"Congruent" and "Opposite" Neurons: Sisters for Multisensory Integration and Segregation
Experiments reveal that in the dorsal medial superior temporal (MSTd) and the ventral intraparietal (VIP) areas, where visual and vestibular cues are integrated to infer heading direction, there are two types of neurons with roughly the same number. One is "congruent" cells, whose preferred heading directions are similar in response to visual and vestibular cues; and the other is "opposite" cells, whose preferred heading directions are nearly "opposite" (with an offset of 180
• ) in response to visual vs. vestibular cues. Congruent neurons are known to be responsible for cue integration, but the computational role of opposite neurons remains largely unknown. Here, we propose that opposite neurons may serve to encode the disparity information between cues necessary for multisensory segregation. We build a computational model composed of two reciprocally coupled modules, MSTd and VIP, and each module consists of groups of congruent and opposite neurons. In the model, congruent neurons in two modules are reciprocally connected with each other in the congruent manner, whereas opposite neurons are reciprocally connected in the opposite manner. Mimicking the experimental protocol, our model reproduces the characteristics of congruent and opposite neurons, and demonstrates that in each module, the sisters of congruent and opposite neurons can jointly achieve optimal multisensory information integration and segregation. This study sheds light on our understanding of how the brain implements optimal multisensory integration and segregation concurrently in a distributed manner.
It is thought that in MSTd and VIP neural responses to visual and vestibular stimulation are integrated as part of the computation to estimate heading direction. In recent years, two relatively distinct types of neurons have been reported in both visual areas. Both types have their response level modulated by visual and vestibular input, but in different ways. Congruent neurons prefer inputs that maximally agree. Opposite neurons prefer inputs that maximally disagree. The manuscript aims to develop a computational account of the functional purpose of the opposite neurons. They suggest that opposite neurons may play an important role in segregating multimodal input. This hypothesis is not new. But the authors undertake a serious attempt to construct a computational framework that makes rigorous the hypothesis. I can quibble with certain aspects of their model. But overall, I think it is a very nice contribution.
The most significant concern I had with the manuscript was that all the modeling results were presented with a fano factor of 0. The authors took pains to develop the model so that they could take into account the effects of pseudo-realistic neural noise, but they made no attempt to justify why their presentation of results did not include it. There was not even any discussion of why the authors made this seemingly odd choice. Do results change markedly as the fano factor is manipulated? Why or why not? Are the results sensitive to the level of noise? Is there a regime in which results are qualitatively unaffected by the level of noise (as one would hope would be true in a biological system)? Or are they brittle?  Abstract: “there exist two types of neurons with comparable numbers.”  Hard to parse. “with comparable numbers”. Please rephrase.  Abstract: “the sisters of congruent and opposite neurons can jointly achieve optimal multisensory information integration and segregation”  Confusing. What are ‘sisters’ of congruent and opposite neurons.  Line 22: “Our brain senses the external world with multiple sensory modalities, including vision, audition…”   This comment is epistemological in nature, but the brain does not sense the external world. The sense organs sense proximal stimuli. The transduced proximal stimuli are then processed into estimates or representations of the external world. The authors are surely aware of this distinction but I think it is important to be clear about it in written text. There is a lot of loose language in science and the media about these issues. We practitioners should be precise.  Line 32: “The neural circuit models underlying optimal multisensory integration were also proposed…”  The neural circuit models? Maybe- ‘Neural circuit models underlying optimal multisensory integration have been proposed’  Line 39: “In an ambiguous environment, however, the brain is unable to differentiate the two situations straightforwardly”  Nonsense sentence. If input stimuli are ambiguous there is no correct answer, so it cannot be straightforward to differentiate one situation from another. Please rephrase so that the point is clear. Not sure what the authors are going for here.  Line 41: “brain is unable to perceive…”  Brains don’t perceive (or decide or realize or love). Animals and people do.   Line 51: “The psychophysical data tends to support this idea, which shows that when multisensory integration happens, the brain can still sense the difference between cues [8].”  The authors might consider citing Girshick et al 2009 here.  Line 53: “…brain to realize…”   Line 61: “modelling"  Spelling.  Line 71: “In reality, because of noises, the brain…”  Unorthodox sentence. And noisES?  Should it be ‘noise’?  Line 77- “The stimulus we consider is heading direction, which is a circular variable with values in the range of (􀀀_; _] , we therefore adopt the von Mises distribution (Supplementary Information Sec. 1).”  Ungramattical sentence.  Line 106: “The indirect cue x2  is informative to s1  via the prior p(s1; s2) . By using Eqs. (1,3) and under reasonable approximations (SI Sec. 1.4), we obtain [posterior apprx. proportional to Von Mises distribution]”   The authors should probably cite Murray & Morgenstern (2010). Their paper directly examines Bayesian cue integration of circular variables under a von Mises assumption.  Line 169: “In their model, MSTd and VIP are congruently connected, i.e., Neurons in one module are strongly connected to those in the other module having the similar preferred heading directions.”  Grammar.   Figure 4 Caption: “but the preferred direction of the opposite neuron under either cues are opposite by 180_”  Grammar.  Line 222: “We then consider the population activities of our model under different cuing conditions without  input noise (F = 0 )”  Motivate this. Why do the authors consider the response properties with a fano factor of 0, rather than a more realistic value?  Potentially confusing language. The authors refer to a fano factor as ‘input noise’. But standard usage would call the parameter, which characterizes the ratio of a neuron’s response variance to its mean, as affecting ‘output noise’ rather than ‘input noise’.   Line 252: “Over the past years, multisensory integration has received large attention in modelling studies,”   Grammar.   Line 253: “The present study proposes that opposite neurons, which has been widely observed atMSTd and VIP but with origin and computational role unresolved, serve to encode the disparity information between sensory cues.”   Hard sentence to parse.  Line 259: “To our best knowledge, this work is the first modelling study unveiling the functional role of opposite cells.”  Line 260: “It  has a far-reaching implication on multisensory information processing, that is, the brain can exploit sisters of congruent and opposite neurons to implement cue integration and segregation concurrently.”  Grammar. And what are sisters?    Appendix Line 35: “The Bayesian integration of two cues is expressed as…”  Please be more precise with the language. The equation stated is only correct under a flat prior. The authors write this later in the appendix but it would be could to state it here.  Appendix Line 55: “von pMises”   Typo.  Appendix Line 143: “The strength of the background input was Ib = 1 , and all Fano factors F  of the cues and background inputs were set to 0.5 . This resulted in a Fano factor of single neuron responses in the order of 1. In the simulation, the activity bump position was estimated by using a population vector (see below).”  Please discuss this issue in more depth. It is not clear why different units should have different fano factors.   Appendix  Line 171: “perceived by opposite neurons”   Neurons don’t perceive.