Short-lasting conditioned stimulus applied to the middle cerebellar peduncle elicits delayed conditioned eye blink responses in the decerebrate ferret

A computational passage-of-time model of the cerebellar Purkinje cell in eyeblink conditioning

The cerebellar Purkinje cell controlling eyeblinks can learn, remember, and reproduce the interstimulus interval in a classical conditioning paradigm. Given temporally separated inputs, the cerebellar Purkinje cell learns to pause its tonic inhibition of a motor pathway with high temporal precision so that an overt blink occurs at the right time. Most models place the passage-of-time representation in upstream network effects. Yet, bypassing the upstream network and directly stimulating the Purkinje cell's pre-synaptic fibers during conditioning still causes acquisition of a well-timed response. Additionally, while network models are sensitive to variance in the temporal structure of probe stimulation, in vivo findings suggest that the acquired Purkinje cell response is not. Such findings motivate alternative approaches to modeling neural function. Here, we present a proof-of-principle model of the passage-of-time which is internal to the Purkinje cell and is invariant to probe structure. The model is consistent with puzzling findings, accurately recapitulates Purkinje cell firing during classical conditioning and makes testable electrophysiological predictions.

Intrinsic memory of temporal intervals in cerebellar Purkinje cells

The general consensus for learning and memory, including in the cerebellum, is that modification of synaptic strength via long-term potentiation (LTP) or long-term depression (LTD) are the primary mechanisms for the formation of memories. Recent findings suggest additional cellular mechanisms - referred to as 'intrinsic plasticity' - where a neuron's membrane excitability intrinsically changes. These mechanisms act like a dimmer and alter neuronal responsiveness by adjusting response amplitudes and spike thresholds. Here, I argue that classical conditioning of cerebellar Purkinje cell responses reveals yet another cell-intrinsic learning mechanism which significantly differs from both changes in synaptic strength and changes in membrane excitability. When the conditional (CS) and unconditional stimuli (US) are delivered directly to the Purkinje cell's immediate pre-synaptic afferents, the parallel fibres and the climbing fibre, the cell learns to respond to the CS with a pause in its spontaneous firing that reflects the interval between the two stimuli. The pause response has a delayed onset and adaptively timed maximum, offset and duration, determined by the previously experienced CS-US interval. The timing is not dependent on any network-generated time-varying input. This implies the existence of a timing mechanism and a memory substrate that encodes the duration of the CS-US interval inside the Purkinje cell. Such temporal interval learning is not simply a change that causes more or less firing in response to an input. Here, I review these findings in relation to the standard theory of synaptic strength changes and the network interactions believed to be necessary for generating time codes.

A cerebellar adaptation to uncertain inputs

Noise and variability are inherent and unavoidable features of neural processing. Despite this physiological challenge, brain systems function well, suggesting the existence of adaptations that cope with noise. We report a novel adaptation that the cerebellum implements to maintain correct responses in the face of ambiguous inputs. We found that under these conditions, the cerebellum used a probabilistic binary choice: Although the probability of behavioral response gradually increased or decreased depending on the degree of similarity between current and trained inputs, the size of response remained constant. That way the cerebellum kept responses adaptive to trained input corrupted by noise while minimizing false responses to novel stimuli. Recordings and analysis of Purkinje cells activity showed that the binary choice is made in the cerebellar cortex. Results from large-scale simulation suggest that internal feedback from cerebellar nucleus back to cerebellar cortex plays a critical role in implementation of binary choice.

Learned response sequences in cerebellar Purkinje cells

Associative learning in the cerebellum has previously focused on single movements. In eyeblink conditioning, for instance, a subject learns to blink at the right time in response to a conditional stimulus (CS), such as a tone that is repeatedly followed by an unconditional corneal stimulus (US). During conditioning, the CS and US are transmitted by mossy/parallel fibers and climbing fibers to cerebellar Purkinje cells that acquire a precisely timed pause response that drives the overt blink response. The timing of this conditional Purkinje cell response is determined by the CS-US interval and is independent of temporal patterns in the input signal. In addition to single movements, the cerebellum is also believed to be important for learning complex motor programs that require multiple precisely timed muscle contractions, such as, for example, playing the piano. In the present work, we studied Purkinje cells in decerebrate ferrets that were conditioned using electrical stimulation of mossy fiber and climbing fiber afferents as CS and US, while alternating between short and long interstimulus intervals. We found that Purkinje cells can learn double pause responses, separated by an intermediate excitation, where each pause corresponds to one interstimulus interval. The results show that individual cells can not only learn to time a single response but that they also learn an accurately timed sequential response pattern.

