Input-output Function Research Articles

Canonical transient receptor potential channel subtype 3‐mediated hair cell Ca2+ entry regulates sound transduction and auditory neurotransmission

The physiological significance of canonical transient receptor potential (TRPC) ion channels in sensory systems is rapidly emerging. Heterologous expression studies show that TRPC3 is a significant Ca(2+) entry pathway, with dual activation via G protein-coupled receptor (GPCR)-phospholipase C-diacylglycerol second messenger signaling, and through negative feedback, whereby a fall in cytosolic Ca(2+) releases Ca(2+) -calmodulin channel block. We hypothesised that the latter process contributes to cochlear hair cell cytosolic Ca(2+) homeostasis. Confocal microfluorimetry with the Ca(2+) indicator Fluo-4 acetoxymethylester showed that, when cytosolic Ca(2+) was depleted, Ca(2+) re-entry was significantly impaired in mature TRPC3(-/-) inner and outer hair cells. The impact of this disrupted Ca(2+) homeostasis on sound transduction was assessed with the use of distortion product otoacoustic emissions (DPOAEs), which constitute a direct measure of the outer hair cell transduction that underlies hearing sensitivity and frequency selectivity. TRPC3(-/-) mice showed significantly stronger DPOAE (2f1 -f2 ) growth functions than wild-type (WT) littermates within the frequency range of best hearing acuity. This translated to hyperacusis (decreased threshold) measured by the auditory brainstem response (ABR). TRPC3(-/-) and WT mice did not differ in the levels of temporary and permanent threshold shift arising from noise exposure, indicating that potential GPCR signaling via TRPC3 is not pronounced. Overall, these data suggest that the Ca(2+) set-point in the hair cell, and hence membrane conductance, is modulated by TRPC3s through their function as a negative feedback-regulated Ca(2+) entry pathway. This TPRC3-regulated Ca(2+) homeostasis shapes the sound transduction input-output function and auditory neurotransmission.

Transfer Functions for Protein Signal Transduction: Application to a Model of Striatal Neural Plasticity

We present a novel formulation for biochemical reaction networks in the context of protein signal transduction. The model consists of input-output transfer functions, which are derived from differential equations, using stable equilibria. We select a set of “source” species, which are interpreted as input signals. Signals are transmitted to all other species in the system (the “target” species) with a specific delay and with a specific transmission strength. The delay is computed as the maximal reaction time until a stable equilibrium for the target species is reached, in the context of all other reactions in the system. The transmission strength is the concentration change of the target species. The computed input-output transfer functions can be stored in a matrix, fitted with parameters, and even recalled to build dynamical models on the basis of state changes. By separating the temporal and the magnitudinal domain we can greatly simplify the computational model, circumventing typical problems of complex dynamical systems. The transfer function transformation of biochemical reaction systems can be applied to mass-action kinetic models of signal transduction. The paper shows that this approach yields significant novel insights while remaining a fully testable and executable dynamical model for signal transduction. In particular we can deconstruct the complex system into local transfer functions between individual species. As an example, we examine modularity and signal integration using a published model of striatal neural plasticity. The modularizations that emerge correspond to a known biological distinction between calcium-dependent and cAMP-dependent pathways. Remarkably, we found that overall interconnectedness depends on the magnitude of inputs, with higher connectivity at low input concentrations and significant modularization at moderate to high input concentrations. This general result, which directly follows from the properties of individual transfer functions, contradicts notions of ubiquitous complexity by showing input-dependent signal transmission inactivation.

