laboratoire de physique statistique
 
 
laboratoire de physique statistique

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JOURNAL OF NEUROPHYSIOLOGY 


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2015
Visual coding with a population of direction-selective neurons - Fiscella, Michele and Franke, Felix and Farrow, Karl and Mueller, Jan and Roska, Botond and da Silveira, Rava Azeredo and Hierlemann, Andreas
JOURNAL OF NEUROPHYSIOLOGY 1142485-2499 (2015)

Abstract : The brain decodes the visual scene from the action potentials of similar to 20 retinal ganglion cell types. Among the retinal ganglion cells, direction-selective ganglion cells (DSGCs) encode motion direction. Several studies have focused on the encoding or decoding of motion direction by recording multiunit activity, mainly in the visual cortex. In this study, we simultaneously recorded from all four types of ON-OFF DSGCs of the rabbit retina using a microelectronics-based high-density microelectrode array (HDMEA) and decoded their concerted activity using probabilistic and linear decoders. Furthermore, we investigated how the modification of stimulus parameters (velocity, size, angle of moving object) and the use of different tuning curve fits influenced decoding precision. Finally, we simulated ON-OFF DSGC activity, based on real data, in order to understand how tuning curve widths and the angular distribution of the cells' preferred directions influence decoding performance. We found that probabilistic decoding strategies outperformed, on average, linear methods and that decoding precision was robust to changes in stimulus parameters such as velocity. The removal of noise correlations among cells, by random shuffling trials, caused a drop in decoding precision. Moreover, we found that tuning curves are broad in order to minimize large errors at the expense of a higher average error, and that the retinal direction-selective system would not substantially benefit, on average, from having more than four types of ON-OFF DSGCs or from a perfect alignment of the cells' preferred directions.
 
2011
Interspike interval distributions of spiking neurons driven by fluctuating inputs - Ostojic, Srdjan
JOURNAL OF NEUROPHYSIOLOGY 106361-373 (2011)

Abstract : Ostojic S. Interspike interval distributions of spiking neurons driven by fluctuating inputs. J Neurophysiol 106: 361-373, 2011. First published April 27, 2011; doi:10.1152/jn.00830.2010.-Interspike interval (ISI) distributions of cortical neurons exhibit a range of different shapes. Wide ISI distributions are believed to stem from a balance of excitatory and inhibitory inputs that leads to a strongly fluctuating total drive. An important question is whether the full range of experimentally observed ISI distributions can be reproduced by modulating this balance. To address this issue, we investigate the shape of the ISI distributions of spiking neuron models receiving fluctuating inputs. Using analytical tools to describe the ISI distribution of a leaky integrate-and-fire (LIF) neuron, we identify three key features: 1) the ISI distribution displays an exponential decay at long ISIs independently of the strength of the fluctuating input; 2) as the amplitude of the input fluctuations is increased, the ISI distribution evolves progressively between three types, a narrow distribution (suprathreshold input), an exponential with an effective refractory period (subthreshold but suprareset input), and a bursting exponential (subreset input); 3) the shape of the ISI distribution is approximately independent of the mean ISI and determined only by the coefficient of variation. Numerical simulations show that these features are not specific to the LIF model but are also present in the ISI distributions of the exponential integrate-and-fire model and a Hodgkin-Huxley-like model. Moreover, we observe that for a fixed mean and coefficient of variation of ISIs, the full ISI distributions of the three models are nearly identical. We conclude that the ISI distributions of spiking neurons in the presence of fluctuating inputs are well described by gamma distributions.
 
2007
Doubts about quantal analysis - Ninio, Jacques
JOURNAL OF NEUROPHYSIOLOGY 981827-1835 (2007)