Frank W Ohl

Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Bavaria, Germany

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Publications (101)349.7 Total impact

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    ABSTRACT: The receptive field (RF) represents the signal preferences of sensory neurons and is the primary analysis method for understanding sensory coding. While it is essential to estimate a neurons RF, finding numerical solutions to increasingly complex RF models can become computationally intensive, in particular for high-dimensional stimuli or if many neurons are involved. Here we propose an optimization scheme based on stochastic approximations that facilitates this task. The basic idea is to derive solutions on a random subset rather than computing the full solution on the available data set. To test this, we applied different optimization schemes based on stochastic gradient descent (SGD) to both the generalized linear model (GLM) and a recently developed classification-based RF estimation approach. Using simulated and recorded responses, we demonstrate that RF parameter optimization based on state-of-the-art SGD algorithms produces robust estimates of the spectro-temporal receptive field (STRF). Results on recordings from the auditory midbrain demonstrate that stochastic approximations preserve both predictive power and tuning properties of STRFs. A correlation of 0.93 with the STRF derived from the full solution may be obtained in less than 10 % of the full solution's estimation time. We also present an on-line algorithm that allows simultaneous monitoring of STRF properties of more than 30 neurons on a single computer. The proposed approach may not only prove helpful for large-scale recordings but also provides a more comprehensive characterization of neural tuning in experiments than standard tuning curves. Copyright © 2015 Elsevier B.V. All rights reserved.
  • European Journal of Neuroscience 03/2015; 41(5):515-7. DOI:10.1111/ejn.12832 · 3.67 Impact Factor
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    ABSTRACT: In this study, we describe differences between neural plasticity in auditory cortex (AC) of animals that developed subjective tinnitus (group T) after noise-induced hearing loss (NIHL) compared to those that did not [group non-tinnitus (NT)]. To this end, our analysis focuses on the input activity of cortical neurons based on the temporal and spectral analysis of local field potential (LFP) recordings and an in-depth analysis of auditory brainstem responses (ABR) in the same animals. In response to NIHL in NT animals we find a significant general reduction in overall cortical activity and spectral power as well as changes in all ABR wave amplitudes as a function of loudness. In contrast, T-animals show no significant change in overall cortical activity as assessed by root mean square analysis of LFP amplitudes, but a specific increase in LFP spectral power and in the amplitude of ABR wave V reflecting activity in the inferior colliculus (IC). Based on these results, we put forward a refined model of tinnitus prevention after NIHL that acts via a top-down global (i.e., frequency-unspecific) inhibition reducing overall neuronal activity in AC and IC, thereby counteracting NIHL-induced bottom-up frequency-specific neuroplasticity suggested in current models of tinnitus development.
    Frontiers in Neurology 02/2015; 6(22). DOI:10.3389/fneur.2015.00022
  • Frank W Ohl
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    ABSTRACT: Rhythmic activity appears in the auditory cortex in both microscopic and macroscopic observables and is modulated by both bottom-up and top-down processes. How this activity serves both types of processes is largely unknown. Here we review studies that have recently improved our understanding of potential functional roles of large-scale global dynamic activity patterns in auditory cortex. The experimental paradigm of auditory category learning allowed critical testing of the hypothesis that global auditory cortical activity states are associated with endogenous cognitive states mediating the meaning associated with an acoustic stimulus rather than with activity states that merely represent the stimulus for further processing.
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    ABSTRACT: Temporal variability of neuronal response characteristics during sensory stimulation is a ubiquitous phenomenon that may reflect processes such as stimulus-driven adaptation, top-down modulation or spontaneous fluctuations. It poses a challenge to functional characterization methods such as the receptive field, since these often assume stationarity. We propose a novel method for estimation of sensory neurons' receptive fields that extends the classic static linear receptive field model to the time-varying case. Here, the long-term estimate of the static receptive field serves as the mean of a probabilistic prior distribution from which the short-term temporally localized receptive field may deviate stochastically with time-varying standard deviation. The derived corresponding generalized linear model permits robust characterization of temporal variability in receptive field structure also for highly non-Gaussian stimulus ensembles. We computed and analyzed short-term auditory spectro-temporal receptive field (STRF) estimates with characteristic temporal resolution 5 s to 30 s based on model simulations and responses from in total 60 single-unit recordings in anesthetized Mongolian gerbil auditory midbrain and cortex. Stimulation was performed with short (100 ms) overlapping frequency-modulated tones. Results demonstrate identification of time-varying STRFs, with obtained predictive model likelihoods exceeding those from baseline static STRF estimation. Quantitative characterization of STRF variability reveals a higher degree thereof in auditory cortex compared to midbrain. Cluster analysis indicates that significant deviations from the long-term static STRF are brief, but reliably estimated. We hypothesize that the observed variability more likely reflects spontaneous or state-dependent internal fluctuations that interact with stimulus-induced processing, rather than experimental or stimulus design.
