About

Bruce C. Wheeler is a Professor Emeritus in the Department of Bioengineering at the University of Illinois Urbana-Champaign. He earned his PhD in Electrical Engineering from Cornell University in 1981. His research interests include the patterned growth of neurons, controlled stimulation of neuronal networks, microminiature sensors for neural recording, and analysis of multichannel neural signals. Wheeler's work focuses on technologies for studying small neuronal networks through micropatterning the growth of individual neurons, microelectrode array fabrication, and neural signal processing. He also works on hearing-related technologies, such as signal processing for smart hearing aids and the controlled growth of neurons for cochlear prostheses, with a broader focus on biomedical engineering and signal processing. Throughout his career, Wheeler has received numerous awards and honors, including being named a Fellow of the American Institute for Medical and Biological Engineering in 2004 and a Fellow of IEEE in 2008. His contributions have significantly advanced the understanding and development of neural interface technologies and biomedical engineering applications.

Research topics

• Computer science
• Neuroscience
• Materials science
• Nanotechnology
• Chemistry

Selected publications

• Advancing Science, Improving Health, and Saving Lives in an Evolving Research Landscape

  Nicotine & Tobacco Research · 2025-05-02 · 3 citations

  articleOpen access
  PublisherOA PDFDOI

• Panel Session: Preparing a Competitive Nomination for IEEE and ASEE Fellow and Other Competitive Awards

  2024-10-13

  article

  In this session, a panel of members of the current Fellows Committee of the IEEE Education Society will share advice on preparing nominations for Fellow of the IEEE and Fellow of the American Society for Engineering Education. The panelists will also provide advice on applications for competitive national and international awards. The panelists will explain how to enhance career planning to become eligible for awards and honors.

  PublisherDOI

• The Flow of Axonal Information Among Hippocampal Subregions: 1. Feed-Forward and Feedback Network Spatial Dynamics Underpinning Emergent Information Processing

  Frontiers in Neural Circuits · 2021-08-27 · 24 citations

  articleOpen access

  The tri-synaptic pathway in the mammalian hippocampus enables cognitive learning and memory. Despite decades of reports on anatomy and physiology, the functional architecture of the hippocampal network remains poorly understood in terms of the dynamics of axonal information transfer between subregions. Information inputs largely flow from the entorhinal cortex (EC) to the dentate gyrus (DG), and then are processed further in the CA3 and CA1 before returning to the EC. Here, we reconstructed elements of the rat hippocampus in a novel device over an electrode array that allowed for monitoring the directionality of individual axons between the subregions. The direction of spike propagation was determined by the transmission delay of the axons recorded between two electrodes in microfluidic tunnels. The majority of axons from the EC to the DG operated in the feed-forward direction, with other regions developing unexpectedly large proportions of feedback axons to balance excitation. Spike timing in axons between each region followed single exponential log-log distributions over two orders of magnitude from 0.01 to 1 s, indicating that conventional descriptors of mean firing rates are misleading assumptions. Most of the spiking occurred in bursts that required two exponentials to fit the distribution of inter-burst intervals. This suggested the presence of up-states and down-states in every region, with the least up-states in the DG to CA3 feed-forward axons and the CA3 subregion. The peaks of the log-normal distributions of intra-burst spike rates were similar in axons between regions with modes around 95 Hz distributed over an order of magnitude. Burst durations were also log-normally distributed around a peak of 88 ms over two orders of magnitude. Despite the diversity of these spike distributions, spike rates from individual axons were often linearly correlated to subregions. These linear relationships enabled the generation of structural connectivity graphs, not possible previously without the directional flow of axonal information. The rich axonal spike dynamics between subregions of the hippocampus reveal both constraints and broad emergent dynamics of hippocampal architecture. Knowledge of this network architecture may enable more efficient computational artificial intelligence (AI) networks, neuromorphic hardware, and stimulation and decoding from cognitive implants.

