Benefits of contrast normalization demonstrated in neurons and model cells.
Gaudry, K.S. and Reinagel, P. (2007) J. Neurosci. 27(30):8071-9.
The large dynamic range of natural stimuli poses a challenge for neural coding: how is a neuron to encode large differences at high
contrast while remaining sensitive to small differences at low contrast? Many sensory neurons exhibit contrast normalization: gain
depends on the range of stimuli presented, such that firing-rate modulation is not proportional to contrast. However, coding depends
strongly on the precision of spike timing and the reliability of spike number, neither of which can be predicted from neural gain. The
presumption that contrast normalization is associated with maintained coding efficiency remained untested. We report that, as contrast
decreases, responses are more variable and encode less information, as expected. Nevertheless, these changes can be small, and information
transmission is even better preserved across contrasts than rate modulation. The extent of contrast normalization is correlated
with the extent to which information transmission is preserved across contrasts. Specifically, normalization is associated with maintaining
the bits of information per spike rather than bits per second. Finally, we show that a nonadapting model can exhibit both contrast
normalization and the associated information preservation.
Contrast adaptation in a non-adapting LGN model.
Sensory neurons appear to adapt their gain to match the variance of signals
along the dimension they encode, a property we shall call “contrast normalization”.
Contrast normalization has been the subject of extensive physiological and theoretical
study. We previously found that neurons in the lateral geniculate nucleus (LGN)
exhibit contrast normalization in their responses to full-field flickering white-noise
stimuli, and that neurons with the strongest contrast normalization best preserved
information transmission across a range of contrasts. We have also shown that both of
these properties could be reproduced by non-adapting model cells. Here we present a
detailed comparison of this non-adapting model to physiological data from the LGN.
First, the model cells recapitulated other contrast dependencies of LGN responses:
decreasing stimulus contrast resulted in an increase in spike-timing jitter and spikenumber
variability. Second, we find that the extent of contrast normalization in this
model depends on model parameters related to refractoriness and to noise. Third, we
show that the model cells exhibit rapid, transient changes in firing rate just after
changes in contrast, and that this is sufficient to produce the transient changes in
information transmission that have been reported in other neurons. It is known that
intrinsic properties of neurons change upon contrast adaptation. Nevertheless the
model demonstrates that the spiking nonlinearity of neurons can produce many of the
temporal aspects of contrast gain control, including normalization to input variance
and transient effects of contrast change.
Evidence for an additive inhibitory component of contrast adaptation.
The latency of visual responses generally decreases as contrast increases. Recording in the lateral geniculate nucleus (LGN), we find that response latency increases with increasing contrast in ON cells for some visual stimuli. We propose that this surprising latency trend can be explained if ON cells rest further from threshold at higher contrasts. Indeed, while contrast changes caused a combination of multiplicative gain change and additive shift in LGN cells, the additive shift predominated in ON cells. Modeling results supported this theory: the ON cell latency trend was found when the distance-to-threshold shifted with contrast, but not when distance-to-threshold was fixed across contrasts. In the model, latency also increases as surround-to-center ratios increase, which has been shown to occur at higher contrasts. We propose that higher-contrast full-field stimuli can evoke more surround inhibition, shifting the potential further from spiking threshold and thereby increasing response latency.
Gaudry, K.S. and Reinagel, P. (2007) J. Neurophys. 98(3):1287-96.
Gaudry and Reinagel (see arXiv preprint)