Robustness, Evolvability, and the Logic of Genetic Regulation

In gene regulatory circuits, the expression of individual genes is commonly modulated by a set of regulating gene products, which bind to a gene's cis-regulatory region. This region encodes an input-output function, referred to as signal-integration logic, that maps a specific combination of regulatory signals (inputs) to a particular expression state (output) of a gene. The space of all possible signal-integration functions is vast and the mapping from input to output is many-to-one: For the same set of inputs, many functions (genotypes) yield the same expression output (phenotype). Here, we exhaustively enumerate the set of signal-integration functions that yield identical gene expression patterns within a computational model of gene regulatory circuits. Our goal is to characterize the relationship between robustness and evolvability in the signal-integration space of regulatory circuits, and to understand how these properties vary between the genotypic and phenotypic scales. Among other results, we find that the distributions of genotypic robustness are skewed, so that the majority of signal-integration functions are robust to perturbation. We show that the connected set of genotypes that make up a given phenotype are constrained to specific regions of the space of all possible signal-integration functions, but that as the distance between genotypes increases, so does their capacity for unique innovations. In addition, we find that robust phenotypes are (i) evolvable, (ii) easily identified by random mutation, and (iii) mutationally biased toward other robust phenotypes. We explore the implications of these latter observations for mutation-based evolution by conducting random walks between randomly chosen source and target phenotypes. We demonstrate that the time required to identify the target phenotype is independent of the properties of the source phenotype.

Physical Realizations: Transforming into Physical Embodiments of Concepts in the Design of Mechanical Movements

Conceptual design involves identification of required functions of the intended design, generation of concepts to fulfill these functions, and evaluation of these concepts to select the most promising ones for further development. The focus of this paper is the second phase—concept generation, in which a challenge has been to develop possible physical embodiments to offer designers for exploration and evaluation. This paper investigates the issue of how to transform and thus synthesise possible generic physical embodiments and reports an implemented method that could automatically generate these embodiments. In this paper, a method is proposed to transform a variety of possible initial solutions to a design problem into a set of physical solutions that are described in terms of abstraction of mechanical movements. The underlying principle of this method is to make it possible to link common attributes between a specific abstract representation and its possible physical objects. For a given input, this method can produce a set of concepts in terms of their generic physical embodiments. The method can be used to support designers to start with a given input-output function and systematically search for physical objects for design consideration in terms of simplified functional, spatial, and mechanical movement requirements.

Evidence of plasticity in the pontocerebellar conditioned stimulus pathway during classical conditioning of the eyeblink response in the rabbit.

Electrical stimulation thresholds required to elicit eyeblinks with either pontine or cerebellar interpositus stimulation were measured before and after classical eyeblink conditioning with paired pontine stimulation (conditioned stimulus, CS) and corneal airpuff (unconditioned stimulus, US). Pontine stimulation thresholds dropped dramatically after training and returned to baseline levels following extinction, whereas interpositus thresholds and input-output functions remained stable across training sessions. Learning rate, magnitude of threshold change, and electrode placements were correlated. Pontine projection patterns to the cerebellum were confirmed with retrograde labeling techniques. These results add to the body of literature suggesting that the pons relays CS information to the cerebellum and provide further evidence of synaptic plasticity in the cerebellar network.

Real-time Identification of different types of non-holonomic mobile robots

This paper presents the real-time identification of different types of non-holonomic mobile robot systems. Since the robot type is a priori unknown, the robot systems are formulated as a switched singular nonlinear system, and the problem becomes the real-time identification of the switching signal, and then the existence of the input-output functions and the distinguishability of the system are studied. We show in the simulations that the proposed technique is implemented easily and effectively, and it is robust to the noises as well.

Stabilization of Memory States by Stochastic Facilitating Synapses

Bistability within a small neural circuit can arise through an appropriate strength of excitatory recurrent feedback. The stability of a state of neural activity, measured by the mean dwelling time before a noise-induced transition to another state, depends on the neural firing-rate curves, the net strength of excitatory feedback, the statistics of spike times, and increases exponentially with the number of equivalent neurons in the circuit. Here, we show that such stability is greatly enhanced by synaptic facilitation and reduced by synaptic depression. We take into account the alteration in times of synaptic vesicle release, by calculating distributions of inter-release intervals of a synapse, which differ from the distribution of its incoming interspike intervals when the synapse is dynamic. In particular, release intervals produced by a Poisson spike train have a coefficient of variation greater than one when synapses are probabilistic and facilitating, whereas the coefficient of variation is less than one when synapses are depressing. However, in spite of the increased variability in postsynaptic input produced by facilitating synapses, their dominant effect is reduced synaptic efficacy at low input rates compared to high rates, which increases the curvature of neural input-output functions, leading to wider regions of bistability in parameter space and enhanced lifetimes of memory states. Our results are based on analytic methods with approximate formulae and bolstered by simulations of both Poisson processes and of circuits of noisy spiking model neurons.