    Frontiers in Computational Neuroscience 12/2014; 8(165). DOI:10.3389/fncom.2014.00165 · 2.23 Impact Factor
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    Frontiers in Behavioral Neuroscience 10/2014; 8. DOI:10.3389/fnbeh.2014.00372 · 4.16 Impact Factor
  • 1 st Conference on Image-Guided Interventions (IGIC), Magdeburg; 10/2014
  • Frank W Ohl
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    ABSTRACT: Rhythmic activity appears in the auditory cortex in both microscopic and macroscopic observables and is modulated by both bottom-up and top-down processes. How this activity serves both types of processes is largely unknown. Here we review studies that have recently improved our understanding of potential functional roles of large-scale global dynamic activity patterns in auditory cortex. The experimental paradigm of auditory category learning allowed critical testing of the hypothesis that global auditory cortical activity states are associated with endogenous cognitive states mediating the meaning associated with an acoustic stimulus rather than with activity states that merely represent the stimulus for further processing.
    Current Opinion in Neurobiology 09/2014; 31C:88-94. DOI:10.1016/j.conb.2014.08.014 · 6.77 Impact Factor
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    ABSTRACT: Electrical and optogenetic methods for brain stimulation are widely used in rodents for manipulating behavior and analyzing functional connectivities in neuronal circuits. High-resolution in vivo imaging of the global, brain-wide, activation patterns induced by these stimulations has remained challenging, in particular in awake behaving mice. We here mapped brain activation patterns in awake, intracranially self-stimulating mice using a novel protocol for single-photon emission computed tomography (SPECT) imaging of regional cerebral blood flow (rCBF). Mice were implanted with either electrodes for electrical stimulation of the medial forebrain bundle (mfb-microstim) or with optical fibers for blue-light stimulation of channelrhodopsin-2 expressing neurons in the ventral tegmental area (vta-optostim). After training for self-stimulation by current or light application, respectively, mice were implanted with jugular vein catheters and intravenously injected with the flow tracer 99m-technetium hexamethylpropyleneamine oxime (99mTc-HMPAO) during seven to ten minutes of intracranial self-stimulation or ongoing behavior without stimulation. The 99mTc-brain distributions were mapped in anesthetized animals after stimulation using multipinhole SPECT. Upon self-stimulation rCBF strongly increased at the electrode tip in mfb-microstim mice. In vta-optostim mice peak activations were found outside the stimulation site. Partly overlapping brain-wide networks of activations and deactivations were found in both groups. When testing all self-stimulating mice against all controls highly significant activations were found in the rostromedial nucleus accumbens shell. SPECT-imaging of rCBF using intravenous tracer-injection during ongoing behavior is a new tool for imaging regional brain activation patterns in awake behaving rodents providing higher spatial and temporal resolutions than 18F-2-fluoro-2-dexoyglucose positron emission tomography.
    NeuroImage 09/2014; DOI:10.1016/j.neuroimage.2014.09.023 · 6.13 Impact Factor
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    ABSTRACT: It is commonly assumed that cortical activity in non-rapid eye movement sleep (NREMS) is spatially homogeneous on the mesoscopic scale. This is partly due to the limited observational scope of common metabolic or imaging methods in sleep. We used the recently developed technique of thallium-autometallography (TlAMG) to visualize mesoscopic patterns of activity in the sleeping cortex with single-cell resolution. We intravenously injected rats with the lipophilic chelate complex thallium diethyldithiocarbamate (TlDDC) during spontaneously occurring periods of NREMS and mapped the patterns of neuronal uptake of the potassium (K(+)) probe thallium (Tl(+)). Using this method, we show that cortical activity patterns are not spatially homogeneous during discrete 5-min episodes of NREMS in unrestrained rats-rather, they are complex and spatially diverse. Along with a relative predominance of infragranular layer activation, we find pronounced differences in metabolic activity of neighboring neuronal assemblies, an observation which lends support to the emerging paradigm that sleep is a distributed process with regulation on the local scale.
