The hippocampus and cerebellum in adaptively timed learning, recognition, and movement

The concepts of declarative memory and procedural memory have been used to distinguish two basic types of learning. A neural network model suggests how such memory processes work together as recognition learning, reinforcement learning, and sensory-motor learning take place during adaptive behaviors. To coordinate these processes, the hippocampal formation and cerebellum each contain circuits that learn to adaptively time their outputs. Within the model, hippocampal timing helps to maintain attention on motivationally salient goal objects during variable task-related delays, and cerebellar timing controls the release of conditioned responses. This property is part of the model's description of how cognitive-emotional interactions focus attention on motivationally valued cues, and how this process breaks down due to hippocampal ablation. The model suggests that the hippocampal mechanisms that help to rapidly draw attention to salient cues could prematurely release motor commands were not the release of these commands adaptively timed by the cerebellum. The model hippocampal system modulates cortical recognition learning without actually encoding the representational information that the cortex encodes. These properties avoid the difficulties faced by several models that propose a direct hippocampal role in recognition learning. Learning within the model hippocampal system controls adaptive timing and spatial orientation. Model properties hereby clarify how hippocampal ablations cause amnesic symptoms and difficulties with tasks which combine task delays, novelty detection, and attention towards goal objects amid distractions. When these model recognition, reinforcement, sensory-motor, and timing processes work together, they suggest how the brain can accomplish conditioning of multiple sensory events to delayed rewards, as during serial compound conditioning.Air Force Office of Scientific Research (F49620-92-J-0225, F49620-86-C-0037, 90-0128); Advanced Research Projects Agency (ONR N00014-92-J-4015); Office of Naval Research (N00014-91-J-4100, N00014-92-J-1309, N00014-92-J-1904); National Institute of Mental Health (MH-42900

Benchmarking Cerebellar Control

Cerebellar models have long been advocated as viable models for robot dynamics control. Building on an increasing insight in and knowledge of the biological cerebellum, many models have been greatly refined, of which some computational models have emerged with useful properties with respect to robot dynamics control. Looking at the application side, however, there is a totally different picture. Not only is there not one robot on the market which uses anything remotely connected with cerebellar control, but even in research labs most testbeds for cerebellar models are restricted to toy problems. Such applications hardly ever exceed the complexity of a 2 DoF simulated robot arm; a task which is hardly representative for the field of robotics, or relates to realistic applications. In order to bring the amalgamation of the two fields forwards, we advocate the use of a set of robotics benchmarks, on which existing and new computational cerebellar models can be comparatively tested. It is clear that the traditional approach to solve robotics dynamics loses ground with the advancing complexity of robotic structures; there is a desire for adaptive methods which can compete as traditional control methods do for traditional robots. In this paper we try to lay down the successes and problems in the fields of cerebellar modelling as well as robot dynamics control. By analyzing the common ground, a set of benchmarks is suggested which may serve as typical robot applications for cerebellar models

Electrophysiological activity from over the cerebellum and cerebrum during eye blink conditioning in human subjects

We report the results of an experiment in which electrophysiological activity was recorded from the human cerebellum and cerebrum in a sample of 14 healthy subjects before, during and after a classical eye blink conditioning procedure with an auditory tone as conditional stimulus and a maxillary nerve unconditional stimulus. The primary aim was to show changes in the cerebellum and cerebrum correlated with behavioral ocular responses. Electrodes recorded EMG and EOG at peri-ocular sites, EEG from over the frontal eye-fields and the electrocerebellogram (ECeG) from over the posterior fossa. Of the 14 subjects half strongly conditioned while the other half were resistant. We confirmed that conditionability was linked under our conditions to the personality dimension of extraversion-introversion. Inhibition of cerebellar activity was shown prior to the conditioned response, as predicted by Albus (1971). However, pausing in high frequency ECeG and the appearance of a contingent negative variation (CNV) in both central leads occurred in all subjects. These led us to conclude that while conditioned cerebellar pausing may be necessary, it is not sufficient alone to produce overt behavioral conditioning, implying the existence of another central mechanism. The outcomes of this experiment indicate the potential value of the noninvasive electrophysiology of the cerebellum

Integrated plasticity at inhibitory and excitatory synapses in the cerebellar circuit

