The human cerebellum, universal transform or multiple functionality?

At what level does a uniform circuitry really imply a uniform function? Maybe it's time to rethink the "the cerebellum is an internal model" doctrine. Our review in @NeuroCellPress is out today. @Brains_CAN. https://t.co/g2QD7B780a

— DiedrichsenLab (@diedrichsenlab) June 6, 2019

The concluding discussion:

We have reviewed convergent evidence that highlights the functional diversity of the human cerebellum. This diversity makes the formulation of a domain-general theory of cerebellar function, at best, very challenging. It is also important to keep in mind that an algorithmic account of cerebellar function may entail multiple computational concepts and that these may differ across domains, an idea we termed multiple functionality.

The relative merits of the universal transform and multiple functionality hypotheses will, in the end, be an empirical question. For now, we think there is considerable value in carefully developing hypotheses of cerebellar function for specific cognitive domains without being limited, a priori, by the assumption that the function is somehow analogous to those established for motor control. For example, most of our hypotheses and experiments in the sensorimotor domain focus on the role of the cerebellar circuit in the adult organism. However, in the cognitive domain, the cerebellum may play a more important role in development than in mature function (Badura et al., 2018). Furthermore, although damage to the cerebellum in adulthood frequently results in rather subtle symptoms on cognitive and affective measures (Alexander et al., 2012), the same damage in the developing brain may have much more profound consequences. Thus, in cognitive and social domains, the cerebellum may help set up cortical circuitry during certain sensitive phases of development. When established, the cortical circuits may no longer require substantial cerebellum-based modulation. This hypothesis may be important for understanding why cerebellar dysfunction has been attributed to neuropsychiatric developmental disorders such as autism (Wang et al., 2014) and schizophrenia (Moberget et al., 2018), even though damage to the cerebellum in adulthood will not result in the symptoms associated with these disorders.

When exploring cerebellar function in each task domain, there are two critical issues that must be addressed. First, cerebellar activity should be studied in the context of the activity patterns in the cerebral cortex. In isolation, the study of cerebellar activity may lead to interesting punctuated insights, such as “the cerebellum represents reward” (Wagner et al., 2017). However, to gain a deeper understanding of cerebellar function, we need to compare cerebellar and cortical representations (Wagner et al., 2019). Do the pontine nuclei simply transmit information from the neocortex to the cerebellum in a non-selective manner, or are specific aspects of cortical representations emphasized and other aspects omitted? The circuitry in the pontine nuclei suggests that these subcortical nuclei can perform non-linear integration and gating of cortical input (Schwarz and Thier, 1999). Thus, the information reaching the cerebellum may differ in informative ways from the way it is represented in the neocortex.

Identifying these differences is likely to yield important insight into the role of the cerebellum. For example, if a cerebellar area is especially important in a specific phase of skill activation, then we would expect different activity time courses for the relevant cerebellar and cortical regions: disproportionately higher activity in early phases of learning when the cerebellum is involved in initial acquisition and disproportionately higher activity in later phases when it is important for the performance of automatized behaviors. To perform such experiments and analyses, a full model of cortical-cerebellar connectivity is required, allowing the researcher to identify the relevant pairs of cortical and cerebellar regions.

Second, it will be important to understand what information is carried by the climbing fiber system. According to the Marr-Albus-Ito model, the climbing fiber input specifies the “learning goal” for the cerebellar circuit and, therefore, plays a pivotal role in shaping the output of the cerebellum. Although the climbing fiber input has traditionally been assumed to represent an error signal, new evidence suggests that it may be better conceptualized as a general teaching signal that may sometimes also relate to reward rather than error (Heffley et al., 2018). At present, we have virtually no insight concerning the information content of the climbing fiber system in the “cognitive” regions of the human cerebellum. Thus, we do not know what these cerebellar circuits are being instructed to learn. Understanding the learning goal (or cost function) will likely provide an important key to understanding cerebellar function in the domain of cognition.

In summary, careful investigation of cerebellar function within well-specified task domains will provide a clearer picture of the functional diversity of this major subcortical structure. Looking across domains, we may ultimately discover a universal cerebellar transform. It is likely, however, that this computation will not be easily captured in the functional terms we can intuitively describe: ideas such as timing, automatization, prediction, error correction, or internal models. Rather, a common principle may only emerge in terms of a more abstract language describing the population dynamics of neuronal networks.