Classifying the motion of visual stimuli from the spike response of a population of retinal ganglion cells

Author(s):  
Alexander Cerquera ◽  
Martin Greschner ◽  
Jan A. Freund
2004 ◽  
Vol 92 (4) ◽  
pp. 2510-2519 ◽  
Author(s):  
Jonathan B. Demb ◽  
Peter Sterling ◽  
Michael A. Freed

Synaptic vesicles are released stochastically, and therefore stimuli that increase a neuron's synaptic input might increase noise at its spike output. Indeed this appears true for neurons in primary visual cortex, where spike output variability increases with stimulus contrast. But in retinal ganglion cells, although intracellular recordings (with spikes blocked) showed that stronger stimuli increase membrane fluctuations, extracellular recordings showed that noise at the spike output is constant. Here we show that these seemingly paradoxical findings occur in the same cell and explain why. We made intracellular recordings from ganglion cells, in vitro, and presented periodic stimuli of various contrasts. For each stimulus cycle, we measured the response at the stimulus frequency (F1) for both membrane potential and spikes as well as the spike rate. The membrane and spike F1 response increased with contrast, but noise (SD) in the F1 responses and the spike rate was constant. We also measured membrane fluctuations (with spikes blocked) during the response depolarization and found that they did increase with contrast. However, increases in fluctuation amplitude were small relative to the depolarization (<10% at high contrast). A model based on estimated synaptic convergence, release rates, and membrane properties accounted for the relative magnitudes of fluctuations and depolarization. Furthermore, a cell's peak spike response preceded the peak depolarization, and therefore fluctuation amplitude peaked as the spike response declined. We conclude that two extremely general properties of a neuron, synaptic convergence and spike generation, combine to minimize the effects of membrane fluctuations on spiking.


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Riccardo Volpi ◽  
Matteo Zanotto ◽  
Alessandro Maccione ◽  
Stefano Di Marco ◽  
Luca Berdondini ◽  
...  

Abstract The retina is a complex circuit of the central nervous system whose aim is to encode visual stimuli prior the higher order processing performed in the visual cortex. Due to the importance of its role, modeling the retina to advance in interpreting its spiking activity output is a well studied problem. In particular, it has been shown that latent variable models can be used to model the joint distribution of Retinal Ganglion Cells (RGCs). In this work, we validate the applicability of Restricted Boltzmann Machines to model the spiking activity responses of a large a population of RGCs recorded with high-resolution electrode arrays. In particular, we show that latent variables can encode modes in the RGC activity distribution that are closely related to the visual stimuli. In contrast to previous work, we further validate our findings by comparing results associated with recordings from retinas under normal and altered encoding conditions obtained by pharmacological manipulation. In these conditions, we observe that the model reflects well-known physiological behaviors of the retina. Finally, we show that we can also discover temporal patterns, associated with distinct dynamics of the stimuli.


2015 ◽  
Vol 35 (30) ◽  
pp. 10815-10820 ◽  
Author(s):  
N.-W. Tien ◽  
J. T. Pearson ◽  
C. R. Heller ◽  
J. Demas ◽  
D. Kerschensteiner

2021 ◽  
Author(s):  
Artem Pinchuk

Abstract Magnocellular-projecting retinal ganglion cells show spike response in two cases. Firstly, as a result of presentation of the optimal stimulus. Secondly, rebound excitation when removing the opposite stimulus. Also, there are studies suggesting that rebound excitation meets conditions to participate in visual perception at the same sensitivity and reaction speed as a response to the optimal stimulus. Thus, white noise stimulation creates possibility to catch the form of a smooth transition from one type of response to another. Using freely available data, a spike-triggered behavior map was built that does not show the area of silence between those two types of spike triggers. Moreover, linear filter with biphasic temporal properties which work as the derivative kernel demonstrate that both responses are two sides of the same coin. Thus, it is suggested to determine the optimal stimulus for magnocellular-projecting retinal ganglion cells as brightness change according to concentric center–surround receptive field structure.


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