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

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

A Rescorla-Wagner Drift-Diffusion Model of Conditioning and Timing

Computational models of classical conditioning have made significant contributions to the theoretic understanding of associative learning, yet they still struggle when the temporal aspects of conditioning are taken into account. Interval timing models have contributed a rich variety of time representations and provided accurate predictions for the timing of responses, but they usually have little to say about associative learning. In this article we present a unified model of conditioning and timing that is based on the influential Rescorla-Wagner conditioning model and the more recently developed Timing Drift-Diffusion model. We test the model by simulating 10 experimental phenomena and show that it can provide an adequate account for 8, and a partial account for the other 2. We argue that the model can account for more phenomena in the chosen set than these other similar in scope models: CSC-TD, MS-TD, Learning to Time and Modular Theory. A comparison and analysis of the mechanisms in these models is provided, with a focus on the types of time representation and associative learning rule used

Ubiquitous molecular substrates for associative learning and activity-dependent neuronal facilitation.

Recent evidence suggests that many of the molecular cascades and substrates that contribute to learning-related forms of neuronal plasticity may be conserved across ostensibly disparate model systems. Notably, the facilitation of neuronal excitability and synaptic transmission that contribute to associative learning in Aplysia and Hermissenda, as well as associative LTP in hippocampal CA1 cells, all require (or are enhanced by) the convergence of a transient elevation in intracellular Ca2+ with transmitter binding to metabotropic cell-surface receptors. This temporal convergence of Ca2+ and G-protein-stimulated second-messenger cascades synergistically stimulates several classes of serine/threonine protein kinases, which in turn modulate receptor function or cell excitability through the phosphorylation of ion channels. We present a summary of the biophysical and molecular constituents of neuronal and synaptic facilitation in each of these three model systems. Although specific components of the underlying molecular cascades differ across these three systems, fundamental aspects of these cascades are widely conserved, leading to the conclusion that the conceptual semblance of these superficially disparate systems is far greater than is generally acknowledged. We suggest that the elucidation of mechanistic similarities between different systems will ultimately fulfill the goal of the model systems approach, that is, the description of critical and ubiquitous features of neuronal and synaptic events that contribute to memory induction

Multi-scale modelling of fibrillation during copper-rubber interface delamination

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Multi-scale modelling of delamination through fibrillation

Copper-rubber interfaces show extensive rubberfibrillation at the micro-scale during delamination. This constitutes a major problem for identifying unambiguous interface properties, as the micromechanics of rubber fibrillation is not taken into account in conventional (macroscopic) interface characterization methods. As a result, a significant mismatch exists between micromechanical experimental observations, such as fibril length, and the obtained macroscopic cohesive zone parameters. In this paper, the fibrillation micromechanics is explicitly taken into account in the macroscopic interface description through the use of a multi-scale interface model by means of computational homogenization. In the micro-model physical small scale phenomena such as fibril deformation and debonding are taken into account. The resulting macroscopic cohesive zone parameters are quantitatively coupled to the micromechanical quantities. The micro-model results show the growth of a fibril, including the drawing of material from the bulk into the fibril, which is accompanied by large strains that are well beyond typical bulk strain values. The micromechanical contributions to the macroscopic work-of-separation are identified. It is concluded that the intrinsic adhesion energy only constitutes a small amount of the total dissipated energy. The main fraction of the work-of-separation consists of elastically stored energy in the fibril, that is dissipated through dynamical release upon instantaneous fibril debonding. Up to the moment of debonding, the fibril micromechanics are virtually unaffected by the intrinsic adhesion properties. The influence of the intrinsic adhesion parameters on the moment of debonding is shown