Resume

• 2013

  A fast spiking neural network analysis toolkit which leverages the ease of use of a Python frontend with the power and speed of a C++ backend. \n\nhttps://github.com/HRLAnalysis/HRLAnalysis

  Corey Thibeault

  Michael

  OBrien

  Neural Analytics

  Claremont McKenna College

  Raytheon

  Aerospace Corporation

  Reed Institute of Decision Science

  HRL Laboratories

  LLC

  Claremont McKenna College

  Los Angeles

  CA

  Senior Data Scientist

  Neural Analytics

• 2010

  Malibu

  CA

  Member of Center for Neural and Emergent Systems

  Information and System Sciences Laboratory \n•\tWork on IARPA’s KRNS project\n•\tDevelop optimization techniques for signal processing of fMRI data\n•\tUse Formal Concept Analysis (rooted in lattice theory) to map fMRI data to semantic structures to understand the hierarchical structure of the human brain\n•\tWork on DARPA’s SyNAPSE project\n•\tContribute to the development of the large scale neural network simulator called HRLSim\n•\tLook for a minimal set of biological characteristics that achieve desired neural network behaviors\n•\tDevelop novel neural network models that exemplify self-organization\n•\tDevelop reinforcement learning algorithms to train networks to perform desired tasks\n•\tDevelop analytical techniques and metrics to quantify in concrete terms the network learning dynamics\n•\tContribute to the open source analytics package HRLAnalysis for analyzing large spiking networks\n•\tHRL Outstanding Team Award

  Research Scientist

  HRL Laboratories

  LLC

  Investigated several aspects of additive number theory\n· Collaborated with a team of undergraduate mathematics students\n· Employed creative problem solving to prove an original mathematical theorem\n· Presented weekly progress reports to other students and professors\n· Wrote a mathematical paper describing findings (to be published)

  Reed Institute of Decision Science

  Claremont McKenna College

  Claremont

  CA

  Taught single variable calculus\n· Developed class curriculum including daily lectures

  homework assignments and exams

  Adjunct Professor Mathematics

  Developed methods to check for errors in telemetry tables\n· Wrote software that uses the methods I developed to automatically check for errors in the telemetry tables

  \nrepair the errors

  and write error analysis reports\n· Supported the Systems Engineering Department through various data and error analysis tasks

  Raytheon

  Software Engineering Intern

  Model and analyze satellite software systems to predict failure rates

  average system availability and overall\nreadiness of the software package\n· Find root causes of problems based on system models\n· Advise program offices of software analysis and recommend software for deployment or give an estimate for\namount of testing necessary to bring software to maturity\n· Spot Award for excellence in work on SBIRS during the summer of 2006\n· Spot Award for excellence in work on AEHF during the summer of 2007

  Aerospace Corporation

  Claremont McKenna College

  Claremont

  CA

  Taught single variable calculus\n· Developed class curriculum including daily lectures

  homework assignments and exams

  Adjunct Professor Mathematics

• 2005

  Masters

  Mathematics

  UCLA

  Doctor of Philosophy (Ph.D.)

  Mathematics; Computational Neuroscience

  UCLA

• 2001

  Bachelor of Science

  Physics; Mathematics

  Claremont McKenna College

• Neural Analytics Inc. Awarded National Science Foundation Grant of $743

  756 for Non-Invasive Monitoring of Intracranial Pressure for Traumatic Brain Injury

  LOS ANGELES--(BUSINESS WIRE)-- Neural Analytics

  a medical device company focusing on developing devices and services to diagnose

  monitor

  and triage a number of neurological conditions announced it has been awarded a $743

  756 National Science Foundation grant for its Small Business Innovation Research Phase II project to monitor intracranial pressure and improve the management of severe traumatic brain injury (TBI).

  Neural Analytics Inc. Raises $10 MM Series A

  LOS ANGELES-(BUSINESS WIRE)-December 21

  2015- Neural Analytics Inc.

  a medical device company focused on developing devices and services to measure

  diagnose and track brain health

  announced today that it has secured $10 MM in Series A financing. This brings the total investment amount in Neural Analytics to $13 MM including their $3 MM Series Seed financing last year.