Are Purkinje Cell Pauses Drivers of Classically Conditioned Blink Responses?

Several lines of evidence show that classical or Pavlovian conditioning of blink responses depends on the cerebellum. Recordings from cerebellar Purkinje cells that control the eyelid and the conditioned blink show that during training with a conditioning protocol, a Purkinje cell develops a pause response to the conditional stimulus. This conditioned cellular response has many of the properties that characterise the overt blink. The present paper argues that the learned Purkinje cell pause response is the memory trace and main driver of the overt conditioned blink and that it explains many well-known behavioural phenomena.

Mechanisms for motor timing in the cerebellar cortex

In classical eyeblink conditioning a subject learns to blink to a previously neutral stimulus. This conditional response is timed to occur just before an air puff to the eye. The learning is known to depend on the cerebellar cortex where Purkinje cells respond with adaptively timed pauses in their spontaneous firing. The pauses in the inhibitory Purkinje cells cause disinhibition of the cerebellar nuclei, which elicit the overt blinks. The timing of a Purkinje cell response was previously thought to require a temporal code in the input signal but recent work suggests that the Purkinje cells can learn to time their responses through an intrinsic mechanism that is activated by metabotropic glutamate receptors (mGluR7).

Theoretical considerations for understanding a Purkinje cell timing mechanism

In classical conditioning, cerebellar Purkinje cells learn an adaptively timed pause in spontaneous firing. This pause reaches its maximum near the end of the interstimulus interval. While it was thought that this timing was due to temporal patterns in the input signal and selective engagement of changes in synapse strength, we have shown Purkinje cells learn timed responses even when the conditional stimulus is delivered to its immediate afferents.¹ This shows that Purkinje cells have a cellular timing mechanism. The cellular models of intrinsic timing we are aware of are based on adapting the rise time of the concentration of a given ion. As an alternative, we here propose a selection mechanism in abstract terms for how a Purkinje cell could learn to respond at a particular time after an external trigger.

Memory trace and timing mechanism localized to cerebellar Purkinje cells

The standard view of the mechanisms underlying learning is that they involve strengthening or weakening synaptic connections. Learned response timing is thought to combine such plasticity with temporally patterned inputs to the neuron. We show here that a cerebellar Purkinje cell in a ferret can learn to respond to a specific input with a temporal pattern of activity consisting of temporally specific increases and decreases in firing over hundreds of milliseconds without a temporally patterned input. Training Purkinje cells with direct stimulation of immediate afferents, the parallel fibers, and pharmacological blocking of interneurons shows that the timing mechanism is intrinsic to the cell itself. Purkinje cells can learn to respond not only with increased or decreased firing but also with an adaptively timed activity pattern.

Time course of classically conditioned Purkinje cell response is determined by initial part of conditioned stimulus

Classical conditioning of a motor response such as eyeblink is associated with the development of a pause in cerebellar Purkinje cell firing that is an important driver of the overt response. This conditioned Purkinje cell response is adaptively timed and has a specific temporal profile that probably explains the time course of the overt behavior. It is generally assumed that the temporal properties of the conditioned Purkinje cell response are determined by the temporal pattern of the parallel fiber impulses generated by the conditioned stimulus at the time of the conditioned response. We show here in the decerebrate ferret preparation that a very brief conditioned stimulus, consisting of only one or two impulses in the mossy fibers, can be sufficient to elicit a full conditioned Purkinje cell response with normal time course. The finding suggests that parallel fiber input to the Purkinje cell influences the firing rate several hundred milliseconds later. It poses a serious challenge to the standard view of the role of parallel fiber impulses in response timing.

Computational models of timing mechanisms in the cerebellar granular layer

A long-standing question in neuroscience is how the brain controls movement that requires precisely timed muscle activations. Studies using Pavlovian delay eyeblink conditioning provide good insight into this question. In delay eyeblink conditioning, which is believed to involve the cerebellum, a subject learns an interstimulus interval (ISI) between the onsets of a conditioned stimulus (CS) such as a tone and an unconditioned stimulus such as an airpuff to the eye. After a conditioning phase, the subject’s eyes automatically close or blink when the ISI time has passed after CS onset. This timing information is thought to be represented in some way in the cerebellum. Several computational models of the cerebellum have been proposed to explain the mechanisms of time representation, and they commonly point to the granular layer network. This article will review these computational models and discuss the possible computational power of the cerebellum.

