CNS*2011 Workshop on

Methods of System Identification for Studying Information Processing in Sensory Systems

Thursday, July 28, 2011

Stockholm, Sweden


    A functional characterization of an unknown system typically begins by making observations about the response of that system to input signals. The knowledge obtained from such observations can then be used to derive a quantitative model of the system in a process called system identification. The goal of system identification is to use a given input/output data set to derive a function that maps an arbitrary system input into an appropriate output.

    In neurobiology, system identification has been applied to a variety of sensory systems, ranging from insects to vertebrates. Depending on the level of abstraction, the identified neural models vary from detailed mechanistic models to purely phenomenological models.

    The workshop will provide a state of the art forum for discussing methods of system identification applied to the visual, auditory, olfactory and somatosensory systems in insects and vertebrates.

    The lack of a deeper understanding of how sensory systems encode stimulus information has hindered the progress in understanding sensory signal processing in higher brain centers. Evaluations of various systems identification methods and a comparative analysis across insects and vertebrates may reveal common neural encoding principles and future research directions.

    The workshop is targeted towards systems, computational and theoretical neuroscientists with interest in the representation and processing of stimuli in sensory systems in insects and vertebrates.


    Wu, M., David, S., & Gallant, J. (2006). Complete Functional Characterization of Sensory Neurons by System Identification. Annual Review of Neuroscience, 29, 477–505.

    Ljung, L. (2010). Perspectives on System Identification, Annual Reviews in Control, 34 (2010), 1-12.

Program Committee

    Aurel A. Lazar, Department of Electrical Engineering, Columbia University.

    Mikko I. Juusola, Department of Biomedical Science, University of Sheffield.

Program Overview

    Thursday (9:00 AM - 5:00 PM), July 28, 2011

    Morning Session I (9:00 AM - 10:20 AM)
    Identification in Sensory Systems
    Chair: Shihab A. Shamma

    9:00 AM - 9:40 AM

    How Insect Photoreceptors and Visual Interneurons Process Information from the Natural Environment

    Mikko I. Juusola, Department of Biomedical Science, University of Sheffield.

    9:40 AM - 10:20 AM

    Multiplexing of Visual Information in Primary Visual Cortex

    Alberto Mazzoni, The Italian Institute of Technology, Genoa.

    10:20 AM - 10:50 AM

    Morning Break

    Morning Session II (10:50 AM - 12:10 PM)
    Identification in Sensory Systems
    Chair: Gonzalo G. de Polavieja

    10:50 AM - 11:30 AM

    Reverse Engineering Drosophila Olfactory Sensory Neurons

    Aurel A. Lazar, Department of Electrical Engineering, Columbia University.

    The lack of a deeper understanding of how olfactory sensory neurons (OSNs) encode odors has hindered the progress in understanding the olfactory signal processing in higher brain centers. Here we employ methods of system identification to investigate the encoding of time-varying odor stimuli and their representation for further processing in the spike domain by Drosophila OSNs.

    In order to apply system identification techniques, we built a novel low-turbulence odor delivery system that allowed us to deliver airborne stimuli in a precise and reproducible fashion. The system provides a 1% tolerance in stimulus reproducibility and an exact control of odor concentration and concentration gradient on a millisecond time scale.

    Using this novel setup, we recorded and analyzed the in-vivo response of OSNs to a wide range of time-varying odor waveforms. We report for the first time that across trials the response of OR59b OSNs is very precise and reproducible. Further, we empirically show that the response of an OSN depends not only on the concentration, but also on the rate of change of the odor concentration. Moreover, we demonstrate that a two-dimensional (2D) Encoding Manifold in a concentration-concentration gradient space provides a quantitative description of the neuron's response.

    We then use the white noise system identification methodology to construct one-dimensional (1D) and two-dimensional (2D) Linear-Nonlinear-Poisson (LNP) cascade models of the sensory neuron for a fixed mean odor concentration and fixed contrast. We show that in terms of predicting the intensity rate of the spike train, the 2D LNP model performs on par with the 1D LNP model, with a root mean-square error (RMSE) increase of about 5 to 10%.

    Surprisingly, we find that for a fixed contrast of the white noise odor waveforms, the nonlinear block of each of the two models changes with the mean input concentration. The shape of the nonlinearities of both the 1D and the 2D LNP model appears to be, for a fixed mean of the odor waveform, independent of the stimulus contrast. This suggests that white noise system identification of Or59b OSNs only depends on the first moment of the odor concentration.

    Finally, by comparing the 2D Encoding Manifold and the 2D LNP model, we demonstrate that the OSN identification results depend on the particular type of the employed test odor waveforms. This suggests an adaptive neural encoding model for Or59b OSNs that changes its nonlinearity in response to the odor concentration waveforms.

