Showing papers by "Erik C. B. Johnson published in 2021"

DotMotif: An open-source tool for connectome subgraph isomorphism search and graph queries

Abstract: Recent advances in neuroscience have enabled the exploration of brain structure at the level of individual synaptic connections. These connectomics datasets continue to grow in size and complexity; methods to search for and identify interesting graph patterns offer a promising approach to quickly reduce data dimensionality and enable discovery. These graphs are often too large to be analyzed manually, presenting significant barriers to searching for structure and testing hypotheses. We combine graph database and analysis libraries with an easy-to-use neuroscience grammar suitable for rapidly constructing queries and searching for subgraphs and patterns of interest. Our approach abstracts many of the computer science and graph theory challenges associated with nanoscale brain network analysis and allows scientists to quickly conduct research at scale. We demonstrate the utility of these tools by searching for motifs on simulated data and real public connectomics datasets, and we share simple and complex structures relevant to the neuroscience community. We contextualize our findings and provide case studies and software to motivate future neuroscience exploration.

Mine Your Own vieW: Self-Supervised Learning Through Across-Sample Prediction

Abstract: State-of-the-art methods for self-supervised learning (SSL) build representations by maximizing the similarity between different transformed "views" of a sample. Without sufficient diversity in the transformations used to create views, however, it can be difficult to overcome nuisance variables in the data and build rich representations. This motivates the use of the dataset itself to find similar, yet distinct, samples to serve as views for one another. In this paper, we introduce Mine Your Own vieW (MYOW), a new approach for self-supervised learning that looks within the dataset to define diverse targets for prediction. The idea behind our approach is to actively mine views, finding samples that are neighbors in the representation space of the network, and then predict, from one sample's latent representation, the representation of a nearby sample. After showing the promise of MYOW on benchmarks used in computer vision, we highlight the power of this idea in a novel application in neuroscience where SSL has yet to be applied. When tested on multi-unit neural recordings, we find that MYOW outperforms other self-supervised approaches in all examples (in some cases by more than 10%), and often surpasses the supervised baseline. With MYOW, we show that it is possible to harness the diversity of the data to build rich views and leverage self-supervision in new domains where augmentations are limited or unknown.

DotMotif: an open-source tool for connectome subgraph isomorphism search and graph queries.

Abstract: Recent advances in neuroscience have enabled the exploration of brain structure at the level of individual synaptic connections. These connectomics datasets continue to grow in size and complexity; methods to search for and identify interesting graph patterns offer a promising approach to quickly reduce data dimensionality and enable discovery. These graphs are often too large to be analyzed manually, presenting significant barriers to searching for structure and testing hypotheses. We combine graph database and analysis libraries with an easy-to-use neuroscience grammar suitable for rapidly constructing queries and searching for subgraphs and patterns of interest. Our approach abstracts many of the computer science and graph theory challenges associated with nanoscale brain network analysis and allows scientists to quickly conduct research at scale. We demonstrate the utility of these tools by searching for motifs on simulated data and real public connectomics datasets, and we share simple and complex structures relevant to the neuroscience community. We contextualize our findings and provide case studies and software to motivate future neuroscience exploration.

Large-Scale Deep Multi-Layer Analysis of Alzheimer’s Disease Brain Reveals Strong Proteomic Disease-Related Changes Not Observed at the RNA Level

Abstract: The biological processes that are disrupted in the Alzheimer’s disease (AD) brain remain incompletely understood. We recently performed a proteomic analysis of >2000 brains to better understand these changes, which highlighted alterations in astrocytes and microglia as likely key drivers of disease. Here, we extend this analysis by analyzing >1000 brain tissues using a tandem mass tag mass spectrometry (TMT-MS) pipeline, which allowed us to nearly triple the number of quantified proteins across cases. A consensus protein co-expression network analysis of this deeper dataset revealed new co-expression modules that were highly preserved across cohorts and brain regions, and strongly altered in AD. Nearly half of the protein co-expression modules, including modules significantly altered in AD, were not observed in RNA networks from the same cohorts and brain regions, highlighting the proteopathic nature of AD. Two such AD-associated modules unique to the proteomic network included a module related to MAPK signaling and metabolism, and a module related to the matrisome. Analysis of paired genomic and proteomic data within subjects showed that expression level of the matrisome module was influenced by the APOE e4 genotype, but was not related to the rate of cognitive decline after adjustment for neuropathology. In contrast, the MAPK/metabolism module was strongly associated with the rate of cognitive decline. Disease-associated modules unique to the proteome are sources of promising therapeutic targets and biomarkers for AD.

Online learning for orientation estimation during translation in an insect ring attractor network

Abstract: Insect neural systems are a promising source of inspiration for new algorithms for navigation, especially on low size, weight, and power platforms. There have been unprecedented recent neuroscience breakthroughs with Drosophila in behavioral and neural imaging experiments as well as the mapping of detailed connectivity of neural structures. General mechanisms for learning orientation in the central complex (CX) of Drosophila have been investigated previously; however, it is unclear how these underlying mechanisms extend to cases where there is translation through an environment (beyond only rotation), which is critical for navigation in robotic systems. Here, we develop a CX neural connectivity-constrained model that performs sensor fusion, as well as unsupervised learning of visual features for path integration; we demonstrate the viability of this circuit for use in robotic systems in simulated and physical environments. Furthermore, we propose a theoretical understanding of how distributed online unsupervised network weight modification can be leveraged for learning in a trajectory through an environment by minimizing of orientation estimation error. Overall, our results here may enable a new class of CX-derived low power robotic navigation algorithms and lead to testable predictions to inform future neuroscience experiments.

A spiking network model for semantic representation and replay-based association acquisition

Abstract: The ability to form and store several types of associations between representations of natural images is an area of ongoing research in artificial deep neural networks, which may be informed by biologically-inspired computational models. It is hypothesized that replay of sensory stimuli through cortical-hippocampal connections is responsible for training associations between events, as a powerful form of associative learning. While models of associative memories and sensory processing have been studied extensively, there is a potential for spiking models encompassing sensory processing, reasoning over associations, and learning representations which has not been previously demonstrated. Such networks would be suitable for reasoning and learning from visual data on neuromorphic hardware. In this work, we demonstrate a novel visual reasoning network capable of representing semantic relationships and learning new associations through replay-based association with spiking models using natural images. This is demonstrated through associations of natural images from Tiny Imagenet with a knowledge graph derived from WordNet, and we show that relations in the knowledge graph can be accurately traversed for multiple sequential queries. We also demonstrate learning of a novel association after replayed presentations of natural images. This represents a novel capability for machine learning and reasoning with spiking neural networks which may be amenable to neuromorphic hardware.

Simulation and analysis of neuromorphic tactile data for object interaction speed detection

Abstract: The sense of touch provides an intimate connection to our surroundings and is a critical component for navigating the environment and manipulating objects. To better capture and convey touch information to prosthetic arm users or autonomous robots, it is useful to leverage biological representations of tactile signals for rapid and efficient processing. We developed a software pipeline that simulates and analyzes biomimetic tactile signals for developing better techniques and approaches for robotic systems that rely on the sense of touch. Touch sensor signals were simulated with a virtual hand model (MuJoCo HAPTIX) and were converted to a biological representation of tactile afferent spiking activity using existing neuron models (TouchSim). We simulated virtual hand object interaction, at three different movement speeds, and found that biomimetic tactile signals yield better results (91%) compared to using traditional force signals (61%) at classifying the different hand speeds. This software toolkit enables researchers to quickly generate, capture, and analyze biologically realistic tactile behavior for creating smarter and more effective robotic systems.