About

Edward Bartlett is a Professor of Biological Sciences and Biomedical Engineering at Purdue University. His research focuses on biomedical engineering, with particular interest in areas related to biological sciences and biomedical engineering. His contributions include advancing knowledge in these fields through academic and research activities, and he is involved in teaching and mentoring within the department.

Research topics

• Neuroscience
• Psychology
• Biology
• Cognitive psychology

Selected publications

• Focal thalamic infrared neural stimulation propagates dynamical transformations in auditory cortex

  Journal of Neural Engineering · 2025-11-05 · 1 citations

  articleOpen accessSenior authorCorresponding

  Abstract Objective. Infrared neural stimulation (INS) has emerged as a potent neuromodulation technology, offering safe and focal stimulation with superior spatial recruitment profiles compared to conventional electrical methods. However, the neural dynamics induced by INS stimulation remain poorly understood. Elucidating these dynamics will help develop new INS stimulation paradigms and advance its clinical application. Approach. In this study, we assessed the local network dynamics of INS entrainment in the auditory thalamocortical circuit using the chronically implanted rat model. Our approach focused on measuring INS energy-based local field potential (LFP) recruitment induced by focal thalamocortical stimulation. We further characterized linear and nonlinear oscillatory LFP activity in response to single-pulse and periodic INS and performed spectral decomposition to uncover specific LFP band entrainment to INS. Finally, we examined spike-field transformations across the thalamocortical synapse using spike-LFP coherence coupling measures. Main results. We found that INS significantly increases LFP amplitude as a log-linear function of INS energy per pulse, primarily entraining to LFP β and γ bands with synchrony extending to 200 Hz in some cases. A subset of neurons demonstrated nonlinear, chaotic oscillations linked to information transfer across cortical circuits. Finally, we utilized spike-field coherences to correlate spike coupling to LFP frequency band activity and suggest an energy-dependent model of network activation resulting from INS stimulation. Significance. We show that INS reliably drives robust network activity and can potently modulate cortical field potentials across a wide range of frequencies in a stimulus parameter-dependent manner. Based on these results, we propose design principles for developing full coverage, all-optical thalamocortical auditory neuroprostheses.

  PublisherDOI

• Focal Infrared Neural Stimulation Propagates Dynamical Transformations in Auditory Cortex

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-03-13

  preprintOpen accessSenior authorCorresponding

  Significance: Infrared neural stimulation (INS) has emerged as a potent neuromodulation technology, offering safe and focal stimulation with superior spatial recruitment profiles compared to conventional electrical methods. However, the neural dynamics induced by INS stimulation remain poorly understood. Elucidating these dynamics will help develop new INS stimulation paradigms and advance its clinical application. Aim: In this study, we assessed the local network dynamics of INS entrainment in the auditory thalamocortical circuit using the chronically implanted rat model; our approach focused on measuring INS energy-based local field potential (LFP) recruitment induced by focal thalamocortical stimulation. We further characterized linear and nonlinear oscillatory LFP activity in response to single-pulse and periodic INS and performed spectral decomposition to uncover specific LFP band entrainment to INS. Finally, we examined spike-field transformations across the thalamocortical synapse using spike-LFP coherence coupling. Results: bands with synchrony extending to 200 Hz in some cases. A subset of neurons demonstrated nonlinear, chaotic oscillations linked to information transfer across cortical circuits. Finally, we utilized spike-field coherences to correlate spike coupling to LFP frequency band activity and suggest an energy-dependent model of network activation resulting from INS stimulation. Conclusions: We show that INS reliably drives robust network activity and can potently modulate cortical field potentials across a wide range of frequencies in a stimulus parameter-dependent manner. Based on these results, we propose design principles for developing full coverage, all-optical thalamocortical auditory neuroprostheses.

  PublisherOA PDFDOI

• Author response for "Focal thalamic infrared neural stimulation propagates dynamical transformations in auditory cortex"

  2025-10-14

  peer-reviewSenior author
  PublisherDOI

• Protocol for artificial intelligence-guided neural control using deep reinforcement learning and infrared neural stimulation

  STAR Protocols · 2024-12-18 · 3 citations

  articleOpen accessSenior authorCorresponding

  Closed-loop neural control is a powerful tool for both the scientific exploration of neural function and for mitigating deficiencies found in open-loop deep brain stimulation (DBS). Here, we present a protocol for artificial intelligence-guided neural control in rats using deep reinforcement learning (RL) and infrared neural stimulation (INS). We describe steps for integrating RL closed-loop control into neuroscience and neuromodulation studies. We then detail procedures for using and deploying infrared INS in chronic DBS applications. For complete details on the use and execution of this protocol, please refer to Coventry et al. 1 and Coventry and Bartlett. 2 • Perform chronic electrode implantations in rat to facilitate long-term neural stimulation • Perform thalamic infrared neural stimulation and cortical recordings • Use deep reinforcement learning to drive neural firing to desired firing states Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Closed-loop neural control is a powerful tool for both the scientific exploration of neural function and for mitigating deficiencies found in open-loop deep brain stimulation (DBS). Here, we present a protocol for artificial intelligence-guided neural control in rats using deep reinforcement learning (RL) and infrared neural stimulation (INS). We describe steps for integrating RL closed-loop control into neuroscience and neuromodulation studies. We then detail procedures for using and deploying infrared INS in chronic DBS applications.

