Michael Stiber | University of Washington Bothell (original) (raw)

Papers by Michael Stiber

We consider the input/output behavior of a realistic dynamical neural model in comparison to thos... more We consider the input/output behavior of a realistic dynamical neural model in comparison to those typically used in artificial neural networks. We have found that such models duplicate well those behaviors seen in living neurons, displaying a range of behaviors commonly seen in a wide variety of nonlinear dynamical systems. This is not captured well by weighted sum/monotonic transfer function models. An example of the consequences of nonlinear dynamics in neural responses is presented for monotonically changing input transients.

IEEE International Conference on Neural Networks, Dec 30, 2002

Nervous systems and their constituent neurons often display complex dynamics in response to input... more Nervous systems and their constituent neurons often display complex dynamics in response to inputs with simple characteristics. Until recently, these behaviors were not even classiAEed, let alone understood. This lack of understanding impedes determination of the utility of dynamical processing elements in artiAEcial neural networks. This paper summarizes a comparison of the responses of an ionic permeability based neural model to periodic inhibitory driving with that of a living preparation. Unlike previous, simpler models, duplication of most neuron response types was excellent, and simulation results led to insights into neuron activities that were subsequently veriAEed by examination of the living data. It is hoped that knowledge of the underlying physiological mechanisms and formal properties of neuron dynamics will lead to advances in artiAEcial neural network computational theory.

Neuroinformatics presents a great challenge to the computer science community. Quantities of data... more Neuroinformatics presents a great challenge to the computer science community. Quantities of data currently range up to multiple-petabyte levels. The data itself are diverse, including scalar, vector (from 1 to 4 dimensions), volumetric (up to 4 dimensional spatio-temporal), topological, and symbolic, structured knowledge. Spatial scales range from Angstroms to meters, while temporal scales go from microseconds to decades. Base data vary greatly from individual to individual, and results computed can change with improvements in algorithms, data collection techniques, or underlying methods. We describe a system for managing, sharing, processing, and visualizing such data. Envisioned as a "researcher's associate", it will facilitate collaboration, interface between researchers and data, and perform bookkeeping associated with the complete scientific information life cycle, from collection, analysis, and publication to review of previous results and the start of a new cycle.

Springer eBooks, 1998

Neuroscientists study various anatomical, physiological, and functional components of nervous sys... more Neuroscientists study various anatomical, physiological, and functional components of nervous systems to better understand how the “low-level” activity of individual cells maps to behavior. In this research process, massive amounts of complex data are collected, but technology has not yet provided systems which integrate this information to help scientists analyze, visualize, and understand the data.

Springer eBooks, 1995

Spike-producing neurons produce complex responses to stationary input trains. These responses hav... more Spike-producing neurons produce complex responses to stationary input trains. These responses have been described using techniques from the field of nonlinear dynamics, and are typical of those from periodically perturbed nonlinear oscillators. Here we are concerned with the effects of nonstationary input trains. We present recent simulation results, largely in agreement with experimental results on a living preparation, emphasizing the relationships between stationary and nonstationary behaviors. The implications for synaptic coding are considered. We suggest that the viewpoint of a neuron as a nonlinear dynamical system has important contributions to make to our understanding of neural computation.

Interest in the ANN AEeld has recently focused on dynamical neural networks for performing tempor... more Interest in the ANN AEeld has recently focused on dynamical neural networks for performing temporal operations, as more realistic models of biological information processing, and to extend ANN learning techniques. While this represents a step towards realism, it is important to note that individual neurons are complex dynamical systems, interacting through nonlinear, nonmonotonic connections. The result is that the ANN concept of learning, even when applied to a single synaptic connection, is a nontrivial subject. Based on recent results from living and simulated neurons, a AErst pass is made at clarifying this problem. We summarize how synaptic changes in a 2-neuron, single synapse neural network can change system behavior and how this constrains the type of modiAEcation scheme that one might want to use for realistic neuron-like processors.

This communication is concerned with synaptic coding of information in realistic neural networks,... more This communication is concerned with synaptic coding of information in realistic neural networks, characterized by changes of neurons' output in response to synaptic input-the operational unit of nervous systems. We examine a simple network with two spike-producing neurons coupled by a single inhibitory synapse. Extending previous work with pacemaker inputs, we provide frequency modulated inputs, such as those associated with rhythmic motor activity. We present evidence to support the hypothesis that such modulated input serves in part to systematically "select" output responses from those seen with pacemaker input. We propose that, as a first approximation, the pacemaker responses can be considered to be basic component "letters" in the neural code.

arXiv (Cornell University), May 21, 2008

A powerful experimental approach for investigating computation in networks of biological neurons ... more A powerful experimental approach for investigating computation in networks of biological neurons is the use of cultured dissociated cortical cells grown into networks on a multi-electrode array. Such preparations allow investigation of network development, activity, plasticity, responses to stimuli, and the effects of pharmacological agents. They also exhibit whole-culture pathological bursting; understanding the mechanisms that underlie this could allow creation of more useful cell cultures and possibly have medical applications [1, 2]. This paper presents preliminary results of a computational study of the interplay of individual neuron activity, cell culture development, and the network behavior. We investigate whether bursting can occur in an initially unconnected "network" that develops connections according to an experimentally-verified model of cell culture connectivity growth.

This report presents a simple, robust algorithm for detection of locking in dynamical systems com... more This report presents a simple, robust algorithm for detection of locking in dynamical systems composed of coupled oscillators. Simulations with added noise show worst-case noise tolerance 10%, with general-case tolerance closer to 50%. We suggest that pattern-recognition approaches to dynamical systems analysis may prove more practical for data accompanied with unavoidable noise.

