Divisive Normalization in Olfactory Population Codes

Abstract

In many regions of the visual system, the activity of a neuron is normalized by the activity of other neurons in the same region. Here we show that a similar normalization occurs during olfactory processing in the Drosophila antennal lobe. We exploit the orderly anatomy of this circuit to independently manipulate feedforward and lateral input to second-order projection neurons (PNs). Lateral inhibition increases the level of feedforward input needed to drive PNs to saturation, and this normalization scales with the total activity of the olfactory receptor neuron (ORN) population. Increasing total ORN activity also makes PN responses more transient. Strikingly, a model with just two variables (feedforward and total ORN activity) accurately predicts PN odor responses. Finally, we show that discrimination by a linear decoder is facilitated by two complementary transformations: the saturating transformation intrinsic to each processing channel boosts weak signals, while normalization helps equalize responses to different stimuli.

Generating sparse and selective third-order responses in the olfactory system of the fly

“…A previous mathematical characterization of PN responses did not show decorrelation (21,27) because it did not include lateral suppression. In agreement with more recent data (22), the more accurate characterization we use here does decorrelate.…”

“…This nonlinearity was described using an exponential function, but it can also be fit by an equation of the form r PN ¼ R max r 1:5 ORN =ðσ 1:5 þ r 1:5 ORN Þ, where r PN and r ORN are the firing rates of a corresponding PN and ORN pair, and R max and σ are constants. The advantage of using this latter form is that it fits in well with more recent work in which the effects of lateral suppression have been incorporated (22). Lateral suppression, which is the dominant effect of ORNs that do not drive a given PN directly, can be described by adding a term proportional to the sum of the firing rates of all of the ORNs to the denominator of the expression for r PN .…”

“…1A) and then determining the corresponding PN responses on the basis of an experimental characterization of the transformations occurring within the antennal lobe (see below) (21,22). Finally, we use the computed PN responses to drive model LHNs and KCs.…”

“…where m is another constant (see Methods for parameter values), provides an accurate description of both the feedforward nonlinearity and lateral suppression generated by the antennal lobe circuitry (22). Eq.…”

“…PN responses were generated from the ORN firing rates using Eq. 1 with R max = 165 Hz, σ = 12 Hz, and m = 0.05 Hz (22). Values of these parameters differ between PNs, so we have used values that provide the best overall fit.…”