Modeling and Simulation Methods of Neuronal Populations and Neuronal Networks

This thesis presents numerical methods and modeling related to simulating neurons. Two approaches to the simulation are taken: a population density approach and a neuronal network approach. The first two chapters present the results from the population density approach and its applications. The population density approach assumes that each neuron can be identified by its states (e.g., membrane potential, conductance of ion channels). Additionally, it assumes the population is large such that it can be approximated by a continuous population density distribution in the state space. By updating this population density, we can learn the macroscopic behavior of the population, such as the average firing rate and average membrane potential. The Population density approach avoids the need to simulate every single neuron when the population is large. While many previous population-density methods, such as the mean-field method, make further simplifications to the models, we developed the Asymmetric Particle Population Density (APPD) method to simulate the population density directly without the need to simplify the dynamics of the model. This enables us to simulate the macroscopic properties of coupled neuronal populations as accurately as a direct simulation. The APPD method tracks multiple asymmetric Gaussians as they advance in time due to a convection-diffusion equation, and our main theoretical innovation is deriving this update algorithm by tracking a level set. Tracking a single Gaussian is also applicable to the Bayesian filtering for continuous-discrete systems. By adding a measurement-update step, we reformulated our tracking method as the Level Set Kalman Filter(LSKF) method and find that it offers greater accuracy than state-of-the-art methods. Chapter IV presents the methods for direct simulation of a neuronal network. For this approach, the aim is to build a high-performance and expandable framework that can be used to simulate various neuronal networks. The implementation is done on GPUs using CUDA, and this framework enables simulation for millions of neurons on a high-performance desktop computer. Additionally, real-time visualization of neuron activities is implemented. Pairing with the simulation framework, a detailed mouse cortex model with experiment-determined morphology using the CUBIC-Atlas, and neuron connectome information from Allen's brain atlas is generated.PHDApplied and Interdisciplinary MathematicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169840/1/nywang_1.pd

Fly-Over: A Light-Weight Distributed Router Power-Gating Mechanism for Energy-Efficient Interconnects

Scalable Networks-on-chip (NoCs) have become the de facto interconnection mechanism in large scale Chip Multiprocessors. Not only are NoCs devouring a large fraction of the on-chip power budget but static NoC power consumption is becoming the dominant component as technology scales down. Hence reducing static NoC power consumption is critical for energy-efficient computing. Previous research has proposed to power-gate routers attached to inactive cores so as to save static power, but they either required centralized decision making and global network knowledge or a non-scalable escape ring network. In this paper, we propose Fly-Over (FLOV), a light-weight distributed mechanism for power gating routers, which encompasses FLOV router microarchitecture and a partition-based dynamic routing algorithm to maintain network functionality. With simple modifications to the baseline router microarchitecture, FLOV can facilitate fly-over links over power-gated routers. The proposed routing algorithm provides best-effort minimal path routing without the necessity for global network information. We evaluate our scheme using both unicast and multicast synthetic workloads as well as real workloads from PARSEC 2.1 benchmark suite. The results show that FLOV can achieve 19.2% latency reduction and 16.9% total power savings

