Neural instructive signals for associative cerebellar learning

Abstract

Supervised learning depends on instructive signals that shape the output of neural circuits to support learned changes in behavior. Climbing fiber inputs to the cerebellar cortex represent one of the strongest candidates in the vertebrate brain for conveying neural instructive signals. However, recent studies have shown that Purkinje cell stimulation can also drive cerebellar learning, and the relative importance of these two neuron types in providing instructive signals for cerebellum-dependent behaviors remains unresolved. Here we used cell-type specific perturbations of climbing fibers, Purkinje cells, and other cerebellar circuit elements to systematically evaluate their contributions to delay eyeblink conditioning. Our findings reveal that while optogenetic stimulation of either climbing fibers or Purkinje cells can substitute for a sensory unconditioned stimulus, subtle reductions in climbing fiber signaling prevent learning entirely. We conclude that climbing fibers and corresponding Purkinje cell complex-spike events provide essential instructive signals for associative cerebellar learning.

Interactions between circuit architecture and plasticity in a closed-loop cerebellar system

“…Our results reconcile two core models of cerebellar circuit function and learning. Consistent with the Marr-Albus-Ito model and many experimental studies of plasticity ( Coesmans et al, 2004 ; Gilbert and Thach, 1977 ; Ito and Kano, 1982 ; Kimpo et al, 2014 ; Medina and Lisberger, 2008 ; Sakurai, 1987 ; Silva et al, 2023 ; Yang and Lisberger, 2013 ; Yang and Lisberger, 2014 ), we find that net depression at the parallel fiber-Purkinje cell synapses can explain all recording and stimulation data, if efference copy feedback is relatively weak or absent. In particular, our model shows how such net depression can be consistent with the paradoxical increase in Purkinje cell activity during VOR cancellation, which was previously interpreted as evidence for potentiation of vestibular inputs to Purkinje cells ( Lisberger, 1994a ; Miles and Lisberger, 1981 ).…”

“…The classic Marr-Albus-Ito model assumes a feedforward architecture in which errors are reduced through changes in the synaptic inputs to Purkinje cells, the sole output neurons of the cerebellar cortex ( Figure 1C ; Marr, 1969 ; Albus, 1971 ; Ito and Kano, 1982 ). This is consistent with a large number of studies suggesting that long-term depression (LTD) occurs at the excitatory parallel fiber synapses onto Purkinje cells in response to error signals carried by climbing fiber inputs, effectively implementing reinforcement learning through error-driven plasticity (‘parallel fiber-Purkinje cell LTD’; Coesmans et al, 2004 ; Gilbert and Thach, 1977 ; Ito and Kano, 1982 ; Kimpo et al, 2014 ; Medina and Lisberger, 2008 ; Sakurai, 1987 ; Silva et al, 2023 ; Yang and Lisberger, 2013 ; Yang and Lisberger, 2014 , but see Schonewille et al, 2011 ). In contrast, later experimental observations raised the possibility that the learning-related changes in Purkinje cell firing could instead be due to feedback of changes occurring outside of the cerebellar cortex (‘Miles-Lisberger model’, Figure 1D ; Hirata and Highstein, 2001 ; Lisberger, 1994a ; Lisberger et al, 1994c ; Lisberger et al, 1994b ; Miles and Lisberger, 1981 ).…”

Cerebro-cerebellar networks facilitate learning through feedback decoupling

“…7c, d). Thus, simulated "inferior olive" lesions predict that if the cerebellum cannot learn it would result in a stronger negative impact on task learning than ablating the cerebellum itself, in line with recent experimental observations 50 . This further suggests that it is critical for the cerebellum to learn rapidly to be able to provide informative predictions.…”

How Cerebellar Architecture and Dense Activation Patterns Facilitate Online Learning in Dynamic Tasks

“…Long-term potentiation (LTP) was later discovered to lead synaptic plasticity in the absence of climbing fibre inputs [25]. Since then, multiple forms of plasticity have been found at almost every synapse in the cerebellum circuit [12, 61, 56]. It would be of great interest to apply our results to a model with a more biologically detailed learning rule.…”