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eLife.15872
Distributed Plasticity intheCerebellar
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Circuit
EgidioD’Angelo
39
Abstract
In contrast with the original Motor Learning Theory that
included a single form of plasticity at the parallel ber—
Purkinje cell synapse, recent experimental work has
revealed multiple forms of long-term synaptic and non-
synaptic plasticity (some of which are bidirectional) dis-
tributed among the cerebellar cortex and deep cerebellar
nuclei. Thus, understanding cerebellar plasticity requires
now that the spatiotemporal interplay of these multiple
mechanisms is analyzed during specic behaviors. A
recent set of experimental and modeling investigations
has opened a new view on how the multiple forms of
long-term synaptic plasticity might cooperate to generate
cerebellar learning and memory in sensorimotor control
tasks.
Keywords
Long-term synaptic plasticity · Cerebellum · Motor
control
39.1 Introduction
Learning and control have been integrated into the Motor
Learning Theory (Marr 1969; Albus 1971) and its subse-
quent extensions into the Adaptive Filter Model (Dean and
Porrill 2008), in which the cerebellum has been proposed to
learn sensorimotor contingencies and then to act as a forward
controller predicting the consequences of motor acts and
correcting intervening errors (Raymond et al. 1996; Ito
E. D’Angelo (*)
Department of Brain and Behavioral Sciences, Unit of
Neurophysiology, University of Pavia, Pavia, Italy
Brain Connectivity Center, IRCCS Mondino Foundation,
Pavia, Italy
e-mail: dangelo@unipv.it; egidiougo.dangelo@unipv.it
1984). Multiple processes may contribute to motor skill
acquisition, which proceeds through a rapid convergence
toward a stable state before being consolidated into persistent memory (Lee and Schweighofer 2009; Shadmehr etal.
2010). Although multirate models can indeed explain the
cerebellar learning process (Smith etal. 2006), the specic
role of plastic mechanisms remained unclear. These plastic
mechanisms include long-term potentiation (LTP) and longterm depression (LTD): (i) at the mossy ber (mf)—granule
cell (GrC) synapse, (ii) at the synapses formed by parallel
bers (pf), climbing bers (cf) and molecular layer interneurons (MLI: stellate and basket cells) on Purkinje cells (PC),
(iii) at the synapses between mf, aa (ascending axon), pf and
Golgi cell (GoC), and (iv) at the synapses formed by mfs and
PCs on deep-cerebellar nuclear cells (DCN-C). Moreover,
activity-dependent persistent changes of intrinsic excitability
have been reported in GrC, GoCs, PC, and DCN-C
(Fig.39.1). There are comprehensive reviews of the different
forms of cerebellar plasticity (Hansel etal. 2001; De Zeeuw
and Yeo 2005; D’Angelo and De Zeeuw 2009; Gao et al.
2012; D’Angelo 2014) and works reporting some most
recent ones (Hull et al. 2013; Mapelli et al. 2016; Sgritta
etal. 2017; Masoli etal. 2020; Locatelli etal. 2021).
Mf-GrC along with mf/aa/pf—GoC LTP and LTD are
induced and expressed through mechanisms capable of regulating the spatiotemporal pattern and dynamics of repetitive
signal transmission in the granular layer. Multiple forms of
pf-PC LTP and LTD, along with plasticity at molecular interneuron synapses, control the state of PC activation. PC-DCN
and mf-DCN LTP and LTD are regulated by mfs and PCs. In
addition, plastic changes affect intrinsic excitability in granule cells, PCs, GoCs, and DCN cells. A mechanistic synthesis of these multiple cellular level processes leverages of
AFM (Dean and Porrill 2008), as recently demonstrated
through cellular resolution mapping and realistic modeling
of the effects of LTP and LTD (Casali etal. 2020). The granular layer operates as a spatial lter tuning the time delay and
gain of spike retransmission at the cerebellum input stage,
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
D. L. Gruol et al. (eds.), Essentials of Cerebellum and Cerebellar Disorders, https://doi.org/10.1007/978-3-031-15070-8_39
259
260
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Fig. 39.1 Schematic drawing of the cerebellar circuit and its forms of
plasticity. The principal elements of the cerebellar circuit and associate
structures are indicated: mossy ber (mf), parallel ber (pf), climbing
ber (cf), granule cell (GrC), Golgi cell (GoC), Purkinje cell (PC), stellate cell (SC), basket cell (BC), deep cerebellar nuclei cell (DCN-C),
and inferior olive cell (IO-C). The cerebellar cortex is indicated by a
shadowed area. The cerebellar circuit expresses at least nine recognized
forms of plasticity, some of which bidirectional, included into three
main subcircuits. (1) Granular layer (green area): mf—GrC LTP and
LTD; GrC LTP of intrinsic excitability; mf/aa—GoC LTP and LTD. (2)
Molecular layer circuit (yellow area): presynaptic pf—PC LTP and
LTD; postsynaptic pf—PC LTP and LTD; cf.—PC LTD; SC/BC inhibitory LTP; PC LTP of intrinsic excitability; pf—GoC LTP and LTD. (3)
Deep cerebellar nuclei (red area): mf—DCN-C LTP and LTD; PC—
DCN-C inhibitory LTP and LTD; DCN cell LTP of intrinsic excitability
(Taken with permission from D’Angelo 2014)
while PCs operate a perceptron-like operation recombining
the signal transmitted along multiple granular layer channels. Therefore, LTP and LTD along with changes in intrinsic excitability can tune the ltering and combinatorial
properties of the cerebellar neural network.