    Brain Structure and Function 08/2014; DOI:10.1007/s00429-014-0867-9 · 7.84 Impact Factor
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    ABSTRACT: Knowledge about the anatomical organization of the auditory thalamocortical (TC) system is fundamental for the understanding of auditory information processing in the brain. In the Mongolian gerbil (Meriones unguiculatus), a valuable model species in auditory research, the detailed anatomy of this system has not yet been worked out in detail. Here, we investigated the projections from the three subnuclei of the medial geniculate body (MGB), namely its ventral (MGv), dorsal (MGd), and medial (MGm) division as well as from several of their subdivisions (MGv: pars lateralis LV, pars ovoidea OV, rostral pole RP; MGd: deep dorsal nucleus DD), to the auditory cortex (AC) by stereotaxic pressure-injections and electrophysiologically-guided iontophoretic injections of the anterograde tract tracer biocytin. Our data reveal highly specific features of the TC connections regarding their nuclear origin in the subdivisions of the MGB and their termination patterns in the auditory cortical fields and layers. Besides tonotopically organized projections, primarily of LV, OV, and DD to the AC, a large number of axons diverge across the tonotopic gradient. These originate mainly from RP, MGd (proper), and MGm. In particular, neurons of the MGm project in a columnar fashion to several auditory fields, forming small- and medium-sized boutons, and also hitherto unknown giant terminals. The distinctive layer-specific distribution of axonal endings within the AC indicates that each of the TC connectivity systems has a specific function in auditory cortical processing. J. Comp. Neurol., 2014. © 2014 Wiley Periodicals, Inc.
    The Journal of Comparative Neurology 07/2014; 522(10). DOI:10.1002/cne.23540 · 3.51 Impact Factor
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    ABSTRACT: Analysis of sensory neurons' processing characteristics requires simultaneous measurement of presented stimuli and concurrent spike responses. The functional transformation from high-dimensional stimulus space to the binary space of spike and non-spike responses is commonly described with linear-nonlinear models, whose linear filter component describes the neuron's receptive field. From a machine learning perspective, this corresponds to the binary classification problem of discriminating spike-eliciting from non-spike-eliciting stimulus examples. The classification-based receptive field (CbRF) estimation method proposed here adapts a linear large-margin classifier to optimally predict experimental stimulus-response data and subsequently interprets learned classifier weights as the neuron's receptive field filter. Computational learning theory provides a theoretical framework for learning from data and guarantees optimality in the sense that the risk of erroneously assigning a spike-eliciting stimulus example to the non-spike class (and vice versa) is minimized. Efficacy of the CbRF method is validated with simulations and for auditory spectro-temporal receptive field (STRF) estimation from experimental recordings in the auditory midbrain of Mongolian gerbils. Acoustic stimulation is performed with frequency-modulated tone complexes that mimic properties of natural stimuli, specifically non-Gaussian amplitude distribution and higher-order correlations. Results demonstrate that the proposed approach successfully identifies correct underlying STRFs, even in cases where second-order methods based on the spike-triggered average (STA) do not. Applied to small data samples, the method is shown to converge on smaller amounts of experimental recordings and with lower estimation variance than the generalized linear model and recent information theoretic methods. Thus, CbRF estimation may prove useful for investigation of neuronal processes in response to natural stimuli and in settings where rapid adaptation is induced by experimental design.
