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Effects of non-linear processing on information transfer in the lateral geniculate nucleus

  • Author(s): Denning, Kate
  • et al.
Abstract

Visual neurons' spike trains represent a large variety of visual stimuli. The local contrast varies across natural scenes, and the absolute luminance changes that define visual features during low-contrast stimuli are much smaller than during high-contrast stimuli. The contrast of the scene can remain relatively stable for an extended period of timing, suggesting that it would be advantageous for the neuron to adjust its coding strategy to the stimulus contrast. However, the contrast can also rapidly change. If the neuron utilizes different coding strategies during different stimulus conditions, it is imperative that the neuron be able to recognize when the statistics of the stimulus have changed. We propose that neurons in the lateral geniculate nucleus (LGN) utilize nonlinear properties in order to encode visual information across a variety of stimulus conditions. In Chapters 2 and 3, we find that bursts and single spikes represent distinct stimuli, such that distinguishing between the bursts and single spikes provides information about the stimulus. Because bursts only occur following prolonged hyperpolarization, this suggests a means by which the neuron can encode the stimulus context: bursts may provide information about whether a stimulus is surprising given the recent stimulus history. In Chapter 4, we report the contrast normalization allows LGN neurons to encode information about stimuli across a wide range of local contrasts. Cells exhibiting strongest contrast normalization are best able to preserve information across stimuli. Furthermore, both the contrast normalization and the associated preservation of information could be reproduced by a non-adapting LGN model. In Chapter 5, we report other contrast-dependencies of the model, show transient changes in the model responses following a contrast change, and describe why the model is able to exhibit contrast normalization

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