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Andreas V M Herz

Publications and source records attributed to Andreas V M Herz.

14 recordsLinked to original sources
PubMed2006

Modeling single-neuron dynamics and computations: a balance of detail and abstraction.

The fundamental building block of every nervous system is the single neuron. Understanding how these exquisitely structured elements operate is an integral part of the quest to solve the mysteries of the brain. Quantitative mathematical models have proved to be an indispensable tool in pursuing this goal. We review recent advances and examine how single-cell models on five levels of complexity, from black-box approaches to detailed compartmental simulations, address key questions about neural dynamics and signal processing.

Action Potentials↗
PubMed2006

Sensory memory for odors is encoded in spontaneous correlated activity between olfactory glomeruli.

Sensory memory is a short-lived persistence of a sensory stimulus in the nervous system, such as iconic memory in the visual system. However, little is known about the mechanisms underlying olfactory sensory memory. We have therefore analyzed the effect of odor stimuli on the first odor-processing network in the honeybee brain, the antennal lobe, which corresponds to the vertebrate olfactory bulb. We stained output neurons with a calcium-sensitive dye and measured across-glomerular patterns of spontaneous activity before and after a stimulus. Such a single-odor presentation changed the relative timing of spontaneous activity across glomeruli in accordance with Hebb's theory of learning. Moreover, during the first few minutes after odor presentation, correlations between the spontaneous activity fluctuations suffice to reconstruct the stimulus. As spontaneous activity is ubiquitous in the brain, modifiable fluctuations could provide an ideal substrate for Hebbian reverberations and sensory memory in other neural systems.

Animals↗
PubMed2005

Spike-timing precision underlies the coding efficiency of auditory receptor neurons.

Sensory systems must translate incoming signals quickly and reliably so that an animal can act successfully in its environment. Even at the level of receptor neurons, however, functional aspects of the sensory encoding process are not yet fully understood. Specifically, this concerns the question how stimulus features and neural response characteristics lead to an efficient transmission of sensory information. To address this issue, we have recorded and analyzed spike trains from grasshopper auditory receptors, while systematically varying the stimulus statistics. The stimulus variations profoundly influenced the efficiency of neural encoding. This influence was largely attributable to the presence of specific stimulus features that triggered remarkably precise spikes whose trial-to-trial timing variability was as low as 0.15 ms--one order of magnitude shorter than typical stimulus time scales. Precise spikes decreased the noise entropy of the spike trains, thereby increasing the rate of information transmission. In contrast, the total spike train entropy, which quantifies the variety of different spike train patterns, hardly changed when stimulus conditions were altered, as long as the neural firing rate remained the same. This finding shows that stimulus distributions that were transmitted with high information rates did not invoke additional response patterns, but instead displayed exceptional temporal precision in their neural representation. The acoustic stimuli that led to the highest information rates and smallest spike-time jitter feature pronounced sound-pressure deflections lasting for 2-3 ms. These upstrokes are reminiscent of salient structures found in natural grasshopper communication signals, suggesting that precise spikes selectively encode particularly important aspects of the natural stimulus environment.

Acoustic Stimulation↗
PubMed2005

Testing the efficiency of sensory coding with optimal stimulus ensembles.

According to Barlow's seminal "efficient coding hypothesis," the coding strategy of sensory neurons should be matched to the statistics of stimuli that occur in an animal's natural habitat. Using an automatic search technique, we here test this hypothesis and identify stimulus ensembles that sensory neurons are optimized for. Focusing on grasshopper auditory receptor neurons, we find that their optimal stimulus ensembles differ from the natural environment, but largely overlap with a behaviorally important sub-ensemble of the natural sounds. This indicates that the receptors are optimized for peak rather than average performance. More generally, our results suggest that the coding strategies of sensory neurons are heavily influenced by differences in behavioral relevance among natural stimuli.

Acoustic Stimulation↗
PubMed2005

Spike-train variability of auditory neurons in vivo: dynamic responses follow predictions from constant stimuli.

Reliable accounts of the variability observed in neural spike trains are a prerequisite for the proper interpretation of neural dynamics and coding principles. Models that accurately describe neural variability over a wide range of stimulation and response patterns are therefore highly desirable, especially if they can explain this variability in terms of basic neural observables and parameters such as firing rate and refractory period. In this work, we analyze the response variability recorded in vivo from locust auditory receptor neurons under acoustic stimulation. In agreement with results from other systems, our data suggest that neural refractoriness has a strong influence on spike-train variability. We therefore explore a stochastic model of spike generation that includes refractoriness through a recovery function. Because our experimental data are consistent with a renewal process, the recovery function can be derived from a single interspike-interval histogram obtained under constant stimulation. The resulting description yields quantitatively accurate predictions of the response variability over the whole range of firing rates for constant-intensity as well as amplitude-modulated sound stimuli. Model parameters obtained from constant stimulation can be used to predict the variability in response to dynamic stimuli. These results demonstrate that key ingredients of the stochastic response dynamics of a sensory neuron are faithfully captured by a simple stochastic model framework.