  PublisherDOI

• The flow of axonal information between hippocampal subregions. 1. Feedforward and feedback network spatial dynamics underpinning emergent information processing

  Zenodo (CERN European Organization for Nuclear Research) · 2021-04-10

  datasetOpen access

  The trisynaptic pathway in the mammalian hippocampus enables cognitive learning and memory. Despite decades of reports on the anatomy and physiology, the functional architecture of the hippocampal network remains poorly understood in terms of the dynamics of axonal information transfer between subregions. Information largely flows from the entorhinal cortical (EC) inputs into the dentate gyrus (DG) and further processing in the CA3 and CA1 before returning to the EC. Here we reconstructed elements of the rat hippocampus in a novel device over an electrode array that allowed monitoring the directionality of individual axons between the subregions. After three weeks, the network developed robust firing of action potential spikes in each hippocampal region with isolated axons communicating between subregions. The direction of spike propagation was determined by the transmission delay of the axons recorded between two electrodes in the microfluidic tunnels. The majority of axons from the EC to DG operated in the feedforward direction, with other regions developing unexpectedly large proportions of feedback axons to balance excitation. Spike timing in axons between each region followed single exponential log-log distributions over two orders of magnitude from 0.01 to 1 s indicating that conventional descriptors of mean firing rates are misleading assumptions. Most of the spiking occurred in bursts that required two exponentials to fit the distribution of interburst intervals. This suggested the presence of up states and down states in every region, with the least up states in the DG to CA3 feedforward axons and the CA3 subregion. The peaks of the lognormal distributions of intraburst spike rates were similar in axons between regions with modes around 95 Hz distributed over an order of magnitude. Burst durations were also lognormally distributed around a peak of 88 ms over two orders of magnitude. Despite the diversity of these spike distributions, spike rates from individual axons were often linearly correlated to subregions. These linear relationships enabled generation of structural connectivity graphs not previously possible without the directional flow of axonal information. The rich axonal spike dynamics between subregions of the hippocampus reveal both constraints and broad emergent dynamics of hippocampal architecture. Knowledge of this network architecture may enable more efficient computational AI networks, neuromorphic hardware as well as suggest patterns for human brain stimulation and decoding from cognitive implants.

  PublisherDOI

• The flow of axonal information between hippocampal subregions. 1. Feedforward and feedback network spatial dynamics underpinning emergent information processing

  Zenodo (CERN European Organization for Nuclear Research) · 2021-04-10

  datasetOpen access

  The trisynaptic pathway in the mammalian hippocampus enables cognitive learning and memory. Despite decades of reports on the anatomy and physiology, the functional architecture of the hippocampal network remains poorly understood in terms of the dynamics of axonal information transfer between subregions. Information largely flows from the entorhinal cortical (EC) inputs into the dentate gyrus (DG) and further processing in the CA3 and CA1 before returning to the EC. Here we reconstructed elements of the rat hippocampus in a novel device over an electrode array that allowed monitoring the directionality of individual axons between the subregions. After three weeks, the network developed robust firing of action potential spikes in each hippocampal region with isolated axons communicating between subregions. The direction of spike propagation was determined by the transmission delay of the axons recorded between two electrodes in the microfluidic tunnels. The majority of axons from the EC to DG operated in the feedforward direction, with other regions developing unexpectedly large proportions of feedback axons to balance excitation. Spike timing in axons between each region followed single exponential log-log distributions over two orders of magnitude from 0.01 to 1 s indicating that conventional descriptors of mean firing rates are misleading assumptions. Most of the spiking occurred in bursts that required two exponentials to fit the distribution of interburst intervals. This suggested the presence of up states and down states in every region, with the least up states in the DG to CA3 feedforward axons and the CA3 subregion. The peaks of the lognormal distributions of intraburst spike rates were similar in axons between regions with modes around 95 Hz distributed over an order of magnitude. Burst durations were also lognormally distributed around a peak of 88 ms over two orders of magnitude. Despite the diversity of these spike distributions, spike rates from individual axons were often linearly correlated to subregions. These linear relationships enabled generation of structural connectivity graphs not previously possible without the directional flow of axonal information. The rich axonal spike dynamics between subregions of the hippocampus reveal both constraints and broad emergent dynamics of hippocampal architecture. Knowledge of this network architecture may enable more efficient computational AI networks, neuromorphic hardware as well as suggest patterns for human brain stimulation and decoding from cognitive implants.