  PublisherDOI

• Practical Bayesian Inference in Neuroscience: Or How I Learned to Stop Worrying and Embrace the Distribution

  eNeuro · 2024-06-25 · 11 citations

  articleOpen accessSenior author

  Typical statistical practices in the biological sciences have been increasingly called into question due to difficulties in the replication of an increasing number of studies, many of which are confounded by the relative difficulty of null significance hypothesis testing designs and interpretation of p -values. Bayesian inference, representing a fundamentally different approach to hypothesis testing, is receiving renewed interest as a potential alternative or complement to traditional null significance hypothesis testing due to its ease of interpretation and explicit declarations of prior assumptions. Bayesian models are more mathematically complex than equivalent frequentist approaches, which have historically limited applications to simplified analysis cases. However, the advent of probability distribution sampling tools with exponential increases in computational power now allows for quick and robust inference under any distribution of data. Here we present a practical tutorial on the use of Bayesian inference in the context of neuroscientific studies in both rat electrophysiological and computational modeling data. We first start with an intuitive discussion of Bayes' rule and inference followed by the formulation of Bayesian-based regression and ANOVA models using data from a variety of neuroscientific studies. We show how Bayesian inference leads to easily interpretable analysis of data while providing an open-source toolbox to facilitate the use of Bayesian tools.

  PublisherOA PDFDOI

• Characterization and closed-loop control of infrared thalamocortical stimulation produces spatially constrained single-unit responses

  PNAS Nexus · 2024-02-01 · 13 citations

  articleOpen accessSenior author

  Deep brain stimulation (DBS) is a powerful tool for the treatment of circuitopathy-related neurological and psychiatric diseases and disorders such as Parkinson's disease and obsessive-compulsive disorder, as well as a critical research tool for perturbing neural circuits and exploring neuroprostheses. Electrically mediated DBS, however, is limited by the spread of stimulus currents into tissue unrelated to disease course and treatment, potentially causing undesirable patient side effects. In this work, we utilize infrared neural stimulation (INS), an optical neuromodulation technique that uses near to midinfrared light to drive graded excitatory and inhibitory responses in nerves and neurons, to facilitate an optical and spatially constrained DBS paradigm. INS has been shown to provide spatially constrained responses in cortical neurons and, unlike other optical techniques, does not require genetic modification of the neural target. We show that INS produces graded, biophysically relevant single-unit responses with robust information transfer in rat thalamocortical circuits. Importantly, we show that cortical spread of activation from thalamic INS produces more spatially constrained response profiles than conventional electrical stimulation. Owing to observed spatial precision of INS, we used deep reinforcement learning (RL) for closed-loop control of thalamocortical circuits, creating real-time representations of stimulus-response dynamics while driving cortical neurons to precise firing patterns. Our data suggest that INS can serve as a targeted and dynamic stimulation paradigm for both open and closed-loop DBS.

  PublisherDOI

• Rapid and objective assessment of auditory temporal processing using dynamic amplitude-modulated stimuli

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-01-29

  preprintOpen access

  Auditory neural coding of speech-relevant temporal cues can be noninvasively probed using envelope following responses (EFRs), neural ensemble responses phase-locked to the stimulus amplitude envelope. EFRs emphasize different neural generators, such as the auditory brainstem or auditory cortex, by altering the temporal modulation rate of the stimulus. EFRs can be an important diagnostic tool to assess auditory neural coding deficits that go beyond traditional audiometric estimations. Existing approaches to measure EFRs use discrete amplitude modulated (AM) tones of varying modulation frequencies, which is time consuming and inefficient, impeding clinical translation. Here we present a faster and more efficient framework to measure EFRs across a range of AM frequencies using stimuli that dynamically vary in modulation rates, combined with spectrally specific analyses that offer optimal spectrotemporal resolution. EFRs obtained from several species (humans, Mongolian gerbils, Fischer-344 rats, and Cba/CaJ mice) showed robust, high-SNR tracking of dynamic AM trajectories (up to 800Hz in humans, and 1.4 kHz in rodents), with a fivefold decrease in recording time and thirtyfold increase in spectrotemporal resolution. EFR amplitudes between dynamic AM stimuli and traditional discrete AM tokens within the same subjects were highly correlated (94% variance explained) across species. Hence, we establish a time-efficient and spectrally specific approach to measure EFRs. These results could yield novel clinical diagnostics for precision audiology approaches by enabling rapid, objective assessment of temporal processing along the entire auditory neuraxis.