The way long-term potentiation (LTP) and depression (LTD) are integrated within the different synapses of brain neuronal circuits is poorly understood. In order to progress beyond the identification of specific molecular mechanisms, a system in which multiple forms of plasticity can be correlated with large-scale neural processing is required. In this paper we take as an example the cerebellar network, in which extensive investigations have revealed LTP and LTD at several excitatory and inhibitory synapses. Cerebellar LTP and LTD occur in all three main cerebellar subcircuits (granular layer, molecular layer, deep cerebellar nuclei) and correspondingly regulate the function of their three main neurons: granule cells (GrCs), Purkinje cells (PCs) and deep cerebellar nuclear (DCN) cells. All these neurons, in addition to be excited, are reached by feed-forward and feed-back inhibitory connections, in which LTP and LTD may either operate synergistically or homeostatically in order to control information flow through the circuit. Although the investigation of individual synaptic plasticities in vitro is essential to prove their existence and mechanisms, it is insufficient to generate a coherent view of their impact on network functioning in vivo. Recent computational models and cell-specific genetic mutations in mice are shedding light on how plasticity at multiple excitatory and inhibitory synapses might regulate neuronal activities in the cerebellar circuit and contribute to learning and memory and behavioral control.This work was supported by European Union grants to ED [CEREBNETFP7-ITN238686, REAL NET FP7-ICT270434, Human Brain Project(HBP-604102)] and by Centro Fermi grant [13(14)] to LM

Feedback control of cerebellar learning

The ability to anticipate future events and to modify erroneous anticipatory actions is crucial for the survival of any organism. Both theoretical and empirical lines of evidence implicate the cerebellum in this ability. It is often suggested that the cerebellum acquires “expectations” or “internal models”. However, except in a metaphorical sense, the cerebellum, which consists of a set of interconnected nerve cells, cannot contain “internal models” or “have expectations”. The aim of this thesis is to untangle these metaphors by translating them back into neurophysiological cause and effect relationships. This task is approached from within the paradigm of classical conditioning, in which a subject, through repeated presentations of a conditional stimulus, followed by an unconditional stimulus, acquires a conditioned response. Importantly, the conditioned response is timed so that it anticipates the unconditioned response. Available neurophysiological evidence suggests that Purkinje cells, in the cerebellar cortex, generate the conditioned response. In addition, Purkinje cells provide negative feedback to the IO, which is a relay for the unconditional stimulus, via the nucleo-olivary pathway. Purkinje cells can therefore regulate the intensity of the signal derived from the unconditional stimulus, which, in turn, decides subsequent plasticity. Hence, as learning progresses, the IO signal will become weaker and weaker due to increasing negative feedback from Purkinje cells. Thus, in an important sense, learning induced changes in Purkinje cell activity constitute an “expectation” or “anticipation” of a future event (the unconditional stimulus), and, consistent with theoretical models, future learning depends on the accuracy of this expectation. Paper 1 in this thesis show that learned changes in Purkinje cells influences subsequent IO activity. The second paper show that, depending on the number of pulses it contains, the signal from the IO to the Purkinje cells can either cause acquisition or extinction. In the third paper we present evidence that can potentially help explain overexpectation, a behavioral phenomenon, which have for long been elusive. Collectively these papers advance our understanding of the feedback mechanisms that govern cerebellar learning and it proposes a potential solution to some long standing behavioral conundrums

Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing Issue

The cerebellar microcircuit has been the work bench for theoretical and computational modeling since the beginning of neuroscientific research. The regular neural architecture of the cerebellum inspired different solutions to the long-standing issue of how its circuitry could control motor learning and coordination. Originally, the cerebellar network was modeled using a statistical-topological approach that was later extended by considering the geometrical organization of local microcircuits. However, with the advancement in anatomical and physiological investigations, new discoveries have revealed an unexpected richness of connections, neuronal dynamics and plasticity, calling for a change in modeling strategies, so as to include the multitude of elementary aspects of the network into an integrated and easily updatable computational framework. Recently, biophysically accurate realistic models using a bottom-up strategy accounted for both detailed connectivity and neuronal non-linear membrane dynamics. In this perspective review, we will consider the state of the art and discuss how these initial efforts could be further improved. Moreover, we will consider how embodied neurorobotic models including spiking cerebellar networks could help explaining the role and interplay of distributed forms of plasticity. We envisage that realistic modeling, combined with closed-loop simulations, will help to capture the essence of cerebellar computations and could eventually be applied to neurological diseases and neurorobotic control systems

