Richard Hahnloser

Position:
Professor
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Work phone:
41 44/ 635 3060
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What are the key neural computations that give rise to behavior? Can infer from behavior the computations performed by the brain? How far can first principles take us to understand neural circuits and their computations? We are interested in brain functions that can be characterized by a computational goal encompassing sensory inputs and motor outputs. Our favorite examples are vocal production and vocal learning, which we study in songbirds using reductionist experimental and theoretical approaches.

We study vocal communication and social learning in bird groups. We developed a multimodal system for longitudinal observation of group dynamics using multiple cameras, microphones, and animal-borne wireless sensors. Our goal is to better understand the vocal learning dynamics in social settings.

We are deeply involved in the NCCR Evolving Language, collaboratively researching the significance of animal communication as an evolutionary precursor to human language. Part of this effort, we are designing VocallBase, the largest database of animal vocalizations encompassing 10'000 species and the most stringent annotations, see https://vocallbase.evolvinglanguage.ch/.

Our work also focuses on translating songbird research to the domain of natural language processing (NLP) and back. We expect much cross-fertilization between these research areas: On the one hand, NLP approaches are readily applicable to birdsong research, because there are many analytical similarities between songbird-syllable and human-word sequences. On the other hand, we recently discovered that songbirds’ strategy of assigning vocal errors during learning is equivalent to word mover's distance, a highly successful computational strategy for document and sentence retrieval. Thus, songbirds have used retrieval strategies millions of years before computational linguists have invented them. Most certainly, many more analogies are about to be discovered. Our NLP insights we integrate into Endoc, a smart science writing tool made available to researchers in Switzerland: https://endoc.ethz.ch/.

Teaching

IDB 402 Systems, Computation and Neural Technology
INI-431, 227-1045-00 Readings in Neuroinformatics
INI-434, 227-1049-00 Block: Insights Into Neuroinformatics
INI-435, 227-0395-00 Neural Systems
INI-701, 227-1043-00 Neuroinformatics - Colloquia

Publications

2023

2022

2021

2020

2019

2018

2017

2016

2015

  • Bhargava, S. and Blaettler, F. and Kollmorgen, S. and Liu, S.-C. and Hahnloser, R. H. Linear methods for efficient and fast separation of two sources recorded with a single microphone, Neural Computation, 22:(10), 2015
  • Hahnloser, Richard Measurement and control of vocal interactions in songbirds, Journal of the Acoustical Society of America, 137:(4), 2015

2014

2013

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 pdf
  • 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 pdf
  • Hahnloser, R H R and Kotowicz, A Auditory representations and memory in birdsong learning, Current Opinion in Neurobiology, 20:(3) 332-339, 2010 pdf

2008

2007

  • Hahnloser, R.H. Cross-intensity functions and the estimate of spike-time jitter, Biological Cybernetics, 96:(5) 497-506, 2007 pdf
  • 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 pdf
  • 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 pdf

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 pdf

2005

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

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 pdf
  • 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 pdf

2001

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

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 pdf

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 pdf
  • 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 pdf

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 pdf