    PLoS ONE 04/2014; 9(4):e93062. DOI:10.1371/journal.pone.0093062 · 3.53 Impact Factor
  • Lars T. Boenke, David Alais, Frank W. Ohl
    Procedia - Social and Behavioral Sciences 03/2014; 126:164-165. DOI:10.1016/j.sbspro.2014.02.356
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    ABSTRACT: During brain maturation, the occurrence of the extracellular matrix (ECM) terminates juvenile plasticity by mediating structural stability. Interestingly, enzymatic removal of the ECM restores juvenile forms of plasticity, as for instance demonstrated by topographical reconnectivity in sensory pathways. However, to which degree the mature ECM is a compromise between stability and flexibility in the adult brain impacting synaptic plasticity as a fundamental basis for learning, lifelong memory formation, and higher cognitive functions is largely unknown. In this study, we removed the ECM in the auditory cortex of adult Mongolian gerbils during specific phases of cortex-dependent auditory relearning, which was induced by the contingency reversal of a frequency-modulated tone discrimination, a task requiring high behavioral flexibility. We found that ECM removal promoted a significant increase in relearning performance, without erasing already established-that is, learned-capacities when continuing discrimination training. The cognitive flexibility required for reversal learning of previously acquired behavioral habits, commonly understood to mainly rely on frontostriatal circuits, was enhanced by promoting synaptic plasticity via ECM removal within the sensory cortex. Our findings further suggest experimental modulation of the cortical ECM as a tool to open short-term windows of enhanced activity-dependent reorganization allowing for guided neuroplasticity.
    Proceedings of the National Academy of Sciences 02/2014; 111(7):2800-5. DOI:10.1073/pnas.1310272111 · 9.81 Impact Factor
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    ABSTRACT: Dopaminergic neurotransmission in primary auditory cortex (AI) has been shown to be involved in learning and memory functions. Moreover, dopaminergic projections and D1/D5 receptor distributions display a layer-dependent organization, suggesting specific functions in the cortical circuitry. However, the circuit effects of dopaminergic neurotransmission in sensory cortex and their possible roles in perception, learning, and memory are largely unknown. Here, we investigated layer-specific circuit effects of dopaminergic neuromodulation using current source density (CSD) analysis in AI of Mongolian gerbils. Pharmacological stimulation of D1/D5 receptors increased auditory-evoked synaptic currents in infragranular layers, prolonging local thalamocortical input via positive feedback between infragranular output and granular input. Subsequently, dopamine promoted sustained cortical activation by prolonged recruitment of long-range corticocortical networks. A detailed circuit analysis combining layer-specific intracortical microstimulation (ICMS), CSD analysis, and pharmacological cortical silencing revealed that cross-laminar feedback enhanced by dopamine relied on a positive, fast-acting recurrent corticoefferent loop, most likely relayed via local thalamic circuits. Behavioral signal detection analysis further showed that activation of corticoefferent output by infragranular ICMS, which mimicked auditory activation under dopaminergic influence, was most effective in eliciting a behaviorally detectable signal. Our results show that D1/D5-mediated dopaminergic modulation in sensory cortex regulates positive recurrent corticoefferent feedback, which enhances states of high, persistent activity in sensory cortex evoked by behaviorally relevant stimuli. In boosting horizontal network interactions, this potentially promotes the readout of task-related information from cortical synapses and improves behavioral stimulus detection.
    The Journal of Neuroscience : The Official Journal of the Society for Neuroscience 01/2014; 34(4):1234-47. DOI:10.1523/JNEUROSCI.1990-13.2014 · 6.75 Impact Factor
  • Neuroscience 12/2013; 254:97. DOI:10.1016/j.neuroscience.2013.09.040 · 3.33 Impact Factor
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Publication Stats

2k Citations
349.70 Total Impact Points

Institutions

  • 2015
    • Friedrich-Alexander Universität Erlangen-Nürnberg
      Erlangen, Bavaria, Germany
  • 1998–2015
    • Leibniz Institute for Neurobiology
      • • Department of Systemphysiology of Learning
      • • Department of Neurochemistry and Molecular Biology
      Magdeburg, Saxony-Anhalt, Germany
  • 2007–2014
    • Otto-von-Guericke-Universität Magdeburg
      • Institute of Biology (IBIO)
      Magdeburg, Saxony-Anhalt, Germany
  • 2009
    • Bernstein Center for Computational Neuroscience Berlin
      Berlín, Berlin, Germany
  • 1998–2000
    • University of California, Berkeley
      • Division of Neurobiology
      Berkeley, CA, United States
  • 1996
    • University of California, Irvine
      • Center for the Neurobiology of Learning & Memory
      Irvine, California, United States
  • 1995
    • Darmstadt University of Applied Sciences
      Darmstadt, Hesse, Germany