Neural Coding Laboratory

45.

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, in press.

44.

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.

43.

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.

42.

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, in press., DOI: 10.1007/s10827-012-0431-7.

41.

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, in press, DOI: 10.1007/s10827-012-0388-6.

40.

B. M. Seemungal, Q. Arshad, A. M. Bronstein, J. Guzman-Lopez, S. R. Schultz, V. Walsh, N. Yousif (2012). Vestibular activation differentially modulates human V1 and V5/MT excitability and response entropy. Cerebral Cortex, in press, doi:10.1093/cercor/bhr366.

39.

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.

38.

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.

37.

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.

36.

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.

35.

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.

34.

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.

33.

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, 2009.

32.

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.

31.

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.

30.

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.

29.

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.

28.

S. R. Schultz (2007). Signal-to-Noise Ratio in Neuroscience. Scholarpedia, p. 14874.

27.

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.

26.

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.

25.

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.

24.

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.

21.

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.

20.

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.

18.

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.

17.

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.

16.

S. R. Schultz and S. Panzeri (2001). Temporal correlations and neural spike train entropy. Physical Review Letters, 86(25): 5823-5826.

14.

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.

13.

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.

12.

S. R. Schultz and E. T. Rolls (1999). Analysis of information transmission in the Schaffer collaterals. Hippocampus, 9(5):582-598.

11.

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.

10.

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.

8.

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.

5.

S.R. Schultz and M.A. Jabri (1995). An analogue VLSI `integrate-and-fire' neuron with frequency adaptation. Electronics Letters 31(16): 1357-1358.

4.

S.R. Schultz and M.A. Jabri (1995). A silicon basis for synaptic plasticity. Neural Processing Letters 2(6):23-27.

3.

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.

2.

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.