Journal Papers

  • I. Delgado Ruz and S. R. Schultz (2014). Localising and classifying neurons from high density MEA recordings. Journal of Neuroscience Methods, 233:115-128.
  • K. D. Longden, T. Muzzu, D. Cook, S. R. Schultz and H. G. Krapp (2014). Nutritional state modulates the neural processing of visual motion. Current Biology, 24(8): 890-5. PDF
  • J. Oñativia, S. R. Schultz and P. L. Dragotti (2013). A Finite Rate of Innovation algorithm for fast and accurate spike detection from two-photon calcium imaging. Journal of Neural Engineering, 10:046017.
  • F. Montani, E. Phoka, M. Portesi and S. R. Schultz (2013). Statistical modeling of higher order correlations in pools of neuronal activity. Physica A, 392(2013):3066-3086. PDF
  • B. M. Seemungal, Q. Arshad, A. M. Bronstein, J. Guzman-Lopez, S. R. Schultz, V. Walsh, N. Yousif (2013). Vestibular activation differentially modulates human V1 and V5/MT excitability and response entropy. Cerebral Cortex, 23(1):12-19, doi:10.1093/cercor/bhr366.
  • N. Grossmann, V. Simiaki, C. Martinet, C. Toumazou, S. R. Schultz and K. Nikolic (2012). The spatial pattern of light determines the kinetics and modulates backpropagation of optogenetic action potentials. Journal of Computational Neuroscience, 34(3):477-88, doi: 10.1007/s10827-012-0431-7. PDF
  • E. Phoka, M. Wildie, S. R. Schultz and M. Barahona (2012). Sensory experience modifies spontaneous state dynamics in a barrel cortical circuit model. Journal of Computational Neuroscience, 33:323-339, doi: 10.1007/s10827-012-0388-6. PDF
  • A. B. Saleem, K.D. Longden, D. Schwyn, H. G. Krapp, and S. R. Schultz (2012). Bimodal optomotor response to plaids in blowflies: mechanisms of component-selectivity and evidence for pattern-selectivity. Journal of Neuroscience, 32(5):1634-1642. PDF
  • M. T. Schaub and S. R. Schultz (2012). The Ising Decoder: reading out the activity of large neural ensembles. Journal of Computational Neuroscience. 32(1):101-118. PDF
  • A. B. Saleem, P. Chadderton, J. Apergis-Schoute, K. D. Harris and S. R. Schultz (2010). Methods for predicting cortical UP and DOWN states from the phase of deep layer local field potentials. Journal of Computational Neuroscience, 29(1-2):49-62. PDF
  • S. R. Schultz, K. Kitamura, J. Krupic, A. Post-Uiterweer and M. Häusser (2009). Spatial pattern coding of sensory information by climbing-fiber evoked calcium signals in networks of neighboring cerebellar Purkinje cells. Journal of Neuroscience, 29:8005-8015.
  • F. Gibson, P. G. Overton, T. V. Smulders, S. R. Schultz, S. J. Eglen, C. D. Ingram, S. Panzeri, P. J. Bream, E. Sernagor, M. Cunningham, C. Adams, C. Echtermeyer, J. Simonotto, M. Kaiser, D. C. Swan, M. Fletcher and P. Lord, Minimum Information about a Neuroscience Investigation (MINI) Electrophysiology, Nature Preceedings, 2008, new edition 2009.
  • A. B. Saleem, H. G. Krapp and S. R. Schultz (2008). Receptive field characterisation by spike-triggered independent component analysis. Journal of Vision, 8(13):2,1-16.
  • F. Montani, A. Kohn, M. A. Smith, and S. R. Schultz (2007). The role of correlations in direction and contrast coding in the primary visual cortex. Journal of Neuroscience 27:2338-48.
  • F. Montani, A. Kohn, M. A. Smith, and S. R. Schultz (2007). How do stimulus-dependent correlations between V1 neurons affect neural coding? Neurocomputing 70:1782-1787.
  • A. A. Disney and S. R. Schultz (2004). Hallucinations and ACh: signal or noise? Behavioural and Brain Sciences, 27:790-791.