Monoamine influences in cerebellar memory consolidation

An association between climbing fibre and mossy fibre/parallel fibre inputs to the Purkinje cell is critical for cerebellar learning. In addition to these two major afferent systems, the cerebellum also receives a range of neuromodulatory inputs; most prominent are the noradrenergic and serotonergic afferents. Early theoretical and empirical accounts support a role for noradrenergic input as providing an essential third, consolidation signal in learning. In comparison to the glutamatergic afferents very little is known about the anatomy, physiology and behavioural aspects of the neuromodulatory afferents. The distributions by cell type of β1- and β2-adrenoceptors in the cerebellar cortex and nuclei and of α1-adrenoceptors in the cerebellar cortex, are shown for the first time. Earlier work demonstrated the necessity for β-adrenoceptor activation in consolidation of classical conditioning of the nictitating membrane response (NMR). Here, a dissociation of β1- and β2-adrenoceptor expression was shown. β1-adrenoceptors are restricted to Purkinje cells and β2-adrenoceptors are restricted to Bergmann glial cells. The cerebellar cortical distributions of noradrenergic and serotonergic afferents were compared. In cortical vermis, individual noradrenergic afferents were limited in their medial-lateral extent to less than 300 µm but were more extended in the rostral-caudal plane by up to 800 µm. Serotonergic afferents ran orthogonal to the noradrenergic afferents, with extents up to 900 µm in the medial-lateral plane but less than 200 µm in the rostral-caudal plane. Recent work has demonstrated a critical role for Purkinje cell mGlu7 activation in regulating the pause in Purkinje cell simple spike activity believed to be the cellular mechanism underpinning the conditioned eyelid blink/ NMR. Attempts were made to assess the specific function of the β1-adrenoceptor and mGlu7 in consolidation and performance of NMR conditioning, respectively. However, methodological constraints left these questions unresolved. It is concluded that the noradrenaline consolidation signal may target limited cortical territories and modulate Purkinje cells or Bergmann glial cells. In contrast, the serotonin signal is diffuse and targets multiple cortical regions simultaneously to fulfil a role in cerebellar processing distinct from that of noradrenergic signalling

Programming the cerebellum

It is argued that large-scale neural network simulations of cerebellar cortex and nuclei, based on realistic compartmental models of me major cell populations, are necessary before the problem of motor learning in the cerebellum can be solved, [HOUK et al.; SIMPSON et al.

The role of glutamatergic inferior olivary teaching signals in the acquisition of conditioned eyeblinks

The cerebellar learning hypothesis postulates that the inferior olive provides the cerebellum with a teaching signal that is required for acquisition and maintenance of conditioned eyeblinks. This dissertation addresses the effects of blocking inferior olivary glutamate neurotransmission on acquisition of conditioned responses

A Purkinje cell Timing Mechanism. On the Physical Basis of a Temporal Duration Memory.

The standard view of neural signaling is that a neuron can influence its target cell by exciting or inhibiting it. Learning is thought to involve strengthening or weakening synaptic connections. For most behaviors, the brain must learn to produce precisely timed activity patterns. Learned response timing is indispensable for a wide range of tasks and requires learning of interstimulus intervals (ISIs). The learning mechanism thought to accomplish this combines time-varying patterns of activity in the pre-synaptic neural network with changes in synaptic strength between the pre-synaptic neurons active at the end of the ISI and the post-synaptic neuron. Timing-dependent learning can be studied in eyeblink conditioning. If a neutral conditional stimulus is paired with an unconditional blink-eliciting stimulus, at an ISI of fixed duration, it acquires the ability to elicit a blink that peaks near the end of the ISI. Cerebellar Purkinje cells that control the blink acquire adaptively timed pauses in spontaneous firing, conditioned Purkinje cell responses, that interrupt their tonic inhibition of cerebellar nuclear cells and cause excitatory output that generates the overt blink. Most models assume the generation of a time code instantiated in varying patterns of activity in the presynaptic granule cells that represent the passage of time. However, we show here (paper I) that a cerebellar Purkinje cell can learn to respond to a specific input with adaptively timed pauses without such a temporally patterned input. Training Purkinje cells with direct stimulation of their presynaptic fibers, and pharmacological blocking of interneurons shows that the timing mechanism is intrinsic to the cell itself and not an emergent property of the network. That an individual neuron can learn temporal relationships suggests the existence of intracellular temporal duration memory. We demonstrate that this Purkinje cell memory is triggered by the metabotropic glutamate receptor 7 (paper II) and that the timed voltage response in large part is produced by the G-protein activated K+ channel family Kir3/GIRK (paper III). The implication is that a learned and adjustable timing of a metabotropic signaling cascade constitutes a physical memory of temporal duration. A theoretical model (paper IV) describes how this could be accomplished by a learning mechanism that selects among a finite number of regulatory proteins, those which bestow the intracellular signaling cascade with latencies to activation and deactivation that matches the ISI. The results presented in this thesis show that the traditional view of learning as a change in synaptic strength is insufficient. Finally, because Purkinje cells directly control the conditioned eyeblink we believe that, to our knowledge, this is the first time that a causal link can be shown between a learned and timing-dependent behavior and not only a single neuron’s memory, but also the specific activating receptor of said memory and the specific ion channel that puts it into effect