Neurocomputing, Jun 1, 2000

Auditory singularity detection by a gerbil cochlea model. Bilin Z Stiber, Edwin R Lewis, Michael ... more Auditory singularity detection by a gerbil cochlea model. Bilin Z Stiber, Edwin R Lewis, Michael Stiber, Kenneth R Henry NEUROCOMPUT 32, 537-543, 2000. A singularity event in an acoustic waveform typically produces at ...

Neurocomputing, Jun 1, 1999

This ongoing work is concerned with what the gerbil&#x27;s ear tells the gerbil&#... more This ongoing work is concerned with what the gerbil&#x27;s ear tells the gerbil&#x27;s brain. The responses of over 600 auditory nerve fibers to band-limited Gaussian white noise were recorded, and a smoothed, linear estimate of each fiber&#x27;s impulse response was derived using a reverse ...

Availability of affordable hardware that in effect enables desktop supercomputing has enabled mor... more Availability of affordable hardware that in effect enables desktop supercomputing has enabled more ambitious neural simulations driven by more complex software. However, this opportunity comes with costs, in terms of long learning curves to take advantage of the performance possibilities of idiosyncratic, architecturally heterogenous hardware and decreasing ability to be confident in the quality of simulation results. This paper describes a new neural simulation and software/data provenance framework that reduces the difficulty of taking full advantage of GPU computing and increases investigator confidence that simulations results are valid.

Annals of Biomedical Engineering, Mar 1, 1993

Biological Cybernetics, Jun 11, 2014

This paper describes large-scale simulations of growth, network formation, and behavior in cultur... more This paper describes large-scale simulations of growth, network formation, and behavior in cultures of dissociated cortical cells. A neuron model that incorporates synaptic facilitation/depression and neurite outgrowth/retraction was used to construct virtual cultures of 10,000 cells whose spiking behavior and evolution were investigated in closed-loop simulations. This approach allows us to perform detailed analysis of the effects of model parameters on burst shape and timing, their changes, and the interrelationship among these behaviors, gross network structure, and model parameters. We examined the effects of two parameters-network composition (fraction of excitatory cells) and neuron excitability (activity level corresponding to neurite outgrowth equilibrium)-on network structure and behavior. Our results suggest that much of the burst shape and timing observed in vitro can be explained by a model that includes only closed-loop neurite outgrowth and dynamic synapses; features such as LTP/LTD, random connectivity, long distance connections, and detailed neurite topology are not necessary. Keywords cortical cultures • bursting • network development • dynamics This work was partially supported by an equipment grant from the NVIDIA Corporation. The authors would also like to acknowledge Prof. Shinichi Yamagiwa for his generous offer of time on a GPU cluster at the University of Tsukuba.

International Journal of Bifurcation and Chaos, Dec 1, 1991

This paper discusses synaptic inhibition of one pacemaker neuron by another, using data from livi... more This paper discusses synaptic inhibition of one pacemaker neuron by another, using data from living synapses. Spike discharges were assimilated to point processes. Inhibitory rate scale and behavior form. (i) Forms (p:q locked and others) with similar prevalent spectral components assembled monotonically with p:q. Between different lockings, intermittent, messy and other intermittent forms staggered characteristically; hoppings were interspersed. (ii) Locked, intermittent and messy forms occupied about 1/3 each of the rate scale. Individually, the 1:1, 2:1 and 1:2 locked domains were the widest, and seemed continuous; individual intermittent and messy domains were very narrow. Step-like inhibitory transients induced abrupt postsynaptic changes opposing them, which over- or under-shot and slowly returned in either orderly or complicated (chaotic?) ways to steady states. Input-output relations around inhibitory trains resembled those of first-order lead-lag systems distorted by asymmetric sensitivity to change and saturation. Postsynaptic natural discharges separated into &amp;amp;amp;quot;slow&amp;amp;amp;quot; less variable, and &amp;amp;amp;quot;fast&amp;amp;amp;quot; more variable categories with somewhat different inhibited behaviors. Formal modeling is introduced by summarizing comparable models, the data-assumption discrepancies, and reasonable conjectures as to eventual models.

Computing and Software Systems Experimental investigation of the collective dynamics in large net... more Computing and Software Systems Experimental investigation of the collective dynamics in large networks of neurons is a fundamental step towards understanding the mechanisms behind signal and information processing in the brain. In the last decade, the emergence of high performance computing technology has allowed long-duration numerical simulations to model large-scale neural networks. These simulated networks exhibit behaviors (ranging from stochastic spiking to synchronized bursting) that are observed in the living preparations. These simulations' high spatiotemporal resolution and long duration produce data that, in terms of both quantity and complexity, challenge our interpretative abilities. This thesis presents an application of machine learning techniques to bridge the gap between microscopic and macroscopic behaviors and identify the small-scale activity that leads to large-scale behavior, reducing data complexity to a level that can be amenable to further analysis.

Errata fixed, Matlab labs renumbered so they match the chapters in the textbook.

ArXiv, 2022

Archival institutions and programs worldwide work to ensure that the records of governments, orga... more Archival institutions and programs worldwide work to ensure that the records of governments, organizations, communities, and individuals are preserved for future generations as cultural heritage, as sources of rights, and as vehicles for holding the past accountable and to inform the future. This commitment is guaranteed through the adoption of strategic and technical measures for the long-term preservation of digital assets in any medium and form — textual, visual, or aural. Public and private archives are the largest providers of data big and small in the world and collectively host yottabytes of trusted data, to be preserved forever. Several aspects of retention and preservation, arrangement and description, management and administrations, and access and use are still open to improvement. In particular, recent advances in Artificial Intelligence (AI) open the discussion as to whether AI can support the ongoing availability and accessibility of trustworthy public records. This pap...