  • N. C. Rust, S. R. Schultz and J. A. Movshon (2002). A reciprocal relationship between reliability and responsiveness in developing visual cortical neurons. Journal of Neuroscience 22:10519-10523. PDF
  • S. Panzeri, R. S. Petersen, S. R. Schultz, M. A. Lebedev and M. E. Diamond (2002). Coding of stimulus location by spike timing in rat somatosensory cortex. Neurocomputing, 44:573-578.
  • S. Panzeri and S. R. Schultz (2001). A unified approach to the study of temporal, correlational and rate coding. Neural Computation, 13(6): 1311-1349. PDF
  • S. Panzeri, R. S. Petersen, S. R. Schultz, M. Lebedev and M. E. Diamond (2001). The role of spike timing in the coding of stimulus location in rat somatosensory cortex. Neuron, 29:769-777. PDF
  • S. R. Schultz and S. Panzeri (2001). Temporal correlations and neural spike train entropy. Physical Review Letters, 86(25): 5823-5826. PDF
  • S. R. Schultz (2000). What is the operating point? A discourse on perceptual organisation. Behavioural and Brain Sciences, 23(4): 491-492.
  • S. R. Schultz and E. T. Rolls (1999). Analysis of information transmission in the Schaffer collaterals. Hippocampus, 9(5):582-598. PDF
  • S. Panzeri, A Treves, S. R. Schultz and E. T. Rolls (1999). On decoding the responses of a population of neurons from short time windows. Neural Computation, 11:1553-1577. PDF
  • S. Panzeri, S. R. Schultz, A. Treves and E. T. Rolls (1999). Correlations and the encoding of information in the nervous system. Proceedings of the Royal Society of London Series B: Biological Sciences, 266:1001-1012. PDF
  • S. R. Schultz, S. Panzeri, A. Treves and E.T. Rolls (1999). Correlated firing and the information represented by neurons in short epochs. Neurocomputing, 26:499-504.
  • S. R. Schultz and A. Treves (1998). Stability of the replica symmetric solution for the information conveyed by a neural network. Physical Review E, 57(3):3302-3310. PDF
  • S.R. Schultz and M.A. Jabri (1995). An analogue VLSI `integrate-and-fire' neuron with frequency adaptation. Electronics Letters 31(16): 1357-1358. PDF
  • S.R. Schultz and M.A. Jabri (1995). A silicon basis for synaptic plasticity. Neural Processing Letters 2(6):23-27. PDF

Peer-reviewed conference proceedings papers

  • S. Jarvis, K. Nikolic and S. R. Schultz (2014). Optical coactivation in cortical cells: reprogramming the excitation-inhibition balancing act to control neuronal gain in abstract and detailed models. In Proceedings of the 23rd Annual Computational Neuroscience Conference. BMC Neuroscience 2014, 15 (Suppl 1):F1, doi:10.1186/1471-2202-15-S1-F1
  • R. Schuck, L. A. Annecchino and S. R. Schultz (2014). Scaling Up Multiphoton Neural Scanning: the SSA algorithm. Proceedings of the 36th Annual International IEEE EMBS Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, Illinois, USA, August 2014. PDF
  • R. Cazé, M. D. Humphries, B. Gutkin and S. R. Schultz (2013). A difficult classification for neurons without dendrites. Proceedings of the 6th International IEEE/EMBS Conference on Neural Engineering, San Diego, November 2013.
  • O. Agabi, P. Marchand, M. Mutlu, L. Klotz and S. R. Schultz (2013). Calcium imaging in temporal focus. Proceedings of the 6th International IEEE/EMBS Conference on Neural Engineering, San Diego, November 2013.