Interaction between Purkinje cells and inhibitory interneurons may create adjustable output waveforms to generate timed cerebellar output

We develop a new model that explains how the cerebellum may generate the timing in classical delay eyeblink conditioning. Recent studies show that both Purkinje cells (PCs) and inhibitory interneurons (INs) have parallel signal processing streams with two time scales: an AMPA receptor-mediated fast process and a metabotropic glutamate receptor (mGluR)-mediated slow process. Moreover, one consistent finding is an increased excitability of PC dendrites (in Larsell's lobule HVI) in animals when they acquire the classical delay eyeblink conditioning naturally, in contrast to in vitro studies, where learning involves long-term depression (LTD). Our model proposes that the delayed response comes from the slow dynamics of mGluR-mediated IP3 activation, and the ensuing calcium concentration change, and not from LTP/LTD. The conditioned stimulus (tone), arriving on the parallel fibers, triggers this slow activation in INs and PC spines. These excitatory (from PC spines) and inhibitory (from INs) signals then interact at the PC dendrites to generate variable waveforms of PC activation. When the unconditioned stimulus (puff), arriving on the climbing fibers, is coupled frequently with this slow activation the waveform is amplified (due to an increased excitability) and leads to a timed pause in the PC population. The disinhibition of deep cerebellar nuclei by this timed pause causes the delayed conditioned response. This suggested PC-IN interaction emphasizes a richer role of the INs in learning and also conforms to the recent evidence that mGluR in the cerebellar cortex may participate in slow motor execution. We show that the suggested mechanism can endow the cerebellar cortex with the versatility to learn almost any temporal pattern, in addition to those that arise in classical conditioning.

From parallel sequence representations to calligraphic control: a conspiracy of neural circuits

Calligraphic writing presents many challenges for motor control, including: learning and recall of stroke sequences; critical timing of stroke onsets and durations; fine control of grip and contact forces; and letterform invariance under size scaling, which entails fine control of stroke directions and amplitudes during recruitment and derecruitment of musculoskeletal degrees of freedom. Experimental and computational studies in behavioral neuroscience have progressed toward explaining the learning, planning, and control exercised in tasks that share features with calligraphic writing and drawing. This article highlights component operations ranging from parallel sequence representations to fine force control. Treated in succession are: competitive queuing models of sequence representation, performance, learning, and recall; letter size scaling and motor equivalence; cursive handwriting models in which sensory-motor transformations are performed by circuits that learn inverse differential kinematic mappings; and fine-grained control of timing and transient forces by circuit models that learn to solve inverse dynamics problems.

Neural Substrates of Eyeblink Conditioning: Acquisition and Retention

Classical conditioning of the eyeblink reflex to a neutral stimulus that predicts an aversive stimulus is a basic form of associative learning. Acquisition and retention of this learned response require the cerebellum and associated sensory and motor pathways and engage several other brain regions including the hippocampus, neocortex, neostriatum, septum, and amygdala. The cerebellum and its associated circuitry form the essential neural system for delay eyeblink conditioning. Trace eyeblink conditioning, a learning paradigm in which the conditioned and unconditioned stimuli are noncontiguous, requires both the cerebellum and the hippocampus and exhibits striking parallels to declarative memory formation in humans. Identification of the neural structures critical to the development and maintenance of the conditioned eyeblink response is an essential precursor to the investigation of the mechanisms responsible for the formation of these associative memories. In this review, we describe the evidence used to identify the neural substrates of classical eyeblink conditioning and potential mechanisms of memory formation in critical regions of the hippocampus and cerebellum. Addressing a central goal of behavioral neuroscience, exploitation of this simple yet robust model of learning and memory has yielded one of the most comprehensive descriptions to date of the physical basis of a learned behavior in mammals.