Two-source interference as the major reason for auditory-threshold estimation error based on DPOAE input–output functions in normal-hearing subjects

Fine structure in the frequency response of distortion product otoacoustic emissions (DPOAEs) can severely limit the usefulness of DPOAEs in estimating auditory thresholds. Here, fine structure is removed by extracting the primary-source DPOAE component using the onset-decomposition technique (Vetešník et al., 2009) and auditory threshold estimates are compared to those obtained from DPOAEs in response to conventional, continuous two-tone stimulation. Auditory thresholds are predicted using the estimated distortion product thresholds (EDPTs), obtained from linear regression of input–output (I/O) functions of DPOAE pressure amplitude versus second-tone stimulus level (Boege and Janssen, 2002). The accuracy of the auditory-threshold predictions is derived by comparison with measured auditory thresholds. The parameters of the two primary stimulus tones of frequency f1 and f2 and levels of L1 and L2 are chosen as: f2/f1 = 1.2 with 1.5 ≤ f2 ≤ 2.5 kHz, and L1 = 0.4L2 + 39 dB SPL, with 25 ≤ L2 ≤ 65 dB SPL. Data are from 12 normal-hearing subjects with profound DPOAE fine structure. 255 DPOAE I/O functions were measured for each of the two DPOAE paradigms. An EDPT value was accepted as reliable if: 1) the squared correlation coefficient, r2 ≥ 0.8, 2) the regression slope, sI/O ≥ 0.2 μPa/dB, and 3) the standard deviation of the EDPT, σEDPT ≤ 10 dB. The proportion of rejected I/O functions was 8% for onset-decomposition DPOAEs, and 25% for continuous-tone DPOAEs. Removal of data points from the saturation region of the DPOAE I/O function by an automated algorithm reduced the rejection rate, to zero for onset-decomposition DPOAEs, but to only 13% for continuous-tone DPOAEs. In the absence of saturated DPOAE responses, auditory thresholds were predicted with standard deviation of only 4 dB for onset-decomposition DPOAEs, but 12 dB for continuous-tone DPOAEs. In summary, by extracting the primary-source component of the DPOAE by the method of onset-decomposition it is possible to predict human auditory threshold with hitherto unattainable accuracy.

Defective Casting Diagnosing via an Enhanced Knowledge Hyper-Surface Method

The research on the analysis of cause and effect relationships in castings has always been a centre of attention in the manufacturing industry. An intelligent diagnosis system should be able to diagnose effectively the causal representation and also justify its diagnosis. Recently, a method, known as the Knowledge Hyper-surface method which used Lagrange Interpolation polynomials has gained more popularity in learning cause and effect analysis in casting processes. The current method show that the belief value of the occurrence of cause with respect to the change in the belief value in the occurrence of effect can be modelled by linear, quadratic or cubic relationships and the method retained the advantages of neural networks and overcomes their limitations in learning the input-output mapping function in the presence of noisy, limited and sparse data. However, the methodology was unable to model exponential increase/decrease in belief values in cause and effect relationships. This paper proposed an enhancement to the current Knowledge Hyper-surface method by introducing midpoints in the existing shape formulation which further constrains the shape of the Knowledge hyper-surfaces to model an exponential rise in belief values but without exposing the dataset to the limitations of ‘over fitting’. The ability of the proposed method to capture the exponential change in the belief variation of the cause when the belief in the effect is at its minimum is compared to the current method on real casting data.