  • K Cheung, S. R. Schultz and W. Luk (2013). NeuroFlow: A FPGA-based Spiking Neural Network Accelerator with High-level Language Support. Front. Neuroinform. Conference Abstract: Neuroinformatics 2013. doi: 10.3389/conf.fninf.2013.09.00095
  • S. Jarvis, K. Nikolic, N. Grossman and S. R. Schultz (2013). Controlling the neuronal balancing act: Optical coactivation of excitation and inhibition in neuronal subdomains. BMC Neuroscience 14 Suppl 1, P348
  • K. Nikolic, S. Jarvis, N. Grossman and S. R. Schultz (2013). Computational models of optogenetic tools for controlling neural circuits with light. Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC ’13), Osaka, Japan, 2013, pp. 5934-7. PDF
  • K. Cheung, S. R. Schultz and W. Luk (2012). A large-scale spiking neural network accelerator for FPGA systems. Proceedings of 22nd International Conference on Field Programmable Logic and Applications (FPL 2012). Oslo, Norway. PDF
  • N. Cui and S. R. Schultz (2012). Differential entropy of multivariate neural spike trains. Artificial Networks and Machine Learning – ICANN 2012. Lectures Notes in Computer Science Vol. 7552, Springer Berlin/Heidelberg, pp. 280-287. PDF
  • K. Cheung, S. R. Schultz and W. Luk (2012). A large-scale spiking neural network simulator for FPGA systems. Artificial Networks and Machine Learning – ICANN 2012. Lectures Notes in Computer Science Vol. 7552, Springer Berlin/Heidelberg, pp. 113-120. PDF
  • J. Caballero, J. A. Urigüen, S. R. Schultz and P. L. Dragotti (2012). Spike-sorting at sub-Nyquist rates. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan, 2012, pp. 585-588. PDF
  • E. Phoka, M. Wildie, R. S. Petersen, M. Barahona and S. R. Schultz (2010). How is a sensory stimulus represented in ongoing dynamics in the barrel cortex? CNS 2010, San Antonio, Texas, published as BMC Neuroscience 2010 11(Suppl 1):P35, doi:10.1186/1471-2202-11-S1-P35
  • D. Cook, D. Gillies and S. R. Schultz (2010). Using GLMs to recover sparse connectivity in complex networks. BMC Neuroscience 11(Suppl 1):P53, doi: 10.1186/1471-2202-11-S1-P53.
  • M. Wildie, W. Luk, S. R. Schultz, P. H. W. Leong and A. Fidjeland (2009). Reconfigurable acceleration of neural models with gap junctions. Proceedings of the 2009 International Conference on Field-Programmable Technology, Sydney, Australia, December 2009, pp. 439-442. PDF
  • K. Cheung, S. R. Schultz and P. H. W. Leong (2009). A parallel spiking neural network simulator. Proceedings of the 2009 International Conference on Field-Programmable Technology, Sydney, Australia, December 2009, pp. 247-254. PDF
  • H. Seiler, Y. Zhang, A. B. Saleem, P. Bream, J. Apergis-Schoute and S. R. Schultz (2009). Maximum entropy decoding of multivariate neural spike trains. Proceedings of CNS 2009, Berlin, Germany, July 2009, published as BMC Neuroscience 2009, 10(Suppl 1):P107 (13 July 2009).
  • E. Phoka, M. Wildie, R. S. Petersen, M. Barahona and S. R. Schultz (2009). Interplay between spontaneous and sensory activities in barrel cortex: a computational study. CNS 2009, Berlin, Germany, July 2009, published as BMC Neuroscience 2009, 10(Suppl 1):P351 (13 July 2009).
  • H. Einarsdottir, F. Montani and S. R. Schultz (2007). A mathematical model of receptive field reorganization following stroke. IEEE International Conference on Development and Learning, pp. 211-216. PDF
  • F. Montani, O. A. Rosso and S. R. Schultz (2007). Discrimination Measure of Correlations in a Population of Neurons by using the Jensen-Shannon Divergence. AIP Conference Proceedings 913 XV Conference on Nonequilibrium Statistical Mechanics and Nonlinear Physics. Mar Del Plata, Argentina, Dec. 2006, pp. 184-189.
