes to new subjects with electrodes in new positions and to unrelated tasks showing that the representations robustly disentangle the neural signal. Just like in NLP where one can study language by investigating what a language model learns, this approach opens the door to investigating the brain by what a model of the brain learns. As a first step along this path, we demonstrate a new analysis of the intrinsic dimensionality of the computations in different areas of the brain. To construct these representations, we combine a technique for producing super-resolution spectrograms of neural data with an approach designed for generating contextual representations of audio by masking. In the future, far more concepts will be decodable from neural recordings by using representation learning, potentially unlocking the brain like language models unlocked language.

}, keywords = {decoding, language models, Neuroscience, self-supervision, transformer}, url = {https://openreview.net/forum?id=xmcYx_reUn6}, author = {Christopher Wang and Vighnesh Subramaniam and Adam Uri Yaari and Gabriel Kreiman and Boris Katz and Ignacio Cases and Andrei Barbu} } @article {2639, title = {A Causal Relationship Between Face-Patch Activity and Face-Detection Behavior}, journal = {eLife}, year = {2017}, month = {04/2017}, abstract = {

The primate brain contains distinct areas densely populated by face-selective neurons. One of these, face-patch ML, contains neurons selective for contrast relationships between face parts. Such contrast-relationships can serve as powerful heuristics for face detection. However, it is unknown whether neurons with such selectivity actually support face-detection behavior. Here, we devised a naturalistic face-detection task and combined it with fMRI-guided pharmacological inactivation of ML to test whether ML is of critical importance for real-world face detection. We found that inactivation of ML impairs face detection. The effect was anatomically specific, as inactivation of areas outside ML did not affect face detection, and it was categorically specific, as inactivation of ML impaired face detection while sparing body and object detection. These results establish that ML function is crucial for detection of faces in natural scenes, performing a critical first step on which other face processing operations can build.

}, keywords = {face patch, fMRI, inactivation, Neuroscience}, doi = {https://doi.org/10.7554/eLife.18558.001}, url = {https://elifesciences.org/articles/18558}, author = {Srivatsun Sadagopan and Wilbert Zarco and W. A. Freiwald} } @article {438, title = {The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex}, number = {004}, year = {2014}, month = {04/2014}, abstract = {

Is visual cortex made up of general-purpose information processing machinery, or does it consist of a collection of specialized modules? If prior knowledge, acquired from learning a set of objects is only transferable to new objects that share properties with the old, then the recognition system{\textquoteright}s optimal organization must be one containing specialized modules for different object classes. Our analysis starts from a premise we call the invariance hypothesis: that the computational goal of the ventral stream is to compute an invariant-to-transformations and discriminative signature for recognition. The key condition enabling approximate transfer of invariance without sacrificing discriminability turns out to be that the learned and novel objects transform similarly. This implies that the optimal recognition system must contain subsystems trained only with data from similarly-transforming objects and suggests a novel interpretation of domain-specific regions like the fusiform face area (FFA). Furthermore, we can define an index of transformation-compatibility, computable from videos, that can be combined with information about the statistics of natural vision to yield predictions for which object categories ought to have domain-specific regions. The result is a unifying account linking the large literature on view-based recognition with the wealth of experimental evidence concerning domain-specific regions.

}, keywords = {Neuroscience, Theories for Intelligence}, doi = {10.1101/004473}, url = {http://biorxiv.org/lookup/doi/10.1101/004473}, author = {JZ. Leibo and Qianli Liao and F. Anselmi and Tomaso Poggio} } @article {224, title = {Simultaneous whole-animal 3D imaging of neuronal activity using light-field microscopy}, journal = {Nature Methods}, volume = {11}, year = {2014}, month = {05/18/2014}, pages = {727 - 730}, abstract = {

High-speed, large-scale three-dimensional (3D) imaging of neuronal activity poses a major challenge in neuroscience. Here we demonstrate simultaneous functional imaging of neuronal activity at single-neuron resolution in an entire Caenorhabditis elegans and in larval zebrafish brain. Our technique captures the dynamics of spiking neurons in volumes of ~700 μm {\texttimes} 700 μm {\texttimes} 200 μm at 20 Hz. Its simplicity makes it an attractive tool for high-speed volumetric calcium imaging.

}, keywords = {Imaging, Neuroscience}, issn = {1548-7091}, doi = {10.1038/nmeth.2964}, url = {http://www.nature.com/doifinder/10.1038/nmeth.2964}, author = {Prevedel, Robert and Yoon, Young-Gyu and Hoffmann, Maximilian and Pak, Nikita and Wetzstein, Gordon and Kato, Saul and Schr{\"o}del, Tina and Raskar, Ramesh and Zimmer, Manuel and Edward S Boyden and Vaziri, Alipasha} }