Badel L, Lefort S, Brette R, Petersen CC, Gerstner W, Richardson MJ. Dynamic I-V curves are reliable predictors of naturalistic pyramidal-neuron voltage traces. J Neurophysiol 99: 656 -666, 2008. First published December 5, 2007 doi:10.1152/jn.01107.2007. Neuronal response properties are typically probed by intracellular measurements of current-voltage (I-V) relationships during application of current or voltage steps. Here we demonstrate the measurement of a novel I-V curve measured while the neuron exhibits a fluctuating voltage and emits spikes. This dynamic I-V curve requires only a few tens of seconds of experimental time and so lends itself readily to the rapid classification of cell type, quantification of heterogeneities in cell populations, and generation of reduced analytical models. We apply this technique to layer-5 pyramidal cells and show that their dynamic I-V curve comprises linear and exponential components, providing experimental evidence for a recently proposed theoretical model. The approach also allows us to determine the change of neuronal response properties after a spike, millisecond by millisecond, so that postspike refractoriness of pyramidal cells can be quantified. Observations of I-V curves during and in absence of refractoriness are cast into a model that is used to predict both the subthreshold response and spiking activity of the neuron to novel stimuli. The predictions of the resulting model are in excellent agreement with experimental data and close to the intrinsic neuronal reproducibility to repeated stimuli. I N T R O D U C T I O NAccurate models of electrically active cells and their interactions are central requirements for the understanding of the computational processes taking place in nervous tissue. The construction of network models, even at the level of cortical columns, requires the identification of cell classes and the quantification of both their typical behavior and the heterogeneities within a population. The volume of data that is required for this tissue-level modeling demands a high-throughput approach in which response properties can be routinely measured.Electrophysiology provides an array of techniques for the extraction of neuronal response properties. Standard methods involve probing the response to step-change stimuli leading to current-voltage (I-V) curves for the steady-state or instantaneous response. Used systematically with pharmacology, they can yield a full conductance-based description Koch 1999) although the time required is prohibitive for routine neuron-by-neuron classification. More recently, an elegant optimization method (Huys et al. 2006) has been proposed that promises to significantly facilitate the construction of biophysically detailed models, given some prior knowledge of the kinetics of the channels present.Detailed models, comprising hundreds of compartments, are important for understanding the biophysical properties probed during electrophysiological and pharmacological manipulations. Such models can be used for network simulatio...

Odor information is encoded in the activity of a population of glomeruli in the primary olfactory center. However, how this information is decoded in the brain remains elusive. Here, we address this question in Drosophila by combining neuronal imaging and tracking of innate behavioral responses. We find that the behavior is accurately predicted by a model summing normalized glomerular responses, in which each glomerulus contributes a specific, small amount to odor preference. This model is further supported by targeted manipulations of glomerular input, which biased the behavior. Additionally, we observe that relative odor preference changes and can even switch depending on the context, an effect correctly predicted by our normalization model. Our results indicate that olfactory information is decoded from the pooled activity of a glomerular repertoire and demonstrate the ability of the olfactory system to adapt to the statistics of its environment.

The dynamic I -V curve method was recently introduced for the efficient experimental generation of reduced neuron models. The method extracts the response properties of a neuron while it is subject to a naturalistic stimulus that mimics in vivo-like fluctuating synaptic drive. The resulting history-dependent, transmembrane current is then projected onto a one-dimensional current-voltage relation that provides the basis for a tractable non-linear integrateand-fire model. An attractive feature of the method is that it can be used in spike-triggered mode to quantify the distinct patterns of post-spike refractoriness seen in different classes of cortical neuron. The method is first illustrated using a conductance-based model and is then applied experimentally to generate reduced models of cortical layer-5 pyramidal cells and interneurons, in injected-current and injectedconductance protocols. The resulting low-dimensional neuron models-of the refractory exponential integrate-and-fire

Models of neocortical networks are increasingly including the diversity of excitatory and inhibitory neuronal classes. Significant variability in cellular properties are also seen within a nominal neuronal class and this heterogeneity can be expected to influence the population response and information processing in networks. Recent studies have examined the population and network effects of variability in a particular neuronal parameter with some plausibly chosen distribution. However, the empirical variability and covariance seen across multiple parameters are rarely included, partly due to the lack of data on parameter correlations in forms convenient for model construction. To addess this we quantify the heterogeneity within and between the neocortical pyramidal-cell classes in layers 2/3, 4, and the slender-tufted and thick-tufted pyramidal cells of layer 5 using a combination of intracellular recordings, single-neuron modelling and statistical analyses. From the response to both square-pulse and naturalistic fluctuating stimuli, we examined the class-dependent variance and covariance of electrophysiological parameters and identify the role of the h current in generating parameter correlations. A byproduct of the dynamic I-V method we employed is the straightforward extraction of reduced neuron models from experiment. Empirically these models took the refractory exponential integrate-and-fire form and provide an accurate fit to the perisomatic voltage responses of the diverse pyramidal-cell populations when the class-dependent statistics of the model parameters were respected. By quantifying the parameter statistics we obtained an algorithm which generates populations of model neurons, for each of the four pyramidal-cell classes, that adhere to experimentally observed marginal distributions and parameter correlations. As well as providing this tool, which we hope will be of use for exploring the effects of heterogeneity in neocortical networks, we also provide the code for the dynamic I-V method and make the full electrophysiological data set available.

Highlights d Nanopores of Drosophila olfactory sensillum are modified cuticular envelope d Endocytic membrane structures are associated with the site of nanopore formation d Gore-tex/Osiris23 endosomal protein is required for nanopore formation d gore-tex/Osiris23 mutants showed greatly reduced olfactory response