Neural coding in the retina
Due to its experimental accessibility and early position in the visual
stream, the retina provides a beautiful opportunity for exploring
sensory coding and information processing in large populations of
neurons.
Pillow, J., Paninski, L., Uzzell, V., Simoncelli, E. & Chichilnisky,
E. (2005). Structure and
precision of retinal light responses analyzed with a noisy
integrate-and-fire model. Journal of Neuroscience 25: 11003-11013.
Pillow, J., Shlens, J., Paninski, L., Sher, A., Litke, A.,
Chichilnisky, E. & Simoncelli, E. (2008). Spatiotemporal correlations
and visual signaling in a complete neuronal population. Nature
454: 995-999.
Lalor, E., Ahmadian, Y. & Paninski, L. (2009). The relationship
between optimal and biologically plausible decoding of stimulus
velocity in the retina. Journal of the Optical Society of
America A (special issue on ideal observers and efficiency) 26:
B25-42.
Paninski, L., Ahmadian, Y., Ferreira, D., Koyama, S., Rahnama, K., Vidne,
M., Vogelstein, J. & Wu, W. (2009). A new look
at state-space models for neural
data.
Journal of
Computational Neuroscience (special issue on statistical analysis of
neural data).
Ahmadian, Y., Pillow, J. & Paninski, L. (2010).
Efficient Markov Chain Monte Carlo methods for
decoding population spike trains.
Neural Computation.
Pillow, J., Ahmadian, Y. & Paninski, L. (2010).
Model-based decoding, information estimation, and
change-point detection in multi-neuron spike
trains.
Neural Computation.
Babadi, B., Casti, A., Xiao, Y. & Paninski, L. (2010) Visual response in
LGN neurons beyond the monosynaptic retinogeniculate transmission.
Journal of Vision.
Field, G., Gauthier, J., Sher, A. et al. (2010). Mapping a neural
circuit: a complete input-output diagram in the primate retina. Nature.
Liam Paninski's research