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Mixed latent variable model of attention in V1


Bethge,  M
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Vinogradov, O., Ecker, A., Denfield, G., Tolias, A., & Bethge, M. (2017). Mixed latent variable model of attention in V1. Poster presented at Bernstein Conference 2017, Berlin, Germany. doi:10.12751/nncn.bc2017.0210.

Cite as: https://hdl.handle.net/21.11116/0000-0000-C415-B
Neurons show a high degree of variability of spike trains, even in responses to identical stimuli. This variability is often correlated between neurons of one population, however, the sources of the correlation remain unknown. According to one hypothesis, inter-trial fluctuation of an attentional signal can induce noise correlation [Cohen Newsome 2008, Ecker et al. 2016]. To test this hypothesis in the primary visual cortex, we designed a novel cued change detection task in which attentional fluctuations are modulated across trials. We trained two monkeys to maintain fixation and to make a saccade toward coherent gratings among a series of two Gabor patches with randomly changing orientations presented simultaneously in the left and right visual field. The monkeys learned to attend either to the stimulus on one side or to both stimuli (Fig. 1 A, B).
To track the attentional state on a single-trial basis, we developed a model that multiplicatively accounts for the stimulus-driven variability of spikes and shared latent fluctuations of an attentional signal. The model describes the neuronal responses as a product of a stimulus response, attentional cue, slow drift, and shared latent variables (Fig. 1 C). The first two components are assumed to capture attentional modulation of the mean neuronal gain («classical» model of attention [Maunsell Treue, 2006]). The slow modulator accounts for potential drift of individual neurons’ firing rates throughout the recording session and is modeled by a Gaussian process across trials [Rabinowitz et al., 2015]. The shared attentional modulators are also assumed to be smooth, but with a faster timescale, and their within-trial dynamics are modeled by Gaussian Process Factor Analysis [Yu et al., 2009].
We trained the model on responses of V1 neurons in the change detection task. As expected, the gain of V1 neurons is increased by attention. We found that including shared latent variables improved predictive performance (Fig. 1 D) on held-out data compared with a model based on firing rates and attentional cue only. However, the improvement was small when including more than two latent variables. We are currently exploring properties of the learned latent components and how they relate to the animal’s behavior. Overall, our model provides an interpretable account for the effects of spatial attention in V1 by learning the structure and timescales of fluctuations that affect shared neuronal variability.