E. D’Angelo
In front of this complexity of plasticity mechanisms, how
does cerebellar learning occur? Are all these forms of plasticity required to learn and control complex behaviors? How
are these forms of plasticity engaged during a learning task
(Mauk 1997; Llinas etal. 1997)?
39.2 Evidence forDistributed Cerebellar
Plasticity during Behavior
The properties of cerebellar learning can be investigated
through adaptation of the eyeblink classical conditioning
(EBCC) reex (Garcia and Mauk 1998; Medina etal. 2001),
which combines the three major aspects of cerebellar activity: learning, prediction, and timing. In EBCC, the cerebellum allows learning of appropriate timing between
conditioned (CS) and unconditioned stimuli (US), such that
of US can be precisely predicted based on the occurrence of
CS.The functions of cerebellar cortex and nuclei in EBCC
have been dissected using microinjection of the GABA A
receptor agonist muscimol (Attwell etal. 2002; Cooke etal.
2004), and computational modeling has shown that the cer-
ebellar cortex can account for the faster component and the
deep cerebellar nuclei for slower components of EBCC
learning (Medina and Mauk 2000). Moreover, dynamic
transfer of plasticity among multiple sites has been suggested to rebalance synaptic weights moving associative
learning from cortical to nuclear sites (Medina etal. 2001;
Garrido et al. 2013a). We have recently faced the issue of
EBCC learning in humans by using cerebellum transcranial
magnetic stimulation (TMS) (Monaco et al. 2014).
Interestingly, TMS pulses delivered over the oculomotor cerebellum just after EBCC training were able to disrupt the fast
mechanism but not the slow mechanism of learning or even
consolidation, suggesting that memory was acquired in
supercial structures and dynamically transferred to deeper
structures, according to the multirate model (Shadmehr etal.
2010).
39.3 Distributed Plasticity
inComputational Models
In order to investigate the interplay of multiple plasticities in
the cerebellum under realistic operating conditions, we have
integrated cerebellar network models into the feedback and
feedforward circuits of a robot (Garrido et al. 2013a;
Casellato etal. 2014) generating both the motor commands
(simulating cerebral cortex activity) and sensory signals
(derived from various sensors measuring the consequences
of movement). In robotic simulations, multiple plasticities
played different roles on different timescales (Garrido etal.
2013a). Plasticity at the pf-PC synapse rapidly acquired sen-
39 Distributed Plasticity intheCerebellar Circuit
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261
sorimotor correlations but was labile and was overwritten by
new signals. As soon as pf-PC plasticity was formed, PC ring changed and modied the synapses in the DCN. By
transferring plasticity into the DCN, the whole system
became more stable. Moreover, error feedback through sensory reafferences and the entire control system allowed plasticity self-rescaling preventing pf-PC synapse saturation. A
remarkable acceleration of learning was achieved through
plasticity in the internal feedforward loop passing from the
inferior olive (IO) to DCN (Luque etal. 2014), which allowed
system errors to be learnt in the DCN without the need of
complex signal processing through the cortical loop (granular and molecular layers). These robotic simulations thus
suggest that the multiple forms of plasticity observed in the
cerebellar network are needed to obtain exible, fast, and
stable learning as observed in biological systems.
It should be noted that these cerebellar models did not
include granular layer plasticity. Plasticity at the mf-GrC
synapse is critical to regulate the number and precision of
spikes generated by granule cells (D’Angelo and De Zeeuw
2009) and may be assisted by plastic changes at the mf/aa/
pf-Golgi cell (GoC) synapse and at the GoC-GrC synapses
(Garrido etal. 2013b) that have been recently demonstrated.