  PublisherOA PDFDOI

• Rapid and objective assessment of auditory temporal processing using dynamic amplitude-modulated stimuli

  Communications Biology · 2024-11-15 · 5 citations

  articleOpen access

  Current tests of hearing fail to diagnose pathologies in ~10% of patients seeking help for hearing difficulties. Neural ensemble responses to perceptually relevant cues in the amplitude envelope, termed envelope following responses (EFR), hold promise as an objective diagnostic tool to probe these ‘hidden’ hearing difficulties. But clinical translation is impeded by current measurement approaches involving static amplitude modulated (AM) tones, which are time-consuming and lack optimal spectrotemporal resolution. Here we develop a framework to rapidly measure EFRs using dynamically varying AMs combined with spectrally specific analyses. These analyses offer 5x improvement in time and 30x improvement in spectrotemporal resolution, and more generally, are optimal for analyzing time-varying signals with known spectral trajectories of interest. We validate this approach across several mammalian species, including humans, and demonstrate robust responses that are highly correlated with traditional static EFRs. Our analytic technique facilitates rapid and objective neural assessment of temporal processing throughout the brain that can be applied to track auditory neurodegeneration using EFRs, as well as tracking recovery after therapeutic interventions. Combining dynamic amplitude modulated stimuli with spectrally specific analyses yield rapid and robust diagnostic metrics for neural assessment of auditory temporal processing across multiple mammalian species.

  PublisherOA PDFDOI

• Neurometric amplitude modulation detection in the inferior colliculus of Young and Aged rats

  Hearing Research · 2024-05-03 · 6 citations

  articleOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Practical Bayesian Inference in Neuroscience: Or How I Learned To Stop Worrying and Embrace the Distribution

  bioRxiv (Cold Spring Harbor Laboratory) · 2023-11-21

  preprintOpen accessSenior authorCorresponding

  ABSTRACT Typical statistical practices in the biological sciences have been increasingly called into question due to difficulties in replication of an increasing number of studies, many of which are confounded by the relative difficulty of null significance hypothesis testing designs and interpretation of p-values. Bayesian inference, representing a fundamentally different approach to hypothesis testing, is receiving renewed interest as a potential alternative or complement to traditional null significance hypothesis testing due to its ease of interpretation and explicit declarations of prior assumptions. Bayesian models are more mathematically complex than equivalent frequentist approaches, which have historically limited applications to simplified analysis cases. However, the advent of probability distribution sampling tools with exponential increases in computational power now allows for quick and robust inference under any distribution of data. Here we present a practical tutorial on the use of Bayesian inference in the context of neuroscientific studies. We first start with an intuitive discussion of Bayes’ rule and inference followed by the formulation of Bayesian-based regression and ANOVA models using data from a variety of neuroscientific studies. We show how Bayesian inference leads to easily interpretable analysis of data while providing an open-source toolbox to facilitate the use of Bayesian tools. Significance Statement Bayesian inference has received renewed interest as an alternative to null-significance hypothesis testing for its interpretability, ability to incorporate prior knowledge into current inference, and robust model comparison paradigms. Despite this renewed interest, discussions of Bayesian inference are often obfuscated by undue mathematical complexity and misunderstandings underlying the Bayesian inference process. In this article, we aim to empower neuroscientists to adopt Bayesian statistical inference by providing a practical methodological walkthrough using single and multi-unit recordings from the rodent auditory circuit accompanied by a well-documented and user-friendly toolkit containing regression and ANOVA statistical models commonly encountered in neuroscience.

  PublisherOA PDFDOI

Recent grants

• NIH Grant R03DC006357

  NIH · $245k · 2006

• NIH Grant R01DC011580

  NIH · $1.7M · 2019

Frequent coauthors

• Aravindakshan Parthasarathy

  University of Pittsburgh

  22 shared

• Brandon S. Coventry

  University of Wisconsin–Madison

  11 shared

• Daniel Bendor

  University College London

  10 shared

• Jesyin Lai

  St. Jude Children's Research Hospital

  9 shared

• Tong Lu

  Nanjing University

  8 shared

• X Wang

  Johns Hopkins University

  8 shared

• Philip H. Smith

  University of Wisconsin–Madison

  6 shared

• Xiaoqin Wang

  Zhejiang Normal University

  5 shared

Labs

• Weldon School of Biomedical EngineeringPI