Adaptive Switches in Midbrain Circuits
Authors: Tatyana O. Sharpee
Journal: Neuron (2012)

In this issue of Neuron, Mysore and Knudsen, 2012Mysore S.P. Knudsen E.I. Neuron. 2012; 73 (this issue): 193-205Abstract Full Text Full Text PDF PubMed Scopus (50) Google Scholar describe a simple, anatomically supported circuit that can categorize stimuli into “strongest” and “others,” regardless of their absolute strength. Such flexible categorization cannot be achieved by lateral inhibition alone but also requires that the inhibitory neurons reciprocally inhibit each other. In this issue of Neuron, Mysore and Knudsen, 2012Mysore S.P. Knudsen E.I. Neuron. 2012; 73 (this issue): 193-205Abstract Full Text Full Text PDF PubMed Scopus (50) Google Scholar describe a simple, anatomically supported circuit that can categorize stimuli into “strongest” and “others,” regardless of their absolute strength. Such flexible categorization cannot be achieved by lateral inhibition alone but also requires that the inhibitory neurons reciprocally inhibit each other. Selecting the most relevant stimulus in the environment is one of the key elements of behavior. It can be argued that this process begins already in early sensory systems where stimuli are transformed to emphasize their most unexpected features (Gollisch and Meister, 2010Gollisch T. Meister M. Neuron. 2010; 65: 150-164Abstract Full Text Full Text PDF PubMed Scopus (433) Google Scholar), such as differential motion (Lettvin et al., 1959Lettvin, J.Y., Maturana, H.R., McCulloch, W.S., and Pitts, W.H. (1959). What the frog's eye tells the frog's brain. Proc. IRE 47, 1940–1951.Google Scholar, Olveczky et al., 2003Olveczky B.P. Baccus S.A. Meister M. Nature. 2003; 423: 401-408Crossref PubMed Scopus (304) Google Scholar) or deviations from an anticipated trajectory (Berry et al., 1999Berry 2nd, M.J. Brivanlou I.H. Jordan T.A. Meister M. Nature. 1999; 398: 334-338Crossref PubMed Scopus (331) Google Scholar) or from expected second-order stimulus correlations (Sharpee et al., 2006Sharpee T.O. Sugihara H. Kurgansky A.V. Rebrik S.P. Stryker M.P. Miller K.D. Nature. 2006; 439: 936-942Crossref PubMed Scopus (237) Google Scholar). Many of these complex computations come about through the interplay of lateral inhibition (also called “surround suppression”) and response normalization—two features of neural circuitry that are observed almost universally in different neural systems (Olsen et al., 2010Olsen S.R. Bhandawat V. Wilson R.I. Neuron. 2010; 66: 287-299Abstract Full Text Full Text PDF PubMed Scopus (297) Google Scholar, Carandini et al., 1997Carandini M. Heeger D.J. Movshon J.A. J. Neurosci. 1997; 17: 8621-8644PubMed Google Scholar, Reynolds and Heeger, 2009Reynolds J.H. Heeger D.J. Neuron. 2009; 61: 168-185Abstract Full Text Full Text PDF PubMed Scopus (871) Google Scholar). In some cases, stimuli can be usefully categorized using predetermined boundaries (Prather et al., 2009Prather J.F. Nowicki S. Anderson R.C. Peters S. Mooney R. Nat. Neurosci. 2009; 12: 221-228Crossref PubMed Scopus (103) Google Scholar). Often, however, these boundaries need to be defined in relative terms, by separating stimuli into the “strongest” and “others” (Mysore and Knudsen, 2011Mysore S.P. Knudsen E.I. J. Neurosci. 2011; 31: 7745-7752Crossref PubMed Scopus (29) Google Scholar). The article by Mysore and Knudsen in this issue of Neuron argues that such flexible categorization cannot be achieved by lateral inhibition alone and suggests that reciprocal inhibition between neurons that mediate lateral inhibitory connections is one of the simplest and most robust ways of solving this task (Mysore and Knudsen, 2012Mysore S.P. Knudsen E.I. Neuron. 2012; 73 (this issue): 193-205Abstract Full Text Full Text PDF PubMed Scopus (50) Google Scholar). To gain an intuition for why reciprocal inhibition is necessary, we will follow the authors in considering model circuits with two inputs and two outputs (Figure 1, Figure 2). This circuit's task is to signal which of the two inputs is the strongest. Empirically, responses to a single input are nonlinear, saturating for strong inputs. They are well fit by sigmoidal functions, which is also expected on theoretical grounds when a function is minimally constrained to relate inputs to outputs (Fitzgerald et al., 2011Fitzgerald J.D. Sincich L.C. Sharpee T.O. PLoS Comput. Biol. 2011; 7: e1001111Crossref PubMed Scopus (26) Google Scholar). We will assume that all neurons in this circuit respond this way to their inputs.Figure 2A Circuit with Reciprocal Inhibition of Feedforward Lateral Inhibition Can Produce Adaptive Switch-like ResponsesShow full captionNotations are as in Figure 1. The main difference is the switch from low to high level in output 1 occurs when “competitor” input 2 exceeds the “main” input 1.View Large Image Figure ViewerDownload Hi-res image Download (PPT) Notations are as in Figure 1. The main difference is the switch from low to high level in output 1 occurs when “competitor” input 2 exceeds the “main” input 1. A circuit that includes lateral inhibition (Figure 1) can yield switch-like responses in output 1 when the strength of input 2 is increased beyond a certain value. However, the threshold value does not depend on input 1. In other words, this categorization is defined in absolute terms