@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 {5009, title = {Image interpretation by iterative bottom-up top- down processing}, number = {120}, year = {2021}, month = {11/2021}, abstract = {

Scene understanding requires the extraction and representation of scene components, such as objects and their parts, people, and places, together with their individual properties, as well as relations and interactions between them. We describe a model in which meaningful scene structures are extracted from the image by an iterative process, combining bottom-up (BU) and top-down (TD) networks, interacting through a symmetric bi-directional communication between them ({\textquoteleft}counter-streams{\textquoteright} structure). The BU- TD model extracts and recognizes scene constituents with their selected properties and relations, and uses them to describe and understand the image.

The scene representation is constructed by the iterative use of three components. The first model component is a bottom-up stream that extracts selected scene elements, properties and relations. The second component ({\textquoteleft}cognitive augmentation{\textquoteright}) augments the extracted visual representation based on relevant non-visual stored representations. It also provides input to the third component, the top-down stream, in the form of a TD instruction, instructing the model what task to perform next. The top-down stream then guides the BU visual stream to perform the selected task in the next cycle. During this

process, the visual representations extracted from the image can be combined with relevant non- visual representations, so that the final scene representation is based on both visual information extracted from the scene and relevant stored knowledge of the world.
We show how the BU-TD model composes complex visual tasks from sequences of steps, invoked by individual TD instructions. In particular, we describe how a sequence of TD-instructions is used to extract from the scene structures of interest, including an algorithm to automatically select the next TD- instruction in the sequence. The selection of TD instruction depends in general on the goal, the image, and on information already extracted from the image in previous steps. The TD-instructions sequence is therefore not a fixed sequence determined at the start, but an evolving program (or {\textquoteleft}visual routine{\textquoteright}) that depends on the goal and the image.

The extraction process is shown to have favourable properties in terms of combinatorial generalization,

generalizing well to novel scene structures and new combinations of objects, properties and relations not seen during training. Finally, we compare the model with relevant aspects of the human vision, and suggest directions for using the BU-TD scheme for integrating visual and cognitive components in the process of scene understanding.
}, author = {Shimon Ullman and Liav Assif and Alona Strugatski and Ben-Zion Vatashsky and Hila Levi and Aviv Netanyahu and Adam Uri Yaari} } @conference {4827, title = {Multi-resolution modeling of a discrete stochastic process identifies causes of cancer}, booktitle = {International Conference on Learning Representations}, year = {2021}, month = {09/2020}, abstract = {

Detection of cancer-causing mutations within the vast and mostly unexplored human genome is a major challenge. Doing so requires modeling the background mutation rate, a highly non-stationary stochastic process, across regions of interest varying in size from one to millions of positions. Here, we present the split-Poisson-Gamma (SPG) distribution, an extension of the classical Poisson-Gamma formulation, to model a discrete stochastic process at multiple resolutions. We demonstrate that the probability model has a closed-form posterior, enabling efficient and accurate linear-time prediction over any length scale after the parameters of the model have been inferred a single time. We apply our framework to model mutation rates in tumors and show that model parameters can be accurately inferred from high-dimensional epigenetic data using a convolutional neural network, Gaussian process, and maximum-likelihood estimation. Our method is both more accurate and more efficient than existing models over a large range of length scales. We demonstrate the usefulness of multi-resolution modeling by detecting genomic elements that drive tumor emergence and are of vastly differing sizes.

}, url = {https://openreview.net/forum?id=KtH8W3S_RE}, author = {Adam Uri Yaari and Maxwell Sherman and Oliver Clarke Priebe and Po-Ru Loh and Boris Katz and Andrei Barbu and Bonnie Berger} }