Richard Hahnloser

Work phone:
41 44/ 635 3060
Home page:
We research neural computations, their expression in animal behavior, and their possible uses in statistical modeling of the world. By studying behavior, we read out the computations performed by the brain; and by studying neuroscience, we derive how the computations are implemented. 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 perform electrophysiological recordings to read out the neural code in singing birds, we have started to investigate the structure of the nervous system in birds using a combination of electron and light microscopy, termed correlative array tomography (CAT); and we design behavioral experiments to study vocal communication and social learning in bird groups. Currently we are trying to identify the simplest mechanistic equations to describe song learning trajectories.
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 deployable for birdsong research, because there are many analytical similarities between vocal and word sequences. On the other hand, we recently discovered that songbirds’ vocal error assignment strategy is equivalent to today’s most successful computational strategy for document retrieval. Thus, songbirds have used retrieval strategies millions of years before computer scientists have invented them.

Avaliable Positions/Projects

The role of successful and unsuccessful trials during motor learning [Student Project]

Reinforcement (‘good dog’, ‘bad dog’) is one of the main strategies to train animals and humans. We have used this strategy extensively in the lab to train songbirds to change their song and study the neural correlates of vocal and motor learning.

Reinforcement learning of human vocal behavior [Student Project]

We study reinforcement learning of fundamental frequency (pitch) in songbirds and humans. When birds receive aversive reinforcement for low-pitch syllables they successfully learn to increase the syllables’ pitch.

Psychophysical Theory of Human Pitch Processing [Student Project]

We study the mechanisms of fundamental frequency (pitch) adaptation of songbird and human vocalizations. Adaptation can be induced as a response to distortions of pitch feedback.

Analysis of birdsong development and automated clustering of song syllables [Student Project]

During early development, young songbirds such as the Zebra Finch learn acoustically complex but stereotyped sequential behaviors which are termed "songs". Furthermore, zebra finches learn only one song in their lifetime, making the problem of developmental song analysis tractable.



INI-434, 227-1049-00 Block: Insights Into Neuroinformatics
INI-435, 227-0395-00 Neural Systems
INI-503, 227-1041-01 NSC Master Thesis (long) and Exam
INI-504, 227-1041-02 NSC Master Thesis (short) and Exam
INI-505, 227-1036-01 NSC Master Short Project I
INI-506, 227-1036-02 NSC Master Short Project II
INI-701, 227-1043-00 Neuroinformatics - Colloquia




  • 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





  • 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



  • 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


  • 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


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


  • 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


  • 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


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


  • 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


  • 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


  • 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
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