Understanding Auditory Context Effects and their Implications

University of Minnesota Ph.D. dissertation. December 2015. Major: Psychology. Advisor: Peggy Nelson. 1 computer file (PDF); viii, 170 pages.Our perception of sound at any point in time is dependent not only on the sound itself, but also on the acoustic environment of the recent past. These auditory context effects reflect the adaptation of the auditory system to the ambient conditions, and provide the potential for improving coding efficiency as well as providing the basis for some forms of perceptual invariance in the face of different talkers, different room environments, and different types of background noise. Despite their obvious importance for auditory perception, the mechanisms underlying auditory context effects remain unclear. The overall goal of this thesis was to investigate different auditory context effects in both normal-hearing listeners and cochlear-implant (CI) users, to shed light on the potential underlying mechanisms, to reveal their implications for auditory perception, and to investigate the effects of hearing loss on these context effects. In Chapters 2, 3 and 4, different context effects, known respectively as the loudness context effect (LCE), induced loudness reduction (ILR), and spectral motion contrast effect, are examined. Another context effect, known as auditory enhancement, is introduced in Chapter 5 with a vowel enhancement paradigm, and is further explored in Chapter 6 by treating it as process of frequency-selective gain control. Finally, a simplified neural model is proposed in Chapter 7 to explain the basis of auditory enhancement, while remaining consistent with the results from the studies of other context effects. The results reveal both similarities and differences between normal-hearing listeners and CI users in responses to auditory context effects, and suggest a role of peripheral processes played in auditory context effects and a potential opportunity to improve current CI speech processing strategies through a restoration of normal auditory context effects

Emergency Resource Layout with Multiple Objectives under Complex Disaster Scenarios

Effective placement of emergency rescue resources, particularly with joint suppliers in complex disaster scenarios, is crucial for ensuring the reliability, efficiency, and quality of emergency rescue activities. However, limited research has considered the interaction between different disasters and material classification, which are highly vital to the emergency rescue. This study provides a novel and practical framework for reliable strategies of emergency rescue under complex disaster scenarios. The study employs a scenario-based approach to represent complex disasters, such as earthquakes, mudslides, floods, and their interactions. In optimizing the placement of emergency resources, the study considers government-owned suppliers, framework agreement suppliers, and existing suppliers collectively supporting emergency rescue materials. To determine the selection of joint suppliers and their corresponding optimal material quantities under complex disaster scenarios, the research proposes a multi-objective model that integrates cost, fairness, emergency efficiency, and uncertainty into a facility location problem. Finally, the study develops an NSGA-II-XGB algorithm to solve a disaster-prone province example and verify the feasibility and effectiveness of the proposed multi-objective model and solution methods. The results show that the methodology proposed in this paper can greatly reduce emergency costs, rescue time, and the difference between demand and suppliers while maximizing the coverage of rescue resources. More importantly, it can optimize the scale of resources by determining the location and number of materials provided by joint suppliers for various kinds of disasters simultaneously. This research represents a promising step towards making informed configuration decisions in emergency rescue work

Spectral motion contrast as a speech context effect

Spectral contrast effects may help "normalize" the incoming sound and produce perceptual constancy in the face of the variable acoustics produced by different rooms, talkers, and backgrounds. Recent studies have concentrated on the after-effects produced by the long-term average power spectrum. The present study examined contrast effects based on spectral motion, analogous to visual-motion after-effects. In experiment 1, the existence of spectral-motion after-effects with word-length inducers was established by demonstrating that the identification of the direction of a target spectral glide was influenced by the spectral motion of a preceding inducer glide. In experiment 2, the target glide was replaced with a synthetic sine-wave speech sound, including a formant transition. The speech category boundary was shifted by the presence and direction of the inducer glide. Finally, in experiment 3, stimuli based on synthetic sine-wave speech sounds were used as both context and target stimuli to show that the spectral-motion after-effects could occur even with inducers with relatively short speech-like durations and small frequency excursions. The results suggest that spectral motion may play a complementary role to the long-term average power spectrum in inducing speech context effects

On the complexity of computing Markov perfect equilibrium in general-sum stochastic games

Similar to the role of Markov decision processes in reinforcement learning, Markov games (also called stochastic games) lay down the foundation for the study of multi-agent reinforcement learning and sequential agent interactions. We introduce approximate Markov perfect equilibrium as a solution to the computational problem of finite-state stochastic games repeated in the infinite horizon and prove its PPAD-completeness. This solution concept preserves the Markov perfect property and opens up the possibility for the success of multi-agent reinforcement learning algorithms on static two-player games to be extended to multi-agent dynamic games, expanding the reign of the PPAD-complete class