  Neural Analytics Inc. Raises $10 MM Series A

  UCLA spinout nabs $10M Series A to diagnose

  track traumatic brain injury

  Neural Analytics' Transcranial Doppler device--Courtesy of Neural Analytics Neural Analytics is developing portable transcranial Doppler ultrasound devices that it expects could be used by first responders and emergency room physicians to accurately assess mild to severe traumatic brain injury (TBI)

  including concussions.

  UCLA spinout nabs $10M Series A to diagnose

  track traumatic brain injury

  Computer Science

  Scientific Computing

  Linux

  Neural Networks

  Scientific Writing

  LaTeX

  Computational Mathematics

  Mathematics

  Microsoft Office

  Windows

  Emacs

  Matlab

  Bash

  Programming

  Maple

  Python

  Calculus

  Research

  Physics

  C++

  A novel analytical characterization for short-term plasticity parameters in spiking neural networks

  Narayan Srinivasa

  Corey M. Thibeault

  Short-term plasticity (STP) is a phenomenon that widely occurs in the neocortex with implications for learning and memory. Based on a widely used STP model

  we develop an analytical characterization of the STP parameter space to determine the nature of each synapse (facilitating

  depressing

  or both) in a spiking neural network based on presynaptic firing rate and the corresponding STP parameters. We demonstrate consistency with previous work by leveraging the power of our characterization to replicate the functional volumes that are integral for the previous network stabilization results. We then use our characterization to predict the precise transitional point from the facilitating regime to the depressing regime in a simulated synapse

  suggesting in vitro experiments to verify the underlying STP model. We conclude the work by integrating our characterization into a framework for finding suitable STP parameters for self-sustaining random

  asynchronous activity in a prescribed recurrent spiking neural network. The systematic process resulting from our analytical characterization improves the success rate of finding the requisite parameters for such networks by three orders of magnitude over a random search.

  A novel analytical characterization for short-term plasticity parameters in spiking neural networks

  Corey Thibeault

  The additional capabilities provided by high-performance neural simulation environments and modern computing hardware has allowed for the modeling of increasingly larger spiking neural networks. This is important for exploring more anatomically detailed networks but the corresponding accumulation in data can make analyzing the results of these simulations difficult. This is further compounded by the fact that many existing analysis packages were not developed with large spiking data sets in mind. Presented here is a software suite developed to not only process the increased amount of spike-train data in a reasonable amount of time

  but also provide a user friendly Python interface. We describe the design considerations

  implementation and features of the HRLAnalysis™ suite. In addition

  performance benchmarks demonstrating the speedup of this design compared to a published Python implementation are also presented. The result is a high-performance analysis toolkit that is not only usable and readily extensible

  but also straightforward to interface with existing Python modules.

  Analyzing large-scale spking neural data with HRLAnalysis

  Corey Thibeault

  Modeling of large scale spiking neural models is an important tool in the quest to understand brain function and subsequently create real-world applications. This paper describes a spiking neural network simulator environment called HRLSim. This simulator is suitable for implementation on a cluster of General Purpose Graphical Processing Units (GPGPUs). Novel aspects of HRLSim are described and an analysis of its per- formance is provided for various configurations of the cluster. With the advent of inexpensive GPGPU cards and compute power

  HRLSim offers an affordable and scalable tool for design

  real-time simulation

  and analysis of large scale spiking neural networks. Index

  HRLSim: A High Performance Spiking Neural Network Simulator for GPGPU Clusters

  Narayan Srinivasa

  In this thesis

  we assess the role of short-term synaptic plasticity in an artificial neural network constructed to emulate two important brain functions: self-sustained activity and signal propagation. We employ a widely used short-term synaptic plasticity model (STP) in a symbiotic network

  in which two subnetworks with differently tuned STP behaviors are weakly coupled. This enables both self-sustained global network activity

  generated by one of the subnetworks

  as well as faithful signal propagation within subcircuits of the other subnetwork. Finding the parameters for a properly tuned STP network is difficult. We provide a theoretical argument for a method which boosts the probability of finding the elusive STP parameters by two orders of magnitude