  PublisherDOI

• Asynchronous Learning In The Small Engineering Classroom

  2020-08-31 · 1 citations

  articleOpen accessSenior author

  Two small enrollment engineering courses have been taught using the methodology of the Asynchronous Learning Environment, in which computer networking and conferencing capabilities are used to make student-instructor and student-student interaction more immediate. Included in the effort was the creation of all-electronic assignments, where homework posting, execution, reporting, submission, grading, and return were done with personal computers over the network.

  PublisherOA PDFDOI

• Sparse and Specific Coding during Information Transmission between Co-cultured Dentate Gyrus and CA3 Hippocampal Networks

  UNC Libraries · 2020-04-16

  articleOpen access

  To better understand encoding and decoding of stimulus information in two specific hippocampal sub-regions, we isolated and co-cultured rat primary dentate gyrus (DG) and CA3 neurons within a two-chamber device with axonal connectivity via micro-tunnels. We tested the hypothesis that, in these engineered networks, decoding performance of stimulus site information would be more accurate when stimuli and information flow occur in anatomically correct feed-forward DG to CA3 vs. CA3 back to DG. In particular, we characterized the neural code of these sub-regions by measuring sparseness and uniqueness of the responses evoked by specific paired-pulse stimuli. We used the evoked responses in CA3 to decode the stimulation sites in DG (and vice-versa) by means of learning algorithms for classification (support vector machine, SVM). The device was placed over an 8 × 8 grid of extracellular electrodes (micro-electrode array, MEA) in order to provide a platform for monitoring development, self-organization, and improved access to stimulation and recording at multiple sites. The micro-tunnels were designed with dimensions 3 × 10 × 400 μm allowing axonal growth but not migration of cell bodies and long enough to exclude traversal by dendrites. Paired-pulse stimulation (inter-pulse interval 50 ms) was applied at 22 different sites and repeated 25 times in each chamber for each sub-region to evoke time-locked activity. DG-DG and CA3-CA3 networks were used as controls. Stimulation in DG drove signals through the axons in the tunnels to activate a relatively small set of specific electrodes in CA3 (sparse code). CA3-CA3 and DG-DG controls were less sparse in coding than CA3 in DG-CA3 networks. Using all target electrodes with the three highest spike rates (14%), the evoked responses in CA3 specified each stimulation site in DG with optimum uniqueness of 64%. Finally, by SVM learning, these evoked responses in CA3 correctly decoded the stimulation sites in DG for 43% of the trials, significantly higher than the reverse, i.e., how well-recording in DG could predict the stimulation site in CA3. In conclusion, our co-cultured model for the in vivo DG-CA3 hippocampal network showed sparse and specific responses in CA3, selectively evoked by each stimulation site in DG.

  PublisherDOI

• A Freshman General Education Bioengineering Course On The World Wide Web

  2020-08-31

  articleOpen accessSenior author

  A new bioengineering course, Introduction to Bioengineering: Focus on Medical Imaging, has been designed for non-majors as well as freshman engineers and biologists at the University of Illinois. The course is taught from notes available on the World Wide Web. Computer and written exercises can be submitted via the conferencing or bulletin board software. Asynchronous learning technology is helpful in communicating among instructors students and for coordination of group project work. The educational goal of the new course is to motivate further study

  PublisherOA PDFDOI

• Pattern separation and completion of distinct axonal inputs transmitted via micro-tunnels between co-cultured hippocampal dentate, CA3, CA1 and entorhinal cortex networks

  Journal of Neural Engineering · 2018-04-06 · 32 citations

  articleOpen access

  OBJECTIVE: Functions ascribed to the hippocampal sub-regions for encoding episodic memories include the separation of activity patterns propagated from the entorhinal cortex (EC) into the dentate gyrus (DG) and pattern completion in CA3 region. Since a direct assessment of these functions is lacking at the level of specific axonal inputs, our goal is to directly measure the separation and completion of distinct axonal inputs in engineered pairs of hippocampal sub-regional circuits. APPROACH: We co-cultured EC-DG, DG-CA3, CA3-CA1 or CA1-EC neurons in a two-chamber PDMS device over a micro-electrode array (MEA60), inter-connected via distinct axons that grow through the micro-tunnels between the compartments. Taking advantage of the axonal accessibility, we quantified pattern separation and completion of the evoked activity transmitted through the tunnels from source into target well. Since pattern separation can be inferred when inputs are more correlated than outputs, we first compared the correlations among axonal inputs with those of target somata outputs. We then compared, in an analog approach, the distributions of correlation distances between rate patterns of the axonal inputs inside the tunnels with those of the somata outputs evoked in the target well. Finally, in a digital approach, we measured the spatial population distances between binary patterns of the same axonal inputs and somata outputs. MAIN RESULTS: We found the strongest separation of the propagated axonal inputs when EC was axonally connected to DG, with a decline in separation to CA3 and to CA1 for both rate and digital approaches. Furthermore, the digital approach showed stronger pattern completion in CA3, then CA1 and EC. SIGNIFICANCE: To the best of our knowledge, these are the first direct measures of pattern separation and completion for axonal transmission to the somata target outputs at the rate and digital population levels in each of four stages of the EC-DG-CA3-CA1 circuit.