    [1] Anmo J. Kim, Aurel A. Lazar and Yevgeniy B. Slutskiy, System Identification of Drosophila Olfactory Sensory Neurons , Journal of Computational Neuroscience, Vol. 30, No.1, February 2011, pp. 143-161, Special Issue on Methods of Information Theory.
    [2] Bionet Group, Reverse Engineering the Fruit Fly Brain.

    11:30 AM - 12:10 PM

    Reconstructing Sound Stimuli from their Cortical Responses

    Shihab A. Shamma, Department of Electrical Engineering, University of Maryland, College Park.

    Population responses of cortical neurons encode considerable details about sensory stimuli, and the encoded information is likely to change with stimulus context and behavioral conditions. The details of encoding are difficult to discern across large sets of single neuron data because of the complexity of cortical receptive fields. To overcome this problem, we used the method of stimulus reconstruction to study how complex sounds are encoded in primary auditory cortex (AI). This method employs a linear spectro-temporal model to map neural population responses to an estimate of the stimulus spectrogram, thereby enabling a direct comparison between the original stimulus and its reconstruction.

    We demonstrate the value of this approach to identify cortical function by assessing the fidelity of such reconstructions from responses to modulated noise stimuli, thus estimating the range over which AI neurons can faithfully encode spectro-temporal features. For stimuli containing statistical regularities (typical of those found in complex natural sounds), we find that knowledge of these regularities substantially improves reconstruction accuracy over reconstructions that do not take advantage of this prior knowledge. Furthermore, contrasting stimulus reconstructions under different behavioral states reveals a novel view of the rapid changes in spectro-temporal response properties induced by attentional and motivational state. Finally, we employ this method to explore the robustness of auditory cortical responses to environmental noise in the encoding of complex sounds such as speech.

    Joint work with Nima Mesgarani, Stephen David and Jonathan Fritz.

    12:10 PM - 2:00 PM


    Afternoon Session (2:00 PM - 5:00 PM)
    Identification in the Cortex
    Chair: Hugh P.C. Robinson

    2:00 PM - 2:50 PM

    Analysis of Spatiotemporal Odor Codes in the Locust Olfactory System

    Barani Raman, Department of Biomedical Engineering, Washington University.

    Odorants are represented as spatiotemporal patterns of spikes in neurons of the antennal lobe (AL; insects) and olfactory bulb (OB; vertebrates). These response patterns have been thought to arise primarily from interactions within the AL/OB, an idea supported, in part, by the assumption that olfactory receptor neurons (ORNs) respond to odorants with simple firing patterns. However, activating the AL directly with simple pulses of current evoked responses in AL neurons that were much less diverse, complex, and enduring than responses elicited by odorants. Similarly, models of the AL driven by simplistic inputs generated relatively simple output. How then are dynamic neural codes for odors generated? Consistent with recent results from several other species, our recordings from locust ORNs showed a great diversity of temporal structure. Furthermore, we found that, viewed as a population, many response features of ORNs were remarkably similar to those observed within the AL. Using a set of computational models constrained by our electrophysiological recordings, we found that the temporal heterogeneity of responses of ORNs critically underlies the generation of spatiotemporal odor codes in the AL. A test then performed in vivo confirmed that, given temporally homogeneous input, the AL cannot create diverse spatiotemporal patterns on its own; however, given temporally heterogeneous input, the AL generated realistic firing patterns. Finally, given the temporally structured input provided by ORNs, we clarified several separate, additional contributions of the AL to olfactory information processing. Thus, our results demonstrate the origin and subsequent reformatting of spatiotemporal neural codes for odors.

    The fundamental olfactory design and processing principles are not only important for understanding how the brain interprets odor signals, but are also necessary for solutions inspired by biological computations for addressing parallel engineering problems. To conclude, I will briefly discuss my complementary research in the application of olfactory design and computational principles to develop a neuromorphic ‘electronic nose’.

    Joint work with Joby Joseph (NIH), Jeff Tang (NIH), Mark Stopfer (NIH), and Steve Semancik (NIST).

    2:50 PM - 3:40 PM

    Mechanisms and Possible Functions of Irregular Spiking in a Class of Cortical Inhibitory Interneurons

    Hugh P.C. Robinson, Department of Physiology, Development and Neuroscience, University of Cambridge.

    3:40 PM - 4:10 PM

    Afternoon Break

    4:10 PM - 5:00 PM

    Collective Behavior: What Can Many Bayesian Brains Do?

    Gonzalo G. de Polavieja, UAM Madrid.