Information processing in the LGN: a comparison of neural codes and cell types

Agnieszka Pregowska, Alex Casti, Ehud Kaplan, Eligiusz Wajnryb, Janusz Szczepanski

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

To understand how anatomy and physiology allow an organism to perform its function, it is important to know how information that is transmitted by spikes in the brain is received and encoded. A natural question is whether the spike rate alone encodes the information about a stimulus (rate code), or additional information is contained in the temporal pattern of the spikes (temporal code). Here we address this question using data from the cat Lateral Geniculate Nucleus (LGN), which is the visual portion of the thalamus, through which visual information from the retina is communicated to the visual cortex. We analyzed the responses of LGN neurons to spatially homogeneous spots of various sizes with temporally random luminance modulation. We compared the Firing Rate with the Shannon Information Transmission Rate , which quantifies the information contained in the temporal relationships between spikes. We found that the behavior of these two rates can differ quantitatively. This suggests that the energy used for spiking does not translate directly into the information to be transmitted. We also compared Firing Rates with Information Rates for X-ON and X-OFF cells. We found that, for X-ON cells the Firing Rate and Information Rate often behave in a completely different way, while for X-OFF cells these rates are much more highly correlated. Our results suggest that for X-ON cells a more efficient “temporal code” is employed, while for X-OFF cells a straightforward “rate code” is used, which is more reliable and is correlated with energy consumption.

Original languageEnglish
Pages (from-to)453-464
Number of pages12
JournalBiological Cybernetics
Volume113
Issue number4
DOIs
StatePublished - 1 Aug 2019

Keywords

  • Cat LGN
  • Entropy
  • Firing rate
  • Neural coding
  • ON–OFF cells
  • Shannon information theory

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