Literature DB >> 9356386

Decoding visual information from a population of retinal ganglion cells.

D K Warland1, P Reinagel, M Meister.   

Abstract

Decoding visual information from a population of retinal ganglion cells. J. Neurophysiol. 78: 2336-2350, 1997. This work investigates how a time-dependent visual stimulus is encoded by the collective activity of many retinal ganglion cells. Multiple ganglion cell spike trains were recorded simultaneously from the isolated retina of the tiger salamander using a multielectrode array. The stimulus consisted of photopic, spatially uniform, temporally broadband flicker. From the recorded spike trains, an estimate was obtained of the stimulus intensity as a function of time. This was compared with the actual stimulus to assess the quality and quantity of visual information conveyed by the ganglion cell population. Two algorithms were used to decode the spike trains: an optimized linear filter in which each action potential made an additive contribution to the stimulus estimate and an artificial neural network trained by back-propagation to match spike trains with stimuli. The two methods performed indistinguishably, suggesting that most of the information about this stimulus can be extracted by linear operations on the spike trains. Individual ganglion cells conveyed information at a rate of 3.2 +/- 1.7 bits/s (mean +/- SD), with an average information content per spike of 1.6 bits. The maximal possible rate of information transmission compatible with the measured spiking statistics was 13.9 +/- 6.3 bits/s. On average, ganglion cells used 22% of this capacity to encode visual information. When a decoder received two spike trains of the same response type, the reconstruction improved only marginally over that obtained from a single cell. However, a decoder using an ON and an OFF cell extracted as much information as the sum of that obtained from each cell alone.Thus cells of opposite response type encode different and nonoverlapping features of the stimulus. As more spike trains were provided to the decoder, the total information rate rapidly saturated, with 79% of the maximal value obtained from a local cluster of just four neurons of different functional types. The decoding filter applied to a given neuron's spikes within such a multiunit decoder differed substantially from the filter applied to that same neuron in a single-unit decoder. This shows that the optimal interpretation of a ganglion cell's action potential depends strongly on the simultaneous activity of other nearby cells. The quality of the stimulus reconstruction varied greatly with frequency: flicker components below 1 Hz and above 10 Hz were reconstructed poorly, and the performance was optimal near 2.5 Hz. Further analysis suggests that temporal encoding by ganglion cell spike trains is limited by slow phototransduction in the cone photoreceptors and a corrupting noise source proximal to the cones.

Entities:  

Mesh:

Year:  1997        PMID: 9356386     DOI: 10.1152/jn.1997.78.5.2336

Source DB:  PubMed          Journal:  J Neurophysiol        ISSN: 0022-3077            Impact factor:   2.714


  84 in total

1.  Reconstruction of natural scenes from ensemble responses in the lateral geniculate nucleus.

Authors:  G B Stanley; F F Li; Y Dan
Journal:  J Neurosci       Date:  1999-09-15       Impact factor: 6.167

2.  Stimulus encoding and feature extraction by multiple sensory neurons.

Authors:  Rüdiger Krahe; Gabriel Kreiman; Fabrizio Gabbiani; Christof Koch; Walter Metzner
Journal:  J Neurosci       Date:  2002-03-15       Impact factor: 6.167

3.  Representation of acoustic communication signals by insect auditory receptor neurons.

Authors:  C K Machens; M B Stemmler; P Prinz; R Krahe; B Ronacher; A V Herz
Journal:  J Neurosci       Date:  2001-05-01       Impact factor: 6.167

4.  Decorrelation and efficient coding by retinal ganglion cells.

Authors:  Xaq Pitkow; Markus Meister
Journal:  Nat Neurosci       Date:  2012-03-11       Impact factor: 24.884

5.  Information transmission rates of cat retinal ganglion cells.

Authors:  Christopher L Passaglia; John B Troy
Journal:  J Neurophysiol       Date:  2003-11-05       Impact factor: 2.714

6.  Synergy, redundancy, and independence in population codes.

Authors:  Elad Schneidman; William Bialek; Michael J Berry
Journal:  J Neurosci       Date:  2003-12-17       Impact factor: 6.167

7.  Neural population structures and consequences for neural coding.

Authors:  Don H Johnson
Journal:  J Comput Neurosci       Date:  2004 Jan-Feb       Impact factor: 1.621

8.  A model-based approach for the analysis of neuronal information transmission in multi-input and -output systems.

Authors:  M Eger; R Eckhorn
Journal:  J Comput Neurosci       Date:  2002 May-Jun       Impact factor: 1.621

9.  Assessing the encoding of stimulus attributes with rapid sequences of stimulus events.

Authors:  M Eger; R Eckhorn
Journal:  J Comput Neurosci       Date:  2002 Nov-Dec       Impact factor: 1.621

10.  Low error discrimination using a correlated population code.

Authors:  Greg Schwartz; Jakob Macke; Dario Amodei; Hanlin Tang; Michael J Berry
Journal:  J Neurophysiol       Date:  2012-04-25       Impact factor: 2.714

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.