Acoustic Stimulation↗
PubMed2004

Input-driven components of spike-frequency adaptation can be unmasked in vivo.

Spike-frequency adaptation affects the response characteristics of many sensory neurons, and different biophysical processes contribute to this phenomenon. Many cellular mechanisms underlying adaptation are triggered by the spike output of the neuron in a feedback manner (e.g., specific potassium currents that are primarily activated by the spiking activity). In contrast, other components of adaptation may be caused by, in a feedforward way, the sensory or synaptic input, which the neuron receives. Examples include viscoelasticity of mechanoreceptors, transducer adaptation in hair cells, and short-term synaptic depression. For a functional characterization of spike-frequency adaptation, it is essential to understand the dependence of adaptation on the input and output of the neuron. Here, we demonstrate how an input-driven component of adaptation can be uncovered in vivo from recordings of spike trains in an insect auditory receptor neuron, even if the total adaptation is dominated by output-driven components. Our method is based on the identification of different inputs that yield the same output and sudden switches between these inputs. In particular, we determined for different sound frequencies those intensities that are required to yield a predefined steady-state firing rate of the neuron. We then found that switching between these sound frequencies causes transient deviations of the firing rate. These firing-rate deflections are evidence of input-driven adaptation and can be used to quantify how this adaptation component affects the neural activity. Based on previous knowledge of the processes in auditory transduction, we conclude that for the investigated auditory receptor neurons, this adaptation phenomenon is of mechanical origin.

Acoustic Stimulation↗
PubMed2004

Subthreshold resonance explains the frequency-dependent integration of periodic as well as random stimuli in the entorhinal cortex.

Neurons integrate subthreshold inputs in a frequency-dependent manner. For sinusoidal stimuli, response amplitudes thus vary with stimulus frequency. Neurons in entorhinal cortex show two types of such resonance behavior: stellate cells in layer II exhibit a prominent peak in the resonance profile at stimulus frequencies of 5-16 Hz. Pyramidal cells in layer III show only a small impedance peak at low frequencies (1-5 Hz) or a maximum at 0 Hz followed by a monotonic decrease of the impedance. Whether the specific frequency selectivity for periodic stimuli also governs the integration of non-periodic stimuli has been questioned recently. Using frozen-noise stimuli with different distributions of power over frequencies, we provide experimental evidence that the integration of non-periodic subthreshold stimuli is determined by the same subthreshold frequency selectivity as that of periodic stimuli. Differences between the integration of noise stimuli in stellate and pyramidal cells can be fully explained by the resonance properties of each cell type. Response power thus reflects stimulus power in a frequency-selective way. Theoretical predictions based on linear system's theory as well as on conductance-based model neurons support this finding. We also show that the frequency selectivity in the subthreshold range extends to suprathreshold responses in terms of firing rate. Cells in entorhinal cortex are representative examples of cells with resonant or low-pass filter impedance profiles. It is therefore likely that neurons with similar frequency selectivity will process input signals according to the same simple principles.

Action Potentials↗
PubMed2004

Odor-driven attractor dynamics in the antennal lobe allow for simple and rapid olfactory pattern classification.

The antennal lobe plays a central role for odor processing in insects, as demonstrated by electrophysiological and imaging experiments. Here we analyze the detailed temporal evolution of glomerular activity patterns in the antennal lobe of honeybees. We represent these spatiotemporal patterns as trajectories in a multidimensional space, where each dimension accounts for the activity of one glomerulus. Our data show that the trajectories reach odor-specific steady states (attractors) that correspond to stable activity patterns at about 1 second after stimulus onset. As revealed by a detailed mathematical investigation, the trajectories are characterized by different phases: response onset, steady-state plateau, response offset, and periods of spontaneous activity. An analysis based on support-vector machines quantifies the odor specificity of the attractors and the optimal time needed for odor discrimination. The results support the hypothesis of a spatial olfactory code in the antennal lobe and suggest a perceptron-like readout mechanism that is biologically implemented in a downstream network, such as the mushroom body.

Animals↗
PubMed2003

A universal model for spike-frequency adaptation.

