Spiking neural circuits with dendritic stimulus processors

Adelson, E. H., & Bergen, J. R. (1985). Spatiotemporal energy models for the perception of motion. Journal of Optical Society of America, 2(2) 284–299.

Ariav, G., Polsky, A., Schiller, J. (2003). Submillisecond precision of the input-output transformation function mediated by fast sodium dendritic spikes in basal dendrites of ca1 pyramidal neurons. The Journal of Neuroscience, 23(21) 7750–77758.

Berlinet, A., & Thomas-Agnan, C. (2004). Reproducing Kernel Hilbert spaces in probability and statistics. Kluwer Academic Publishers.

Borg-Graham, L. J., & Grzywacz, N. M. (1992). A model of the directional selectivity circuit in retina: transformations by neurons signly and in concert. In T. McKenna, J. Davis, S.F. Zornetzer (Eds.), Single neuron computation (pp. 34776). Academic Press.

Branco, T., Clark, B. A., Häusser, M. (2010). Dendritic discrimination of temporal input sequences in cortical neurons. Science, 329, 1671–1675.

Brezina, V. (2010). Beyond the wiring diagram: signalling through complex neuromodulator networks. Philosophical Transactions of the Royal Society B: Biological Sciences, 365, 2363–2374.

de Bruyne, M., Clyne, P. J., Carlson, J. R (1999). Odor coding in a model olfactory organ: the drosophila maxillary palp. Journal of Neuroscience, 19, 4520–4532.

Fellous, J.-M., & Linster, C. (1998). Computational models of neuromodulation. Neural Computation, 10, 771–805.

Friederich, U., Coca, D., Billings, S., Juusola, M. (2009). Data modelling for analysis of adaptive changes in fly photoreceptors. In Neural information processing, lecture notes in computer science (Vol. 5863, pp. 3448). Berlin: Springer.

Friederich, U., Coca, D., Billings, S. A., Juusola, M. (2010). Invariant contrast coding in photoreceptors. Front Neuroscience Conference Abstract: Computational and Systems Neuroscience. doi: 10.3389/conf.fnins.2010.03.00051

Gabbiani, F., Krapp, H. G., Koch, C., Laurent, G. (2002). Multiplicative computation in a visual neuron sensitive to looming. Nature, 420(21), 320–324.

Hildebrandt, K. J., Benda, J., Hennig, R. M. (2011). Multiple arithmetic operations in a single neuron: the recruitment of adaptation processes in the cricket auditory pathway depends on sensory context. The Journal of Neuroscience, 31(40), 14142–14150.

Izhikevich, E. M. (2007). Dynamical systems in neuroscience. The MIT Press.

Kim, A. J., & Lazar, A. A. (2012). Recovery of stimuli encoded with a Hodgkin-Huxley neuron using conditional PRCs. In N.W. Schultheiss, A.A. Prinz, R.J. Butera (Eds.), Response curves in neuroscience (Vol. 6, pp. 257–277). Springer.

Koch, C. (1999). Biophysics of computation: information processing in single neurons. Oxford University Press.

Lazar, A. A. (2010). Population encoding with Hodgkin-Huxley neurons. IEEE Transactions on Information Theory, 56(2).

Lazar, A. A., & Pnevmatikakis, E. A. (2008). Faithful representation of stimuli with a population of integrate-and-fire neurons. Neural Computation, 20(11), 2715–2744.

Lazar, A. A., & Pnevmatikakis, E. A. (2011). Video time encoding machines. IEEE Transactions on Neural Networks, 22(3), 461–473.

Lazar, A. A., Pnevmatikakis, E. A., Zhou, Y. (2010). Encoding natural scenes with neural circuits with random thresholds. Vision Research. Special Issue on Mathematical Models of Visual Coding.

Lazar, A. A., & Slutskiy, Y. B. (2010). Identifying dendritic processing. Advances in Neural Information Processing Systems, 23, 1261–1269.

Lazar, A. A., & Slutskiy, Y. B. (2012). Channel identification machines. Computational Intelligence and Neuroscience.

Lazar, A. A., & Slutskiy, Y. B. (2013). Multisensory encoding, decoding, and identification. Advances in Neural Information Processing Systems, 26, 3208–3216.

Lazar, A. A., & Slutskiy, Y. B. (2014). Functional identification of spike-processing neural circuits. Neural Computation, 26(2), 264–305.

Lazar, A. A., & Tóth, L. T (2004). Perfect recovery and sensitivity analysis of time encoded bandlimited signals. IEEE Transactions on Circuits and Systems-I: Regular Papers, 51(10), 2060–2073.

Lee, T. S. (1996). Image representation using 2d gabor wavelets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(10), 959–971.

London, M., & Häusser, M. (2005). Dendritic computation. Annual Review of Neuroscience, 28, 503–532.

Marmarelis, V. Z. (2004). Nonlinear dynamic modeling of physiological systems. Wiley-IEEE Press.

Ng, S. Y. (2009). A continuous-time asynchronous Sigma Delta analog to digital converter for broadband wireless receiver with adaptive digital calibration technique. PhD thesis, Ohio State University.

Pillow, J. (2007). Likelihood-based approaches to modeling the neural code. In Bayesian brain: probabilistic approaches to neural coding. The MIT Press.

Pillow, J. W., Shlens, J., Paninski, L., Sher, A., Litke, A. M., Chichilnisky, E. J., Simoncelli, E. P. (2008). Spatio-temporal correlations and visual signaling in a complete neuronal population. Nature, 454, 995–999.

Pnevmatikakis, E. A. (2011). Spikes as projections: representation and processing of sensory stimuli in the time domain. PhD thesis, Columbia University.

Rugh, W. J. (1981). Nonlinear system theory. The John Hopkins University Press.

Song, Z., Postma, M., Billings, S. A., Coca, D., Hardie, R. C., Juusola, M. (2012). Stochastic, adaptive sampling of information by microvilli in fly photoreceptors. Current Biology, 22(22), 1–10.

Ulanovsky, N., Las, L., Farkas, D., Nelken, I. (2004). Multiple time scales of adaptation in auditory cortex neurons. The Journal of Neuroscience, 17.

Urban, N., & Sakmann, B. (2002). Reciprocal intraglomerular excitation and intra- and interglomerular lateral inhibition between mouse olfactory bulb mitral cells. Journal of Physiology 542.2, 355–367.

Volterra, V. (1930). Theory of functionals and of integral and integro-differential equations. New York: Dover Publications.

Wang, Y. (2011). Smoothing Splines: Methods and applications. Chapman and Hall/CRC.

Wu, M. C.-K., David, S. V., Gallant, J. L. (2006). Complete functional characterization of sensory neurons by system identification. Annual Reviews of Neuroscience, 29, 477– 505.