  PublisherOA PDFDOI

• Narrow microtunnel technology for the isolation and precise identification of axonal communication among distinct hippocampal subregion networks

  PLoS ONE · 2017-05-11 · 21 citations

  articleOpen access

  Communication between different sub regions of the hippocampus is fundamental to learning and memory. However accurate knowledge about information transfer between sub regions from access to the activity in individual axons is lacking. MEMS devices with microtunnels connecting two sub networks have begun to approach this problem but the commonly used 10 μm wide tunnels frequently measure signals from multiple axons. To reduce this complexity, we compared polydimethylsiloxane (PDMS) microtunnel devices each with a separate tunnel width of 2.5, 5 or 10 μm bridging two wells aligned over a multi electrode array (MEA). Primary rat neurons were grown in the chambers with neurons from the dentate gyrus on one side and hippocampal CA3 on the other. After 2-3 weeks of culture, spontaneous activity in the axons inside the tunnels was recorded. We report electrophysiological, exploratory data analysis for feature clustering and visual evidence to support the expectation that 2.5 μm wide tunnels have fewer axons per tunnel and therefore more clearly delineated signals than 10 or 5 μm wide tunnels. Several measures indicated that fewer axons per electrode enabled more accurate detection of spikes. A clustering analysis comparing the variations of spike height and width for different tunnel widths revealed tighter clusters representing unique spikes with less height and width variation when measured in narrow tunnels. Wider tunnels tended toward more diffuse clusters from a continuum of spike heights and widths. Standard deviations for multiple cluster measures, such as Average Dissimilarity, Silhouette Value (S) and Separation Factor (average dissimilarity/S value), support a conclusion that 2.5 μm wide tunnels containing fewer axons enable more precise determination of individual action potential peaks, their propagation direction, timing, and information transfer between sub networks.

  PublisherOA PDFDOI

Recent grants

• NIH Grant R21DC004840

  NIH · $211k · 2003

• Engineering Form and Function in Neuronal Networks

  NIH · $4.1M · 2006–2018

• NIH Grant R03RR006870

  NIH · $49k · 1993

• NIH Grant R21NS038617

  NIH · $244k · 2002

Frequent coauthors

• Gregory J. Brewer

  University of California, Irvine

  63 shared

• Yoonkey Nam

  Korea Institute of Brain Science

  29 shared

• Thomas B. DeMarse

  University of North Carolina at Chapel Hill

  22 shared

• William D. O’Brien

  University of Illinois Urbana-Champaign

  19 shared

• Charissa R. Lansing

  University of Illinois Urbana-Champaign

  18 shared

• Douglas L. Jones

  Alexion Pharmaceuticals (United States)

  17 shared

• Albert S. Feng

  University of Illinois Urbana-Champaign

  17 shared

• Robert C. Bilger

  University of Illinois Urbana-Champaign

  15 shared

Education

• PhD, Electrical Engineering

  Cornell University

  1981

• M.S., Electrical Engineering

  Cornell University

  1977

• S.B., Earth and Planetary Sciences

  Massachusetts Institute of Technology

  1971

• S.B., Humanities

  Massachusetts Institute of Technology

  1971

Awards & honors

• Medical Scholars Program Outstanding Advisor, 2003
• Campus Award for Excellence in Advising Undergraduate Studen…
• Illinois Dad's Association Outstanding Faculty Award 1999
• Honorary Knight of St. Pat -- "for demonstrating leadership…
• In the Incomplete List of Teachers Ranked as Excellent, UIUC…