Spike-frequency adaptation is a prominent feature of neural dynamics. Among other mechanisms, various ionic currents modulating spike generation cause this type of neural adaptation. Prominent examples are voltage-gated potassium currents (M-type currents), the interplay of calcium currents and intracellular calcium dynamics with calcium-gated potassium channels (AHP-type currents), and the slow recovery from inactivation of the fast sodium current. While recent modeling studies have focused on the effects of specific adaptation currents, we derive a universal model for the firing-frequency dynamics of an adapting neuron that is independent of the specific adaptation process and spike generator. The model is completely defined by the neuron's onset f-I curve, the steady-state f-I curve, and the time constant of adaptation. For a specific neuron, these parameters can be easily determined from electrophysiological measurements without any pharmacological manipulations. At the same time, the simplicity of the model allows one to analyze mathematically how adaptation influences signal processing on the single-neuron level. In particular, we elucidate the specific nature of high-pass filter properties caused by spike-frequency adaptation. The model is limited to firing frequencies higher than the reciprocal adaptation time constant and to moderate fluctuations of the adaptation and the input current. As an extension of the model, we introduce a framework for combining an arbitrary spike generator with a generalized adaptation current.

Action Potentials↗
PubMed2002

Energy integration describes sound-intensity coding in an insect auditory system.

We investigate the transduction of sound stimuli into neural responses and focus on locust auditory receptor cells. As in other mechanosensory model systems, these neurons integrate acoustic inputs over a fairly broad frequency range. To test three alternative hypotheses about the nature of this spectral integration (amplitude, energy, pressure), we perform intracellular recordings while stimulating with superpositions of pure tones. On the basis of online data analysis and automatic feedback to the stimulus generator, we systematically explore regions in stimulus space that lead to the same level of neural activity. Focusing on such iso-firing-rate regions allows for a rigorous quantitative comparison of the electrophysiological data with predictions from the three hypotheses that is independent of nonlinearities induced by the spike dynamics. We find that the dependence of the firing rates of the receptors on the composition of the frequency spectrum can be well described by an energy-integrator model. This result holds at stimulus onset as well as for the steady-state response, including the case in which adaptation effects depend on the stimulus spectrum. Predictions of the model for the responses to bandpass-filtered noise stimuli are verified accurately. Together, our data suggest that the sound-intensity coding of the receptors can be understood as a three-step process, composed of a linear filter, a summation of the energy contributions in the frequency domain, and a firing-rate encoding of the resulting effective sound intensity. These findings set quantitative constraints for future biophysical models.

Acoustic Stimulation↗
PubMed2002

Traveling waves of excitation in neural field models: equivalence of rate descriptions and integrate-and-fire dynamics.

Field models provide an elegant mathematical framework to analyze large-scale patterns of neural activity. On the microscopic level, these models are usually based on either a firing-rate picture or integrate-and-fire dynamics. This article shows that in spite of the large conceptual differences between the two types of dynamics, both generate closely related plane-wave solutions. Furthermore, for a large group of models, estimates about the network connectivity derived from the speed of these plane waves only marginally depend on the assumed class of microscopic dynamics. We derive quantitative results about this phenomenon and discuss consequences for the interpretation of experimental data.

Animals↗
PubMed2002

Energy-efficient coding with discrete stochastic events.

We investigate the energy efficiency of signaling mechanisms that transfer information by means of discrete stochastic events, such as the opening or closing of an ion channel. Using a simple model for the generation of graded electrical signals by sodium and potassium channels, we find optimum numbers of channels that maximize energy efficiency. The optima depend on several factors: the relative magnitudes of the signaling cost (current flow through channels), the fixed cost of maintaining the system, the reliability of the input, additional sources of noise, and the relative costs of upstream and downstream mechanisms. We also analyze how the statistics of input signals influence energy efficiency. We find that energy-efficient signal ensembles favor a bimodal distribution of channel activations and contain only a very small fraction of large inputs when energy is scarce. We conclude that when energy use is a significant constraint, trade-offs between information transfer and energy can strongly influence the number of signaling molecules and synapses used by neurons and the manner in which these mechanisms represent information.

Animals↗
PubMedDate not supplied

Searching for optimal sensory signals: iterative stimulus reconstruction in closed-loop experiments.

Shaped by evolutionary processes, sensory systems often represent behaviorally relevant stimuli with higher fidelity than other stimuli. The stimulus dependence of neural reliability could therefore provide an important clue in a search for relevant sensory signals. We explore this relation and introduce a novel iterative algorithm that allows one to find stimuli that are reliably represented by the sensory system under study. To assess the quality of a neural representation, we use stimulus reconstruction methods. The algorithm starts with the presentation of an initial stimulus (e.g. white noise). The evoked spike train is recorded and used to reconstruct the stimulus online. Within a closed-loop setup, this reconstruction is then played back to the sensory system. Iterating this procedure, the newly generated stimuli can be better and better reconstructed. We demonstrate the feasibility of this method by applying it to auditory receptor neurons in locusts. Our data show that the optimal stimuli often exhibit pronounced sub-threshold periods that are interrupted by short, yet intense pulses. Similar results are obtained for simple model neurons and suggest that these stimuli are encoded with high reliability by a large class of neurons.

Acoustic Stimulation↗