Machens, Christian (PhD)|
Group for Neural Theory, DEC
ground floor, office 65/66
Ecole normale supérieure
29, rue d'Ulm
|phone ||ON LEAVE (Champolimaud Instiute, Lisbon)|
How does the brain work?
What are the kind of computations carried out by neural systems?
We try to address these questions by analyzing recordings of neural activity
and constructing mathematical models of neural circuits. In particular, we are interested in the function and purpose of recurrent connectivity in biological systems. What do nervous systems gain by using recurrent connections as opposed to purely feedforward connections? What are the computational benefits, what are the costs and problems of recurrent connections? We investigate these questions on a range of concrete examples taken from neurobiology. Our interests range from the study of sensory systems where neural activity is mostly input-driven to the study of cognitive systems (such as the prefrontal cortex) where neural activity can be dominated by a circuit's internal dynamics.
In leave to Champolimaud Institute for the Unknown
Wohrer, A. and Machens, C.K., Percept and the single neuron (News and Views on Haefner et al. 2013, same issue),
Nature Neuroscience, 16, 112-113 (2013).
Wohrer, A., Humphries, M.D., and Machens, C.K., Population-wide distributions of neural activity during perceptual decision-making,
Progress in Neurobiology, http://dx.doi.org/10.1016/j.pneurobio.2012.09.004 (online publication), (2012).
Machens, C.K., Romo, R., and Brody, C.D., Functional, But Not Anatomical, Separation of "What" and "When" in Prefrontal Cortex,
Journal of Neuroscience, 30(1), 350-360 (2010).
Weber, F., Machens, C.K., and Borst, A., Spatio-Temporal Response Properties of Optic-Flow Processing Neurons,
Neuron, 67, 629-642 (2010).
Wohrer, A., Romo, R., and Machens, C.K., Linear readout from a neural population with partial correlation data,
Advances in Neural Information Processing Systems, 23, 2469-2477 (2010).
Machens, C.K., Demixing population activity in higher cortical areas,
Front Comput Neurosci, 4, 26 (2010).
Machens, C.K. and Brody, C.D., Design of continuous attractor networks with monotonic tuning using a symmetry principle,
Neural Computation, 20, 452-485 (2008).
Benda, J., Gollisch, T., Machens, C.K., and Herz, A.V.M., From response to stimulus: adaptive sampling in sensory physiology,
Current Opinion in Neurobiology, 17(4), 430-436 (2007).
Herz, A.V.M., Gollisch, T., Machens, C.K., and Jager, D., Modeling single-neuron dynamics and computations: a balance of detail and abstraction,
Science, 314, 80-85 (2006).
Machens, C.K., Gollisch, T., Kolesnikova, O., and Herz, A.V.M., Testing the efficiency of sensory coding with optimal stimulus ensembles,
Neuron, 47, 447-456 (2005).
Machens, C.K., Romo, R., and Brody, C.D., Flexible dynamics of mutual inhibiton: a neural model of two-interval discrimination,
Science, 307, 1121-1124 (2005).
Machens, C.K., Wehr, M.S., and Zador, A.M., Linearity of cortical receptive fields measured with natural sounds,
Journal of Neuroscience, 24(5), 1089-1100 (2004).
Edin, F., Machens, C.K., Schutze, H., and Herz, A.V.M., Iterative stimulus reconstruction in closed-loop experiments,
Journal of Computational Neuroscience, 17(1), 47-56 (2004).
Machens, C.K., Schutze, H., Franz, A., Kolesnikova, O., Stemmler, M.B., Ronacher, B., and Herz, A.V.M., Rapid discrimination of acoustic communication calls by auditory receptors,
Nature Neuroscience, 6(4), 341-342 (2003).
Machens, C.K., Adaptive sampling by information maximization,
Physical Review Letters, 88, 228104 (2002).
Schreiber, S., Machens, C.K., Herz, A.V.M., and Laughlin, S.B., Energy-efficient coding with discrete stochastic events,
Neural Computation, 14(6), 1323-1346 (2002).
Machens, C.K., Stemmler, M.B., Prinz, P., Krahe, R., Ronacher, B., and Herz, A.V.M., Representation of acoustic communication signals in insect auditory receptors,
Journal of Neuroscience, 21(9), 3215-3227 (2001).