Stimulus level effects on speech-evoked obligatory cortical auditory evoked potentials in infants with normal hearing

ObjectiveTo determine stimulus level effects on speech-evoked cortical auditory evoked potentials (CAEPs) in infants for a low (/m/) and high (/t/) frequency speech sound. MethodsCAEPs were recorded for two natural speech tokens, /m/ and /t/. Participants were 16 infants aged 3–8months with no risk factors for hearing impairment, no parental concern regarding hearing or development, and normal tympanograms and otoacoustic emissions. Infants were either tested at levels of 30, 50, and 70dB SPL or at 40, 60, and 80dB SPL, in counterbalanced order. ResultsInput–output functions show different effects of increasing sound level between stimuli. There were minimal changes in latency with increase in level for /t/. For /m/, there were approximately 50–60ms latency increases at soft compared to loud levels. Amplitudes saturated at moderate–high levels (60–80dB SPL) for both stimuli. ConclusionsInfants’ CAEP input–output functions differ for /t/ versus /m/ and differ from those previously reported for adults for other stimuli. Effects of stimulus and level on CAEPs should be considered when using CAEPs for hearing aid or cochlear implant evaluation in infants. SignificanceSpeech-evoked CAEPs provide an objective measure of central auditory processing. Possible differences in CAEP growth between infants and adults suggest developmental effects on intensity coding by the auditory cortex.

Empirical identification of squeeze-film damper bearings using neural networks

To date empirically obtained SFD models have been based upon the determination of linearised force coefficients; such models are severely limited in their range of applicability since they are only valid for small perturbations from a mean position. The present research provides the introduction and validation of a nonlinear SFD identification technique that uses neural networks, trained from experimental data, to reproduce the input–output function over the full range of the SFD clearance. Details of the commissioning of a specially designed identification test rig and its associated data acquisition system are presented. The neural network's construction and training process is described and relevant testing is detailed. The empirically identified neural network is progressively validated, culminating in remarkably accurate nonlinear vibration response prediction of an SFD test rig subjected to external dual-frequency orthogonal excitation, as present in twin-spool engines (where the nonlinear vibrations are driven by the unbalance on the two rotors turning at different speeds). When used within the dynamic analysis of the test rig, the trained neural network is shown to be capable of predicting complex nonlinear phenomena with excellent accuracy. By comparison to an advanced theoretical model, the results show that the neural networks are able to capture the effects of features that are difficult to include in a hydrodynamic model or are particular to a given SFD.

Computational models of relational processes in cognitive development

Acquisition of relational knowledge is a core process in cognitive development. Relational knowledge is dynamic and flexible, entails structure-consistent mappings between representations, has properties of compositionality and systematicity, and depends on binding in working memory. We review three types of computational models relevant to relational knowledge. The first are formal models of structural commonalities among concepts, including some that differ in surface characteristics. The second is a self-modifying production system model of the role of relational knowledge in strategy acquisition. The third comprises symbolic connectionist models that implement key properties of relational cognition. These models are complemented by the semantic cognition model that shows how some developmentally important concept acquisition mechanisms can emerge from learning input–output functions. We conclude that no one type of model fully suffices as an account of cognitive development but there is potential for future development, including hybrid models that could meet most or all of the criteria.

Rapid Synthesis of Defined Eukaryotic Promoter Libraries

Current gene synthesis methods allow the generation of long segments of dsDNA. We show that these techniques can be used to create synthetic regulatory elements and describe a method for the creation of completely defined, synthetic variants of the PHO5 promoter from the budding yeast Saccharomyces cerevisae. Overall, 128 promoters were assembled by high-temperature ligation, cloned into plasmids by isothermal assembly, maintained in E. coli, and consequently transformed into yeast by homologous recombination. Synthesis errors occurred at frequencies comparable to or lower than those achieved with current gene synthesis methods. The promoter synthesis method reported here is robust, fast, and readily accessible. Synthetically engineered promoter libraries will be useful tools for dissecting the intricacies of promoter input-output functions and may serve as tunable components for synthetic genetic networks.

