Publications

Here you can find pdfs of our publications and links to simulation or analysis code.

 2022

• Calaim N, Dehmelt FA, Gonçalves PJ, Machens CK (2022) Robust coding with spiking networks: a geometric perspective. eLife 11, e73276.
• Cruz BF, Guiomar G, Soares S, Motiwala A, Machens CK, Paton JJ (2022) Action suppression reveals opponent parallel control via striatal circuits. Nature 607 (7919), 521-526.
• Gokcen E, Jasper AI, Semedo JD, Zandvakili A, Kohn A, Machens CK, Yu B (2022) Disentangling the flow of signals between populations of neurons Nature Computational Science 2 (8), 512-525.
• Motiwala A, Soares S, Atallah BV, Paton JJ*, Machens CK* (2022) Efficient coding of cognitive variables underlies dopamine responses and choice behavior. Nature Neuroscience 25, 738-748.
• Semedo JD, Jasper AI, Zandvakili A, Krishna A, Aschner A, Machens CK*, Kohn A*, Yu BM* (2022) Feedforward and feedback interactions between visual cortical areas use different population activity patterns. Nature Communications 13(1), 1-14.

2021

• Okazawa G, Hatch CE, Mancoo A, Machens CK, Kiani R (2021) Representational geometry of perceptual decisions in the monkey parietal cortex. Cell 184(14), 3748-61.
• Semedo JD, Gokcen E, Machens CK, Kohn A, Yu BM (2021) Statistical methods for dissecting interactions between brain areas. Current opinion in neurobiology 65, 59-69.
• Kohn A, Jasper AI, Semedo JD, Gokcen E, Machens CK, Yu BM (2021) Principles of corticocortical communication: proposed schemes and design considerations. Trends in neurosciences 43(9), 725-737

2020

• Brendel W*, Bourdoukan R*, Vertechi P*, Machens CK^, Deneve S^ (2020) Learning to represent signals spike by spike PLoS Computational Biology 16 (3), e1007692.
• Mancoo A*, Keemink S*, Machens CK (2020) Understanding spiking networks through convex optimization. Advances in Neural Information Processing Systems 33.
• Podlaski WF, Machens CK (2020) Biological credit assignment through dynamic inversion of feedforward networks. Advances in Neural Information Processing Systems 33.
• Berger S, Machens CK (2020) Compact task representations as a normative model for higher-order brain activity. Advances in Neural Information Processing Systems 33.

2019

• Semedo JD, Zandvakili A, Machens CK*, Yu BM*, Kohn A* (2019) Cortical areas interact through a communication subspace. Neuron 102 (1), 249-259.
• Keemink SW, Machens CK (2019) Decoding and encoding (de) mixed population responses. Current Opinion in Neurobiology 58, 112-121.
• Kobak D, Pardo-Vazquez JL, Valente M, Machens CK, Renart A (2019) State-dependent geometry of population activity in rat auditory cortex. eLife 8, e44526.

2018

• Jouary A, Machens CK (2018) A living display system, resolved pixel by pixel. Nature 562 (7727), 350-351.
• Alemi A, Machens CK, Deneve D, Slotine JJ (2018) Learning nonlinear dynamics in efficient, balanced spiking networks using local plasticity rules. Proceedings of the AAAI Conference on Artificial Intelligence 32(1).

2017

• Fairhall A, Machens CK (2017) Editorial Overview: Computational Neuroscience. Current opinion in neurobiology 46, A1-A4.

2016

• Barrett DGT, Deneve S, Machens CK (2016) Optimal compensation for neuron loss. eLife 5, e12454. Supplementary Material: Simulation Code
• Tian J, Huang R, Cohen J, Osakada F, Kobak D, Machens CK, Callaway EM, Uchida N, Watabe-Uchida M (2016) Distributed and mixed information in monosynaptic inputs to dopamine neurons. Neuron 91(6), 1374-1389.
• Kobak D*, Brendel W*, Constantinidis C, Feierstein CE, Kepecs A, Mainen ZF, Romo R, Qi XL, Uchida N, Machens CK (2016) Demixed principal component analysis of neural population data. eLife 5, e10989. Supplementary Material: dPCA Code, Romo Data, Constantinidis data, Feierstein Data, Uchida Data
  
• Deneve S, Machens CK (2016) Efficient codes and balanced networks. Nature Neuroscience 19(3), 375-382.

2015

• Gouvêa* TS, Monteiro* T, Motiwala A, Soares S, Machens CK, Paton JJ (2015) Striatal dynamics explain duration judgments. eLife 4, e11386.
  
• Wohrer A, Machens CK (2015) On the number of neurons and time scale of integration underlying the formation of percepts in the brain. PLoS Computational Biology 11(3), e1004082.
  
• Weber F, Machens CK (2015) Sensory Coding, Efficiency. In: Encyclopedia of Computational Neuroscience (Jaeger D, Jung R, ed.), pages 2658–2667. Springer.

2014

• Renart A, Machens CK (2014) Variability in neural activity and behavior. Current Opinion in Neurobioly. 25, 211-220.
• Gonçalves PJ, Arrenberg AB, Hablitzel B, Baier H, Machens CK (2014) Optogenetic perturbations reveal the dynamics of an oculomotor integrator. Frontiers Neural Circuits 8, 10.
• Vertechi P*, Brendel W*, Machens CK (2014) Unsupervised learning of an efficient short-term memory network. Advances in Neural Information Processing Systems 27, 3653-3661. Supplementary Material: Text
  
• Semedo J, Zandvakili A, Kohn A, Machens CK*, Yu B* (2014) Extracting latent structure from multiple interacting neural populations. Advances in Neural Information Processing Systems 27, 2942-2950.