and does not encode the relative strength of the two inputs. In contrast, a circuit where inhibitory “interneurons” also inhibit each other produces switch-like responses (Figure 2), with thresholds that increase with the strength of input 1. Thus, this simple elaboration of the circuit is sufficient to produce switch-like responses that reflect the relative strength between the two inputs, producing nearly constant large responses to a stronger input and also nearly constant small responses to a weaker input. It is noteworthy that output responses to a weaker input are not driven to zero but remain proportional to that weak input, as observed experimentally in the intermediate and deep layers of barn owl's optic tectum (superior colliculus in mammals). This circuit behavior is in contrast to properties of winner-take-all model circuits where all information about the strength of the weaker input is abolished. The addition of reciprocal inhibition to the lateral inhibition circuit is the simplest modification that can yield adaptive switch-like responses. More complex schemes are certainly possible, but they typically are less robust and require longer times to reach the equilibrium, as Mysore and Knudsen, 2012Mysore S.P. Knudsen E.I. Neuron. 2012; 73 (this issue): 193-205Abstract Full Text Full Text PDF PubMed Scopus (50) Google Scholar demonstrate. They also show that their circuit model reproduces a range of other more subtle features in the experimental data. For example, the reciprocal inhibition circuit can, for some values of parameters, produce the sharpening of the transition between the two output states when input strength is increased (such sharpening is illustrated in Figure 2, cf. lower curves for input strength equal to 1 and upper curves for input strength equal to 6). Such sharpening was indeed observed experimentally by Mysore and Knudsen, 2012Mysore S.P. Knudsen E.I. Neuron. 2012; 73 (this issue): 193-205Abstract Full Text Full Text PDF PubMed Scopus (50) Google Scholar in some neurons. Another feature of the data is the presence of stronger suppression of responses to the weakest than to the strongest input. This feature was also accounted for by the reciprocal inhibition circuit. Finally, this circuit is supported by anatomical studies. Neurons in a midbrain GABAergic nucleus, the nucleus isthmi pars magnocellularis, receive input from retinorecipient layers of the optic tectum and send broad projections to the intermediate and deep layers (which would provide support for lateral inhibition) as well as within the nucleus itself (Wang et al., 2004Wang Y. Major D.E. Karten H.J. J. Comp. Neurol. 2004; 469: 275-297Crossref PubMed Scopus (86) Google Scholar). The latter set of connections would provide a way to implement reciprocal inhibition. The importance of the lateral inhibition circuit motif has been appreciated in a variety of sensory systems (Carandini et al., 1997Carandini M. Heeger D.J. Movshon J.A. J. Neurosci. 1997; 17: 8621-8644PubMed Google Scholar, Olsen et al., 2010Olsen S.R. Bhandawat V. Wilson R.I. Neuron. 2010; 66: 287-299Abstract Full Text Full Text PDF PubMed Scopus (297) Google Scholar). The functional implications of reciprocal inhibition between inhibitory neurons have been studied primarily with respect to decision making (Miller and Wang, 2006Miller P. Wang X.-J. Proc. Natl. Acad. Sci. USA. 2006; 103: 201-206Crossref PubMed Scopus (64) Google Scholar, Machens et al., 2005Machens C.K. Romo R. Brody C.D. Science. 2005; 307: 1121-1124Crossref PubMed Scopus (362) Google Scholar). Functional implications of reciprocal inhibition in sensory coding have been less explored, but see the recent report on the role of the “giant” GABAergic interneuron for generating sparse coding in olfaction (Papadopoulou et al., 2011Papadopolou M. Cassenaer S. Nowotny T. Laurent G. Science. 2011; 332: 721-725Crossref PubMed Scopus (142) Google Scholar). The findings of Mysore and Knudsen, 2012Mysore S.P. Knudsen E.I. Neuron. 2012; 73 (this issue): 193-205Abstract Full Text Full Text PDF PubMed Scopus (50) Google Scholar raise the possibility that reciprocal inhibition of feedforward lateral inhibition might also play an important role in sensory systems. First, anatomical support for reciprocal inhibition is found in other brain areas, such as the thalamic reticular nucleus. Second, while switch-like neural responses are crucial for categorization, they are observed only in a minority of neurons in optic tectum, with most neurons exhibiting much more gradual normalization of responses. So far, the more gradual neural responses have been the focus of normalization models in sensory coding. Perhaps, a closer look at sensory coding will also reveal the presence and functional significance of such switch-like responses here. Reciprocal Inhibition of Inhibition: A Circuit Motif for Flexible Categorization in Stimulus SelectionMysore et al.NeuronJanuary 12, 2012In BriefHow are categorical stimulus responses produced when boundaries between categories must adapt flexibly? Mysore and Knudsen show that circuits possessing a simple motif, reciprocal inhibition of feedforward lateral inhibition, categorize stimuli flexibly and efficiently, demonstrating a critical computational function for this circuit motif. Full-Text PDF Open Archive