Glutamate modulates the firing rate in oculomotor nucleus motoneurons as a function of the recruitment threshold current

Studies in alert preparations have demonstrated that ocular motoneurons exhibit a phasic–tonic firing rate related to eye velocity and position, respectively. The slopes of these relationships are higher in motoneurons with higher recruitment threshold and have been proposed to depend upon synaptic input. To investigate this hypothesis, motoneurons of the rat oculomotor nucleus were recorded in a brain slice preparation in control conditions and during glutamate (5 μm) application to the bath. Glutamate did not affect membrane potential or input resistance, but produced a decrease in rheobase and depolarization voltage as a function of the current needed for generating a maintained repetitive discharge (recruitment threshold current). In addition, glutamate compressed the range of recruitment threshold current (0.1–0.4 nA) as compared to the control (0.15–0.7 nA). Glutamate exposed motoneurons showed an increase in the tonic frequency gain and the peak frequency. Such increments depended on the recruitment threshold current and the last recruited motoneurons almost doubled the tonic frequency gain (35.2 vs. 57.9 spikes s(−1) nA(−1)) and the peak frequency (52.4 vs. 102.6 spikes s(−1)). Finally, glutamate increased the spike frequency adaptation due to a significant increase in the phasic firing component as compared to the tonic one. In conclusion, glutamate modulates tonic and phasic discharge properties as a function of the recruitment threshold current and, presumably, motoneuron size. These findings contribute to understand the link between cellular functions and motoneuron discharge during oculomotor behaviour.

An active loudness model suggesting tinnitus as increased central noise and hyperacusis as increased nonlinear gain

The present study uses a systems engineering approach to delineate the relationship between tinnitus and hyperacusis as a result of either hearing loss in the ear or an imbalanced state in the brain. Specifically examined is the input–output function, or loudness growth as a function of intensity in both normal and pathological conditions. Tinnitus reduces the output dynamic range by raising the floor, while hyperacusis reduces the input dynamic range by lowering the ceiling or sound tolerance level. Tinnitus does not necessarily steepen the loudness growth function but hyperacusis always does. An active loudness model that consists of an expansion stage following a compression stage can account for these key properties in tinnitus and hyperacusis loudness functions. The active loudness model suggests that tinnitus is a result of increased central noise, while hyperacusis is due to increased nonlinear gain. The active loudness model also generates specific predictions on loudness growth in tinnitus, hyperacusis, hearing loss or any combinations of the three conditions. These predictions need to be verified by experimental data and have explicit implications for treatment of tinnitus and hyperacusis.

Dopaminergic Modulation of Mitral Cells and Odor Responses in the Zebrafish Olfactory Bulb

In the olfactory bulb, the modulatory neurotransmitter dopamine (DA) is coexpressed with GABA by local interneurons, but its role in odor processing remains obscure. We examined functions of DA mediated by D₂-like receptors in the olfactory bulb of adult zebrafish by pharmacology, whole-cell recordings, calcium imaging, and optogenetics. Bath application of DA had no detectable effect on odorant-evoked sensory input. DA directly hyperpolarized mitral cells (MCs) via D₂-like receptors and slightly increased their response gain. Consistent with this effect on input-output functions of MCs, small odorant responses were suppressed, whereas strong responses were enhanced in the presence of DA. These effects increased the root-mean-square contrast of population activity patterns but did not reduce their correlations. Optical stimulation of interneurons expressing channelrhodopsin-2 evoked fast GABAergic inhibitory currents in mitral cells but failed to activate D₂ receptor-mediated currents when stimuli were short. Prolonged stimulus trains, however, activated a slow hyperpolarizing current that was blocked by an antagonist of D₂-like receptors. GABA and DA are therefore both released from interneurons by electrical activity and hyperpolarize MCs, but D₂-dependent dopaminergic effects occur on slower timescales. Additional effects of DA may be mediated by D₁-like receptors. These results indicate that DA acts on D₂-like receptors via asynchronous release and/or volume transmission and implicate DA in the slow adaptation of circuit function. The shift of the membrane potential away from spike threshold could adapt mitral cells to background input without compromising their sensitivity.

