Richard Hahnloser

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41 44/ 635 3060
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I studied physics and obtained my PhD at the ETH Zurich. I am interested in brain functions that can be characterized by a computational goal encompassing sensory inputs and motor outputs. My favorite examples are vocal production and vocal learning, which I study in songbirds using reductionist experimental and theoretical approaches.

Similar to speech learning in humans, songbirds learn their songs by adjusting their own vocalizations in reference to a memorized tutor song. In my lab we are interested in decoding the 'neural algorithms' for this remarkable behavior. Research methods include but are not limited to chronic and acute electrophysiology, in-vivo pharmacology, light and electron microscopy, and computational modeling.

Projects

Teaching

INI-414 Neurophysics
INI-425 Block: Computational Neuroscience
INI-426 Block: Systems Neuroscience
INI-431 Foundational Literature of Neuroscience
INI-432 Neuroscience: From Networks to Systems
INI-502 Basics of instrumentation, measurement and analysis
INI-503 NSC Master Thesis (long) and Exam
INI-504 NSC Master Thesis (short) and Exam
INI-505 NSC Master Short Project I
INI-506 NSC Master Short Project II
INI-701 Kolloquium in Neuroinformatik

Publications

2011

2010

  • D'Souza, P and Liu, S C and Hahnloser, R H R Perceptron learning rule derived from spike-frequency adaptation and spike-time-dependent plasticity, Proceedings of the National Academy of Sciences of the United States of America, 107:(10) 4722-4727, 2010
  • Fiete, I R and Senn, W and Wang, C Z H and Hahnloser, R H R Spike time-dependent plasticity and heterosynaptic competition organize networks to produce long scale-free sequences of neural activity, Neuron, 65:(4) 563-576, 2010
  • Hahnloser, R H R and Kotowicz, A Auditory representations and memory in birdsong learning, Current Opinion in Neurobiology, 20:(3) 332-339, 2010

2008

2007

  • Hahnloser, R.H. Cross-intensity functions and the estimate of spike-time jitter, Biological Cybernetics, 96:(5) 497-506, 2007
  • Hahnloser, R.H.R. and Fee, M.S. Sleep-related spike bursts in HVC are driven by the nucleus interface of the nidopallium, Journal of Neurophysiology, 97:(1) 423-435, 2007
  • Weber, A.P. and Hahnloser, R.H. Spike correlations in a songbird agree with a simple Markov population model, PLoS Computational Biology, 3:(12) e249 doi:10.1371/journal.pcbi.0030249, 2007

2006

  • Hahnloser, R.H.R. and Kozhevnikov, A. and Fee, M.S. Sleep-related neural activity in a premotor and a basal-ganglia pathway of the songbird.of birdsong., Journal of Neurophysiology, 96:(2) 794-812, 2006

2005

  • Danóczy, M and Hahnloser, R.H.R. Efficient estimation of hidden state dynamics from spike trains , NIPS Proceedings, 17:, 2005

2004

  • Fiete, I and Hahnloser, R and Fee, M and Seung, S Temporal sparseness of the premotor drive is important for rapid learning in a neural network model of birdsong., Journal of Neurophysiology, 92:(4) 2274-82, 2004
  • Fee, Michale and Kozhevnikov, Alex and Hahnloser, Richard Neural mechanisms of vocal sequence generation in the songbird., Behavioral Neurobiology of Birdsong 153-170, 2004

2002

  • Hahnloser, RH and Douglas, RJ and Hepp, K Attentional recruitment of inter-areal recurrent networks for selective gain control., Neural Computation, 14:(7) 1669-89, 2002

2001

  • Rasche, C. and Hahnloser, R. Silicon Synaptic Depression, Biological Cybernetics, 84:(1) 57-62, 2001

2000

  • Hahnloser, Richard and Sarpeshkar, R. and Mahowald, Misha and Douglas, Rodney J. and Seung, S. Digital selection and analog amplification co-exist in an electronic circuit inspired by neocortex, Nature, 405: 947-951, 2000

1999

  • Hahnloser, R and Douglas, R and Mahowald, M and Hepp, K Feedback interactions between neuronal pointers and maps for attentional processing, Nature Neuroscience, 2: 746-752, 1999
  • Mudra, R. and Hahnloser, R. and Douglas, R.J. Neuromorphic Active Vision Used in Simple Navigation Behavior for a Robot, Proceedings of the 7th International Conference on Microelectronics for Neural Networks, 1: 32-36, 1999

1998

  • Hahnloser, R.H.R. Generating Network Trajectories Using Gradient Descent in State Space, IJCNN - International Joint Conference on Neural Networks 2373-2377, 1998
  • Hahnloser, Richard H.R. Learning Algorithms Based on Linearization, Network, Computation in Neural Systems, 9(3): 363-380, 1998
  • Hahnloser, Richard H.R. About the Piecewise Analysis of Networks of Linear Threshold Neurons, Neural Networks, 11: 691-697, 1998
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