Normalization of neuronal responses in cortical area MT across signal strengths and motion directions

Abstract

Multiple visual stimuli are common in natural scenes, yet it remains unclear how multiple stimuli interact to influence neuronal responses. We investigated this question by manipulating relative signal strengths of two stimuli moving simultaneously within the receptive fields (RFs) of neurons in the extrastriate middle temporal (MT) cortex. Visual stimuli were overlapping random-dot patterns moving in two directions separated by 90°. We first varied the motion coherence of each random-dot pattern and characterized, across the direction tuning curve, the relationship between neuronal responses elicited by bidirectional stimuli and by the constituent motion components. The tuning curve for bidirectional stimuli showed response normalization and can be accounted for by a weighted sum of the responses to the motion components. Allowing nonlinear, multiplicative interaction between the two component responses significantly improved the data fit for some neurons, and the interaction mainly had a suppressive effect on the neuronal response. The weighting of the component responses was not fixed but dependent on relative signal strengths. When two stimulus components moved at different coherence levels, the response weight for the higher-coherence component was significantly greater than that for the lower-coherence component. We also varied relative luminance levels of two coherently moving stimuli and found that MT response weight for the higher-luminance component was also greater. These results suggest that competition between multiple stimuli within a neuron's RF depends on relative signal strengths of the stimuli and that multiplicative nonlinearity may play an important role in shaping the response tuning for multiple stimuli.

Testing the assumptions underlying fMRI adaptation using intracortical recordings in area MT

“…Second, conceptually area MT appears to be operate at a stage of visual processing that is more involved with the extraction of features than the combination of features into invariant representations of objects (Fujita, 2002). MT’s emphasis on extraction is consistent with computational models (Adelson & Bergen, 1985; Joukes et al, 2014) and experimental data that reveal competitive interactions (Gaudio & Huang, 2012; Krekelberg & Albright, 2005; Krekelberg & van Wezel, 2013; Xiao et al, 2014), the weighing of evidence and counter-evidence (Duijnhouwer & Krekelberg, 2015), and segmentation of figure and ground (X. Huang, Albright, & Stoner, 2007; X.…”

“…If adaptation suppresses the output of the neurons in the surround, this would result in disinhibition of the neurons in the center and therefore post-adaptation enhancement (Wissig & Kohn, 2012). Similarly, rightward selective neurons in MT are commonly modeled as being inhibited by leftward selective MT neurons (Simoncelli & Heeger, 1998), and there is considerable evidence for more complex competitive interactions in MT neurons (Gaudio & Huang, 2012; Krekelberg & Albright, 2005; Xiao, Niu, Wiesner, & Huang, 2014). If, for instance, this inhibition falls away after adapting to leftward motion (i.e., the leftward preferring neurons undergo repetition suppression), then the rightward selective neurons could increase their response due to disinhibition.…”

Spatial arrangement drastically changes the neural representation of multiple visual stimuli that compete in more than one feature domain

“…Work in our laboratory has shown that the direction tuning curves of MT neurons in response to overlapping random-dot stimuli moving transparently in two different directions can also be described as a weighted sum of the responses elicited by the individual stimulus components (Xiao et al, 2014;Xiao and Huang, 2015). When two stimulus components have different signal strengths in one feature domain, defined either by motion coherence or luminance contrast, MT neurons pool the stimulus component that has a stronger signal strength with a greater weight (Xiao et al, 2014).…”

“…Work in our laboratory has shown that the direction tuning curves of MT neurons in response to overlapping random-dot stimuli moving transparently in two different directions can also be described as a weighted sum of the responses elicited by the individual stimulus components (Xiao et al, 2014;Xiao and Huang, 2015). When two stimulus components have different signal strengths in one feature domain, defined either by motion coherence or luminance contrast, MT neurons pool the stimulus component that has a stronger signal strength with a greater weight (Xiao et al, 2014). The response bias in area MT toward the stimulus component that has a stronger signal strength can be accounted for by a descriptive model of divisive normalization (Xiao et al, 2014), similar to the contrast normalization model used to describe neuronal responses in V1 (Carandini et al, 1997;Busse et al 2009).…”

“…When two stimulus components have different signal strengths in one feature domain, defined either by motion coherence or luminance contrast, MT neurons pool the stimulus component that has a stronger signal strength with a greater weight (Xiao et al, 2014). The response bias in area MT toward the stimulus component that has a stronger signal strength can be accounted for by a descriptive model of divisive normalization (Xiao et al, 2014), similar to the contrast normalization model used to describe neuronal responses in V1 (Carandini et al, 1997;Busse et al 2009).…”