  • L. S. Smith, J. Austin, S. Baker, R. Borisyuk, S. Eglen, J. Feng, K. Gurney, T. Jackson, M. Kaiser, P. Overton, S. Panzeri, R. Quian Quiroga, S. R. Schultz, E. Sernagor, V. A. Smith, T. Smulders, L. Stuart, M. Whittington, C. Ingram (2007). The CARMEN e-Science pilot project: Work packages. In SJ Cox (ed), Proceedings of the UK e-Science All Hands Meeting 2007, Nottingham, Sep. 2007. PDF
  • S. R. Schultz and J. A. Movshon (2003). A Poisson decoder performs near-optimally at extracting motion signals from the temporally structured onset transients of MT neurons. J. Physiol. 555P C164. PDF
  • S. Panzeri, A. Treves, S. Schultz and E.T. Rolls (1998). Decoding population responses in short epochs . In ICANN'98: Proceedings of the 8th International Conference on Artificial Neural Networks, Skovde, Sweden, 2-4 September 1998. L. Niklasson, M. Boden and T. Ziemke (Eds). Springer-Verlag, London, pp. 967-972.
  • S. Schultz, S. Panzeri, A. Treves and E.T. Rolls (1997). Analogue resolution in a model of the Schaffer collaterals. In Gerstner et al. (Eds.) Artificial Neural Networks - ICANN '97, Lecture Notes in Computer Science Vol. 1327, Springer-Verlag, pp. 61-66. PDF
  • L.R. Leerink, S.R. Schultz and M.A. Jabri (1995). A Reinforcement Learning Exploration Strategy based on Ant Foraging Mechanisms. In Proc. Sixth Australian Conf. on Neural Networks, Sydney, Australia, pp. 217-220. PDF
  • P. Heim, S.R. Schultz and M.A. Jabri (1995). Technology-independent biasing technique for CMOS analogue micropower implementations of neural networks. In Proc. Sixth Australian Conf. on Neural Networks, Sydney, Australia, pp. 9-12. PDF
  • S.R. Schultz and M.A. Jabri (1994). Predictive Hebbian Learning of Representation in a Fast Reinforcement Controller. In Proc. Second Australia & New Zealand Conf. on Intelligent Information Processing Systems, Brisbane, Australia.

Book and Encyclopaedia Chapters

  • R. A. A. Ince, S. R. Schultz and S. Panzeri (2014). Summary of Information-Theoretic Quantities. D Jaeger and R Jung (Eds) Encyclopaedia of Computational Neuroscience. Springer Reference, New York / Berlin. arxiv
  • S. R. Schultz, R. A. A. Ince and S. Panzeri (2014). Applications of Information-Theoretic Quantities to Analysis of Neural Data. D Jaeger and R Jung (Eds) Encyclopaedia of Computational Neuroscience. Springer Reference, New York / Berlin. arxiv
  • R. A. A. Ince, S. Panzeri and S. R. Schultz (2014). Estimating Information-Theoretic Quantities. D Jaeger and R Jung (Eds) Encyclopaedia of Computational Neuroscience. Springer Reference, New York / Berlin. arxiv
  • F. Montani and S. R. Schultz (2010). An information-theoretic approach to analysing correlations in neural spike trains. In A.M. Kowalski, R. Rossignoli, E.M.F. Curado (eds.), Concepts and Recent Advances in Generalized Information Measures and Statistics. Bentham Books, 2010.
  • S. R. Schultz (2007). Signal-to-Noise Ratio in Neuroscience. Scholarpedia, p. 14874. PDF
  • G. Pola, S. R. Schultz, R. S. Petersen and S. Panzeri (2002). A practical guide to information analysis of spike trains. In R. Kotter (ed.), Neuroscience Databases: A Practical Guide, pp. 137-152. PDF
  • S. R. Schultz, H. D. R. Golledge and S. Panzeri (2000). Synchronisation, binding and the role of correlated firing in fast information transmission. In S. Wermter, J. Austin and D. Willshaw (Eds.), Emergent Neural Computational Architectures Based on Neuroscience, Lecture Notes in Artificial Intelligence, Springer-Verlag, Heidelberg, pp. 210-226. PDF
  • S. Schultz, S. Panzeri, E. T. Rolls and A. Treves (2000). Quantitative analysis of a Schaffer collateral model. Ch 14, pp. 257-272 in Baddeley et al (eds.) Information Theory and the Brain, Cambridge Univ. Press, Cambridge, UK.
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