In recent years, several studies have examined decision making on the basis of
ambiguous visual stimuli. Neural recordings during a task of motion
discrimination have shown that activity in the lateral intraparietal area (LIP)
increases proportionally to the strength of visual evidence. This accumulation,
however, is observed in the average population activity but not in individual
neurons, which display a wide heterogeneity of response patterns (also termed
response "motifs") [1]. It remains unclear what functional role may be linked
with this neural heterogeneity. Here we propose that heterogeneity promotes the
accurate discrimination of input through a transformation termed a
high-dimensional projection. This effect is shown in a simple linear rate model
with balanced excitation/inhibition and orientation-selective units. In the
model, a balance of excitation and inhibition allows neuronal activity to
produce complex, yet globally stable activity. While the maximum amplitude of
responses to stimuli varies between units, the average variance across
individual units is several orders of magnitude lower than the variance of
average population activity. This behavior of the model allows population
activity to act as a line attractor despite fluctuations in single-unit
activity. The pattern of activity produced in the model forms intricate
response motifs that differ based on input statistics. We suggest that these
response motifs can be used to perform stimulus classification based on the
target decision of individual trials in the dot-motion task. We analyzed the
activity of the model using a Fisher linear discriminant analysis, and show
that units with uncorrelated activity project the activity of the model to a
high-dimensional space where a linear manifold can classify stimuli accurately.
This high-dimensional projection has been studied in other contexts including
object recognition [2], and can be read out linearly using a simple Hebbian
rule. We show that neural correlations induce a fundamental limit on the
readout capacity of the model by inducing collinearity amongst stimulus
responses. As a result, the model performs below theoretical bounds for
classification accuracy. We employed the model to capture neuronal responses in
recordings of LIP in monkeys performing a task of visual motion discrimination.
The model generated five novel predictions that were confirmed in analyses of
electrophysiological data: (1) a linear relation between classification error
and stimulus coherence; (2) a gradual decrease in classification error as more
units are incorporated in the high-dimension projection; (3) an above-chance
classification based on out-response field neurons alone; (4) higher
classification accuracy using in-response field neurons than out response-field
activity; and (5) chance-level discrimination with the removal of linear
accumulation. In sum, results provide an alternative to the common notion that
accurate neural representations of input rely on a simple accumulation of
population activity to a set threshold, and instead suggest that heterogeneous
activity plays a key role in generating a high-dimensional projection where
linear discrimination attains a high accuracy.
[1] M.L. Meister, J.A. Hennig, and A.C. Huk, Signal multiplexing and single-neuron computations in lateral intraparietal area during decision-making. J Neurosci, 2013. 33(6): p. 2254-67.
[2] M. Rigotti, et al., The importance of mixed selectivity in complex cognitive tasks. Nature, 2013. 497(7451): p. 585-90.