Moreover, changes in synaptic strength at the mf-GrC synapse are critical to determine the variety of granular layer
response patterns generated by the granular layer (Rössert
et al. 2014; Casali et al. 2020). Thus, since granular layer
plasticity is critical to process time-dependent multidimensional inputs, three problems need to be solved before including it into adaptive sensorimotor controllers: (i) the coding
scheme should be based on timing rather than ring rates, (ii)
the input dimensionality should be increased, and (iii) the
learning rules should be determined experimentally. The
development of large-scale spiking networks coupled to
extended sensory and command systems, as well as the
inclusion of local oscillations coupled with STDP learning
rules, may help solving the issue.
39.4 Conclusions
A new picture is emerging beyond the original intuition that
learning had to occur at the pf-PC synapse of the cerebellum
under guidance of CF signals in order to allow motor control.
Cerebellar plasticity is distributed and dynamically transferred through the different synaptic sites and can perform
various operations: it is probably needed for expansion
recoding in the granular layer (Casali et al. 2020), then it
allows fast signal association in the Purkinje cell layer,
nally it allows slow memory stabilization in the DCN
(Moscato etal. 2019). The plasticity transfer into deep structures requires internal and external feedback, and it is possible that memory traces are also transferred outside the
cerebellum, e.g., in the cerebral cortex and brainstem (Koch
etal. 2008). Cerebellar plasticity seems therefore unavoidably bound to local circuit dynamics (D’Angelo and De
Zeeuw 2009) and to the extended recurrent networks formed
by the cerebellum with extracerebellar areas.
Acknowledgments We thank Simona Tritto for technical assistance. This work received funding from the European Union’s
Horizon 2020 Framework Programme for Research and Innovation
under the Framework Partnership Agreement No. 650003 (HBP
FPA) to ED.
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Part V
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Basic Physiology
Simple Spikes andComplex Spikes
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ThomasS.Otis
40
Abstract
Cerebellar Purkinje neurons communicate with down-
stream circuit elements by generating two distinct types
of electrical activity. Purkinje neurons re conventional
action potentials, termed simple spikes, and they also
intermittently re a highly stereotyped burst of decre-
menting spikes, called a complex spike. Each of these
types of electrical activity arises from an interaction
between synaptic input and distinct excitability mecha-
nisms intrinsic to Purkinje neurons. Simple spikes occur
at very high frequencies in the range of 50 spikes per sec-
ond and are driven by pacemaking ion channels expressed
by Purkinje neurons. This high simple spike rate is then
modulated by excitatory and inhibitory synaptic input.
Complex spikes occur in response to excitatory synaptic
input from the climbing ber; these compound electrical
events are driven in part by the large voltage-gated cal-
cium conductance in the dendrites of Purkinje neurons.
Finally, the two forms of excitability interact; complex
spikes can exert indirect effects on simple spike ring
rates. Together, these two ring modes endow Purkinje
neurons with a range of signaling behaviors critical for
cerebellar contributions to motor coordination and motor
learning.
40.1 Simple andComplex Spikes
Purkinje neurons (PNs) are unusual neurons and this is particularly true of their highly distinctive electrical excitability.
PNs generate two types of regenerative electrical behavior.
As do most other neurons, they re typical, voltage-gated
sodium channel-dependent action potentials which are
termed simple spikes. In addition, they also generate distinctive burst responses that are characterized by sodium
channel- driven “spikelets” riding on a depolarized plateau
potential (see Fig.40.1). Such bursts, which are known as
complex spikes, occur in response to the very large excitatory synaptic input provided by the single climbing ber
axon that innervates each PN. Complex spikes transmit a
special type of information to PNs, denoting an error or
unexpected event; a complex spike instructs long-term
changes in the strength of the other synaptic inputs to the
PN. Below we discuss the fundamental physiological features of these two types of electrical signals and their importance for information processing within the cerebellum.
40.1.1 Simple Spikes Occur at High Rates
andAre Driven inPart by Intrinsic
Pacemaking
Keywords
Motor learning · Purkinje neuron · Excitability · Ion
channel · Resurgent current · Climbing ber
T. S. Otis (*)
Sainsbury Wellcome Centre for Neural Circuits and Behaviour,
University College London, London, UK
e-mail: t.otis@ucl.ac.uk
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
D. L. Gruol et al. (eds.), Essentials of Cerebellum and Cerebellar Disorders, https://doi.org/10.1007/978-3-031-15070-8_40
Unlike conventional neurons, PNs re action potentials constantly, even in the absence of synaptic inputs. These simple
spikes occur at high rates, ranging from 40 to 100 spikes per
second in resting animals. Moreover, with synaptic inputs
blocked, simple spikes occur at these same high rates and
with remarkable regularity (Hausser and Clark 1997)—see
also Fig.40.2. This metronome-like ability of PNs to pacemakers is a key aspect of the physiology of the cerebellum as
it allows PNs to tonically inhibit their target neurons in the
deep cerebellar nuclei and vestibular nuclei. Populations of
PNs thus cooperate to inuence motor behavior by increasing or decreasing this baseline blanket of tonic inhibition.
