Usually the conditional probabilities needed to calculate transmitted infor
mation are estimated directly from empirically measured distributions. Here
we show that an explicit model of the relation between response strength (
here, spike count) and its variability allows accurate estimates of transmi
tted information. This method of estimating information is reliable for dat
a sets with nine or more trials per stimulus. We assume that the model char
acterizes all response distributions, whether observed in a given experimen
t or not. All stimuli eliciting the same response are considered equivalent
. This allows us to calculate the channel capacity, the maximum information
that a neuron can transmit given the variability with which it sends signa
ls. Channel capacity is uniquely defined, thus avoiding the difficulty of k
nowing whether the 'right' stimulus set has been chosen in a particular exp
eriment. Channel capacity increases with increasing dynamic range and decre
ases as the variance of the signal (noise) increases. Neurons in V1 send mo
re variable signals in a wide dynamic range of spike counts, while neurons
in IT send less variable signals in a narrower dynamic range. Nonetheless,
neurons in the two areas have similar channel capacities. This suggests tha
t variance is being traded off against dynamic range in coding. (C) 1998 El
sevier Science Ireland Ltd. All rights reserved.