2013

• Boerlin M, Machens CK, Deneve S (2013) Predictive coding of dynamical variables in balanced spiking networks. PLoS Computational Biology 9(11), e1003258. Supplementary Material: Text
  
• Barrett DGT, Deneve S, Machens CK (2013) Firing rate predictions in optimal balanced networks. Advances in Neural Information Processing Systems 26, 1538-1546.
• Wohrer A, Machens CK (2013) Percept and the single neuron. Nature Neuroscience 16(2), 112-113.
• Wohrer A, Humphries MD, Machens CK (2013) Population-wide distributions of neural activity during perceptual decision-making. Progress in Neurobiology 103, 156-193.

2012

• Machens CK (2012) Neuroscience. Building the human brain. Science 338(6111), 1156-1157.
  
• Weber F, Machens CK, Borst A (2012) Disentangling the functional consequences of the connectivity between optic-flow processing neurons. Nature Neuroscience 15, 441–448. Supplementary Material: Text
  
• Bourdoukan R*, Barrett DGT*, Machens CK, Deneve S (2012) Learning optimal spike-based representations. Advances in Neural Information Processing Systems 25, 2285-2293.

2011

• Brendel W, Romo R, Machens CK (2011) Demixed principal component analysis. Advances in Neural Information Processing Systems 24, 2654-2662.
  

2010

• Machens CK (2010) Demixing population activity in higher cortical areas. Frontiers Computational Neuroscience 4, 126.
• Weber F, Machens CK, Borst A (2010) Spatio-temporal response properties of optic-flow processing neurons. Neuron 67, 628-641. Supplementary Material: Text
  
• Machens CK, Romo R, Brody CD (2010) Functional, but not anatomical, separation of “what” and “when” in prefrontal cortex. Journal of Neuroscience 30(1), 350-360. Supplementary Material: Text
  
• Wohrer A, Romo R, Machens CK (2010) Linear readout from a neural population with partial correlation data. Advances in Neural Information Processing Systems 23, 2469-2477.

2006-2009

• Machens CK, Brody CD (2008) Design of continuous attractor networks with monotonic tuning using a symmetry principle. Neural Computation 20(2), 452-485.
  
• Benda J, Gollisch T, Machens CK, Herz AVM (2007) From response to stimulus: adaptive sampling in sensory physiology. Current Opinion Neurobiology 17(4), 430-436.
• Herz AVM, Gollisch T, Machens CK, Jaeger D (2006) Modeling single-neuron dynamics and computations: a balance of detail and abstraction. Science 314, 80-85.
  

2005

• Machens CK, Gollisch T, Kolesnikova O, Herz AVM (2005) Testing the efficiency of sensory coding with optimal stimulus ensembles. Neuron 47(3), 447-456. Supplementary Material: Text
  
• Machens CK, Romo R, Brody CD (2005) Flexible control of mutual inhibition: a neural model of two-interval discrimination. Science 307, 1121-1124. Supplementary Material: Text
  
• Herz AVM, Benda J, Gollisch T, Machens CK, Schaette R, Schutze H, Stemmler MB (2005) Auditory processing of acoustic communication signals: sensory biophysics, neural coding, and discrimination of conspecific song. In: Methods in Insect Sensory Neuroscience (Christensen TA, ed.), pages 129–156. CRC Press.

2004

• Machens CK, Wehr MS, Zador AM (2004) Linearity of cortical receptive fields measured with natural sounds. Journal of Neuroscience 24(5), 1089-1100.
  
• Edin F, Machens CK, Schütze H, Herz AVM (2004) Searching for optimal sensory signals: iterative stimulus reconstruction in closed-loop experiments. Journal of Computational Neuroscience 17(1), 47-56.
  

2003

• Machens CK, Schütze H, Franz A, Kolesnikova O, Stemmler MB, Ronacher B, Herz AVM (2003) Single auditory neurons rapidly discriminate conspecific communication signals. Nature Neuroscience 6(4), 341-342.
  
• Machens CK, Zador AM (2003) Auditory modeling gets an edge. Journal of Neurophysiology 90(6), 3581-3582.
• Machens CK, Wehr M, Zador AM (2003) Spectro-temporal receptive fields of subthreshold responses in auditory cortex. Advances in Neural Information Processing Systems 15, 149-156.

2002

• Machens CK (2002) Adaptive sampling by information maximization. Physical Review Letters 88, 228104.
  
• Schreiber S, Machens CK, Herz AVM, Laughlin SB (2002) Energy-efficient coding with discrete stochastic events. Neural Computation 14 (6), 1323-1346.
  

2001

• Machens CK, Stemmler MB, Prinz P, Krahe R, Ronacher B, Herz AVM (2001) Representation of acoustic communication signals in insect auditory receptors. Journal of Neuroscience 21(9), 3215-3227.
• Machens CK, Prinz P, Stemmler MB, Ronacher B, Herz AVM (2001) Discrimination of behaviorally relevant signals by auditory receptor neurons. Neurocomputing 38, 263-268.