  as demonstrated in tests.\nWe then combine STP with a novel critic-like synaptic learning algorithm

  which we call\nARG-STDP for attenuated-reward-gating of STDP. STDP refers to a commonly used long- term synaptic plasticity model called spike-timing dependent plasticity. With ARG-STDP

  we are able to learnmultiple distal rewards simultaneously

  improving on the previous reward modulated STDP (R-STDP) that could learn only a single distal reward. However

  we also provide a theoretical upperbound on the number of distal reward that can be learned using ARG-STDP. \nWe also consider the problem of simulating large spiking neural networks. We describe\nan architecture for efficiently simulating such networks. The architecture is suitable for implementation on a cluster of General Purpose Graphical Processing Units (GPGPU). Novel aspects of the architecture are described and an analysis of its performance is benchmarked on a GPGPU cluster. With the advent of inexpensive GPGPU cards and compute power

  the described architecture offers an affordable and scalable tool for the design

  real-time simulation

  and analysis of large scale spiking neural networks.

  The Role of Short-Term Synaptic Plasticity in Neural Network Spiking Dynamics and in the Learning of Multiple Distal Rewards by

  Rajan Bhattacharyya

  Kendrick Kay

  James Benvenuto

  Rachel Millin

  We present an algorithm

  Sparse Atomic Feature\nLearning (SAFL)

  that transforms noisy labeled datasets into a\nsparse domain by learning atomic features of the underlying\nsignal space via gradient minimization. The sparse signal rep-\nresentations are highly compressed and cleaner than the original\nsignals. We demonstrate the effectiveness of our techniques on\nfMRI activity patterns. We produce low-dimensional

  sparse\nrepresentations which achieve over 98% compression of the\noriginal signals. The transformed signals can be used to classify\nleft-out testing data at a higher accuracy than the initial data.

  Sparse Atomic Feature Learning via Gradient Regularization: With Applications to Finding Sparse Representations of fMRI Activity Patterns

  A Spiking Neural Model for Stable Reinforcement of Synapses Based on Multiple Distal Rewards

  Narayan Srinivasa

  In this letter

  a novel critic-like algorithm was developed to extend the synaptic plasticity rule described in Florian (2007) and Izhikevich (2007) in order to solve the problem of learning multiple distal rewards simultaneously. The system is augmented with short-term plasticity (STP) to stabilize the learning dynamics

  thereby increasing the system's learning capacity. A theoretical threshold is estimated for the number of distal rewards that this system can learn. The validity of the novel algorithm was verified by computer simulations.

  A Spiking Neural Model for Stable Reinforcement of Synapses Based on Multiple Distal Rewards

  Mike O'Neill

  For prime p

  we classify those pairs of subsets of Z mod p for which equality is attained in Pollard’s theorem. Our result may be considered as an extension of the theorem of Vosper characterizing the critical pairs for the Cauchy-Davenport inequality.

  Equality in Pollard's Theorem on Set Addition of Congruence Classes

  Corey Thibeault

  Efficiently passing spiking messages in a neural model is an important aspect of high-performance simulation. As the scale of networks has increased so has the size of the computing systems required to simulate them. In addition

  the information exchange of these resources has become more of an impediment to performance. In this paper we explore spike message passing using different mechanisms provided by the Message Passing Interface (MPI). A specific implementation

  MVAPICH

  designed for high-performance clusters with Infiniband hardware is employed. The focus is on providing information about these mechanisms for users of commodity high-performance spiking simulators. In addition

  a novel hybrid method for spike exchange was implemented and benchmarked.

  Efficiently passing messages in distributed spiking neural network simulation.

  http://www.militaryaerospace.com/articles/2012/06/krns-proposers-day.html\nhttp://www.militaryaerospace.com/articles/2013/01/iarpa-krns-ai.html

  SyNAPSE

  http://www.technologyreview.com/news/532176/a-brain-inspired-chip-takes-to-the-sky/\nhttp://www.technologyreview.com/featuredstory/526506/neuromorphic-chips/\nhttp://www.economist.com/news/science-and-technology/21582495-computers-will-help-people-understand-brains-better-and-understanding-brains\n