Trace eyeblink conditioning in decerebrate guinea pigs

We investigated the trace eyeblink conditioning in decerebrate guinea pigs to elucidate the possible role of the cerebellum and brainstem in this hippocampus-dependent task. A 350-ms tone conditioned stimulus was paired with a 100-ms periorbital shock unconditioned stimulus with a trace interval of either 0, 100, 250 or 500 ms. Decerebrate animals readily acquired the conditioned response with a trace interval of 0 or 100 ms. Even in the paradigm with a 500-ms trace interval, which is known to depend critically on the hippocampus in all animal species examined, the decerebrate guinea pigs acquired the conditioned response, which had adaptive timing as well as in the other paradigms with a shorter trace interval. However, it took many more trials to learn in the 500-ms trace paradigm than in the shorter trace interval paradigms, and the conditioned response expression was unstable from trial to trial. When decerebrate animals were conditioned step by step with a trace interval of 100, 250 and 500 ms, sequentially, they easily acquired the adaptive conditioned response to a 500-ms trace interval. However, the frequency of conditioned responses decreased after the trace interval was shifted from 250 ms to 500 ms, which was not observed after the shift from 100 ms to 250 ms. These results suggest that the cerebellum and brainstem could maintain the 'trace' of the conditioned stimulus and associate it with the unconditioned stimulus even in the 500-ms trace paradigm, but that the forebrain might be required for facilitating and stabilizing the association.

A biophysical model of nonlinear dynamics underlying plateau potentials and calcium spikes in purkinje cell dendrites

Computational capabilities of Purkinje cells (PCs) are central to the cerebellum function. Information originating from the whole nervous system converges on their dendrites, and their axon is the sole output of the cerebellar cortex. PC dendrites respond to weak synaptic activation with long-lasting, low-amplitude plateau potentials, but stronger synaptic activation can generate fast, large amplitude calcium spikes. Pharmacological data have suggested the involvement of only the P-type of Ca channels in both of these electric responses. However, the mechanism allowing this Ca current to underlie responses with such different dynamics is still unclear. This mechanism was explored by constraining a biophysical model with electrophysiological, Ca-imaging, and single ion channel data. A model is presented here incorporating a simplified description of [Ca](i) regulation and three ionic currents: 1) the P-type Ca current, 2) a delayed-rectifier K current, and 3) a generic class of K channels activating sharply in the sub-threshold voltage range. This model sustains fast spikes and long-lasting plateaus terminating spontaneously with recovery of the resting potential. Small depolarizing, tonic inputs turn plateaus into a stable membrane state and endow the dendrite with bistability properties. With larger tonic inputs, the plateau remains the unique equilibrium state, showing long traces of transient inhibitory inputs that are called "valley potentials" because their dynamics mirrors that of inverted, finite-duration plateaus. Analyzing the slow subsystem obtained by assuming instantaneous activation of the delayed-rectifier reveals that the time course of plateaus and valleys is controlled by the slow [Ca](i) dynamics, which arises from the high Ca-buffering capacity of PCs. A bifurcation analysis shows that tonic currents modulate sub-threshold dynamics by displacing the resting state along a hysteresis region edged by two saddle-node bifurcations; these bifurcations mark transitions from finite-duration plateaus to bistability and from bistability to valley potentials, respectively. This low-dimensionality model may be introduced into large-scale models to explore the role of PC dendrite computations in the functional capabilities of the cerebellum.

The cerebellum in action: a simulation and robotics study

The control or prediction of the precise timing of events are central aspects of the many tasks assigned to the cerebellum. Despite much detailed knowledge of its physiology and anatomy, it remains unclear how the cerebellar circuitry can achieve such an adaptive timing function. We present a computational model pursuing this question for one extensively studied type of cerebellar-mediated learning: the classical conditioning of discrete motor responses. This model combines multiple current assumptions on the function of the cerebellar circuitry and was used to investigate whether plasticity in the cerebellar cortex alone can mediate adaptive conditioned response timing. In particular, we studied the effect of changes in the strength of the synapses formed between parallel fibres and Purkinje cells under the control of a negative feedback loop formed between inferior olive, cerebellar cortex and cerebellar deep nuclei. The learning performance of the model was evaluated at the circuit level in simulated conditioning experiments as well as at the behavioural level using a mobile robot. We demonstrate that the model supports adaptively timed responses under real-world conditions. Thus, in contrast to many other models that have focused on cerebellar-mediated conditioning, we investigated whether and how the suggested underlying mechanisms could give rise to behavioural phenomena.