Medial olivocochlear influence on stimulus-frequency otoacoustic emission input-output functions

Modulation of cochlear mechanics by the medial olivocochlear efferent system is characterized by a reduction in active, outer hair cell-mediated amplification of basilar membrane motion. This increases cochlear thresholds and linearizes basilar membrane input-output functions for low-to-moderate stimulus levels. Significant efferent effects have also been observed for responses to higher stimulus levels, potentially reflecting changes in the mechanical properties of the cochlear partition. In humans, sound activated changes in stimulus-frequency otoacoustic emissions have been used as a tool for investigating the dynamics of the medial olivocochlear reflex. However, the degree to which the amplitude and phase of otoacoustic emissions are related to those of basilar membrane motion is not entirely clear. For the purposes of comparison with invasive physiological measurements in animals, stimulus-frequency otoacoustic emission input-output functions were obtained from human subjects in the presence and absence of contralateral acoustic stimulation. Medial olivocochlear effects were quantified in terms of the absolute change in emission level as well as the vector change, which incorporates changes in emission phase. The extent to which efferent modulation of emissions in humans reflects that observed in previous reports of basilar membrane motion will be discussed.

Exercise training normalizes an increased neuronal excitability of NTS-projecting neurons of the hypothalamic paraventricular nucleus in hypertensive rats

Elevated sympathetic outflow and altered autonomic reflexes, including impaired baroreflex function, are common findings observed in hypertensive disorders. Although a growing body of evidence supports a contribution of preautonomic neurons in the hypothalamic paraventricular nucleus (PVN) to altered autonomic control during hypertension, the precise underlying mechanisms remain unknown. Here, we aimed to determine whether the intrinsic excitability and repetitive firing properties of preautonomic PVN neurons that innervate the nucleus tractus solitarii (PVN-NTS neurons) were altered in spontaneously hypertensive rats (SHR). Moreover, given that exercise training is known to improve and/or correct autonomic deficits in hypertensive conditions, we evaluated whether exercise is an efficient behavioral approach to correct altered neuronal excitability in hypertensive rats. Patch-clamp recordings were obtained from retrogradely labeled PVN-NTS neurons in hypothalamic slices obtained from sedentary (S) and trained (T) Wistar-Kyoto (WKY) and SHR rats. Our results indicate an increased excitability of PVN-NTS neurons in SHR-S rats, reflected by an enhanced input-output function in response to depolarizing stimuli, a hyperpolarizing shift in Na(+) spike threshold, and smaller hyperpolarizing afterpotentials. Importantly, we found exercise training in SHR rats to restore all these parameters back to those levels observed in WKY-S rats. In several cases, exercise evoked opposing effects in WKY-S rats compared with SHR-S rats, suggesting that exercise effects on PVN-NTS neurons are state dependent. Taken together, our results suggest that elevated preautonomic PVN-NTS neuronal excitability may contribute to altered autonomic control in SHR rats and that exercise training efficiently corrects these abnormalities.

EEG-guided transcranial magnetic stimulation reveals rapid shifts in motor cortical excitability during the human sleep slow oscillation.

Evoked cortical responses do not follow a rigid input-output function but are dynamically shaped by intrinsic neural properties at the time of stimulation. Recent research has emphasized the role of oscillatory activity in determining cortical excitability. Here we employed EEG-guided transcranial magnetic stimulation (TMS) during non-rapid eye movement sleep to examine whether the spontaneous <1 Hz neocortical slow oscillation (SO) is associated with corresponding fluctuations of evoked responses. Whereas the SO's alternating phases of global depolarization (up-state) and hyperpolarization (down-state) are clearly associated with fluctuations in spontaneous neuronal excitation, less is known about state-dependent shifts in neocortical excitability. In 12 human volunteers, single-pulse TMS of the primary motor cortical hand area (M1(HAND)) was triggered online by automatic detection of SO up-states and down-states in the EEG. State-dependent changes in cortical excitability were traced by simultaneously recording motor-evoked potentials (MEPs) and TMS-evoked EEG potentials (TEPs). Compared to wakefulness and regardless of SO state, sleep MEPs were smaller and delayed whereas sleep TEPs were fundamentally altered, closely resembling a spontaneous SO. However, both MEPs and TEPs were consistently larger when evoked during SO up-states than during down-states, and amplitudes within each SO state depended on the actual EEG potential at the time and site of stimulation. These results provide first-time evidence for a rapid state-dependent shift in neocortical excitability during a neuronal oscillation in the human brain. We further demonstrate that EEG-guided temporal neuronavigation is a powerful tool to investigate the phase-dependent effects of neuronal oscillations on perception, cognition, and motor control.