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T. S. Otis
Fig. 40.1 Examples of complex spikes in response to electrical stimulation of the climbing ber input (red triangle) and spontaneously occurring
simple spikes. Each panel shows two superimposed trials, one blue and one black
Other ionic currents are also critical for pacemaking
because they ensure that simple spikes are extremely brief,
an important factor allowing rapid, cyclic activation of resurgent sodium channels. Spike brevity is ensured by large
potassium conductances with rapid gating kinetics generated
by Kv3.3, Kv3.4, and calcium-activated BK channel sub-
Fig. 40.2 Extracellular recording of PN pacemaking are shown under
the indicated conditions. In the middle panel, synaptic inputs have been
blocked with a cocktail of antagonists of major classes of neurotransmitter receptors (GABA and glutamate); to the right the voltage-gated
sodium channel antagonist tetrodotoxin prevents all simple spikes
types (Raman and Bean 1999; Martina etal. 2007).
Interestingly, slowed pacemaking in Purkinje neurons is a
common physiological decit observed in transgenic mouse
models of spinocerebellar ataxia (Hourez et al. 2011;
Shakkottai etal. 2011; Hansen etal. 2013; Cook etal. 2021)
The ion channels responsible for this intrinsic pacemaking activity are known in some detail. The most important is
a subtype of voltage-gated sodium channel called the “resur-
and treatments that restore altered pacemaking improve
motor function. These results strongly suggest that simple
spike pacemaking is necessary for normal motor behavior.
gent” sodium channel, assembled from pore-forming NaV1.6
and accessory β4 subunits (Grieco et al. 2005). Resurgent
sodium channels are so named because they pass inward current as they recover from inactivation at hyperpolarized
40.1.2 Complex Spikes Occur inResponse
toClimbing Fiber Input
potentials between spikes, thereby generating a pacemakingdrive current. In mice, missense mutations in or loss of the
NaV1.6 gene result in reduced resurgent sodium current,
impaired PN pacemaking activity, and ataxia (Raman etal.
1997).
Mature PNs receive input from the terminal arbor of a
single olivary neuron, the climbing ber, which forms a
powerful excitatory synapse onto the proximal dendritic
tree. The postsynaptic response in the PN to climbing
40 Simple Spikes andComplex Spikes
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267
ber input is a complex spike (Eccles etal. 1964). This
burst response activates CaV3 type (a.k.a. T type) and
CaV2.1 (a.k.a. P/Q type) voltage-gated calcium channels
which are densely distributed throughout PN dendrites
(Swensen and Bean 2003). Although dependent on membrane potential, the complex spike waveform is remarkably stereotyped (Davie etal. 2008). In this way, a single
climbing ber input can serve as a salient, cell-wide signal leading to increased calcium concentrations throughout much of the PN dendritic tree and cell soma (Tank
et al. 1988; Kitamura and Hausser 2011). This is an
important capability as climbing bers convey a teaching
signal to the cerebellum that drives circuit changes underlying associative forms of motor learning (Mauk et al.
1986; Raymond etal. 1996; Medina and Lisberger 2008);
Otis etal. 2012).
40.1.3 Complex Spikes Transiently Inhibit
Simple Spike Firing
Independent of their effects instructing changes in synaptic strength, complex spikes are known to transiently slow
rates of simple spike ring. On a rapid time scale of tens
of milliseconds, complex spikes briey inhibit simple
spike pacemaking. This either results in fewer simple
spikes immediately following a complex spike, termed a
“post-CS pause,” or it results in a period reset in which
resumption of simple spike ring is delayed and phase
shifted (Bell and Grimm 1969). Such rapid inhibition is
due to a combination of climbing ber-driven feedforward
inhibition, and activation of SK calcium-activated potassium channels in PNs (Mathews et al. 2012). Post-CS
pauses may play a role in transmitting a teaching signal to
the deep cerebellar nucleus by providing a synchronous
disinhibition of these target neurons, thereby enabling
circuit-wide modications known to occur during learning (Otis etal. 2012).
On a slower time scale of many seconds, complex spike
rates, which typically average 1Hz but can be suppressed
or driven experimentally, show an inverse relationship
with simple spike rates (Cerminara and Rawson 2004).
Indeed, rates of complex spikes and simple spikes are
often strongly anticorrelated in response to periodic sensory stimuli (Barmack and Yakhnitsa 2011). This anticorrelation likely arises from the same mechanisms mentioned
above; however, it may also reect learning. Repeated
occurrence of complex spikes with specic patterns of
parallel ber synaptic input would result in long-term
changes in excitability of PNs in response to those parallel ber inputs. In this way, the intrinsic mechanisms linking complex and simple spikes can be solidied and
reinforced through experience.
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