Non-invasive characterization of individual neurons with Continuous dynamic photo-stimulation

Event Abstract Back to Event Non-invasive characterization of individual neurons with Continuous dynamic photo-stimulation Andreas Neef1, 2*, Ahmed El Hady1, 2, Elinor Lazarov2, Kai Broeking2, Theo Geisel1, 2, Walter Stühmer1, 3 and Fred Wolf1, 2 1 BCCN Göttingen, Germany 2 MPI for Dynamics and Self-Organization, Non-linear Dynamics, Germany 3 MPI for Experimental Medicine, Molecular Biology of Neuronal Signals, Germany Understanding information encoding by individual central neurons requires characterization of their input-output functions under near-natural input conditions, e.g. in the fluctuation driven regime, characteristic of cortical circuits. Controlling the input and registering on the order of 10.000 - 100.000 spikes as output, one can compute transfer metrics which are critical for collective network dynamics, such as dynamic gain, correlation gain or spike frequency vs current (FI-) curves. So far now such data are exclusively obtained in sharp electrode or patch-clamp recordings, where the input to the cell body and therefore to the spike trigger zone in the axon initial segment is directly controlled. Due to the limited number of spikes obtained in invasive recordings, characterization of individual neurons is often not possible, dynamic gain curves, for instance, are averaged over tens of neurons. We recently developed an alternative, non-invasive method for neuronal characterization. Spikes are recorded by an array of extracellular electrodes. Well-defined, fluctuating stimuli are delivered via light-activated channelrhodopsins to pharmacologically isolated neurons. Careful characterization of channelrhodopsin’s transfer function warrants precise control over the waveform of the induced conductance. An 8x8 array of high-power LEDs provides local (50 µm) stimulation. Spot intensities of up to 30 mW/mm^2 are independently modulated at several kHz. The non-invasive nature of the experiment enables characterization of many individual neurons for many hours, up to few days. The setup delivers orders of magnitude more data than previously possible in the field of input-output characterization. Neuronal responses were stable, measurement of intracellular pH showed only minor acidification under continuous stimulation. Comparison of our results with dynamic gain measurements and FI-curves obtain with traditional methods establishes the equivalence of the non-invasive, high-throughput method. Preliminary experiments show that the method is also applicable to slices. Figure 1 Acknowledgements BMBF 01GQ1005B BMBF 01GQ0811 BMBF 01GQ0813 BMBF 01GQ1005E Keywords: dynamic gain, optogenetics, spike encoding Conference: Bernstein Conference 2012, Munich, Germany, 12 Sep - 14 Sep, 2012. Presentation Type: Poster Topic: Neural encoding and decoding Citation: Neef A, El Hady A, Lazarov E, Broeking K, Geisel T, Stühmer W and Wolf F (2012). Non-invasive characterization of individual neurons with Continuous dynamic photo-stimulation. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference 2012. doi: 10.3389/conf.fncom.2012.55.00227 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 11 May 2012; Published Online: 12 Sep 2012. * Correspondence: Dr. Andreas Neef, BCCN Göttingen, Göttingen, Germany, aneef@gwdg.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Andreas Neef Ahmed El Hady Elinor Lazarov Kai Broeking Theo Geisel Walter Stühmer Fred Wolf Google Andreas Neef Ahmed El Hady Elinor Lazarov Kai Broeking Theo Geisel Walter Stühmer Fred Wolf Google Scholar Andreas Neef Ahmed El Hady Elinor Lazarov Kai Broeking Theo Geisel Walter Stühmer Fred Wolf PubMed Andreas Neef Ahmed El Hady Elinor Lazarov Kai Broeking Theo Geisel Walter Stühmer Fred Wolf Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.