Linking Synaptic Plasticity and Spike Output at Excitatory and Inhibitory Synapses onto Cerebellar Purkinje Cells

Mittmann, Wolfgang; Häusser, Michael

doi:10.1523/jneurosci.5117-06.2007

Cited by 98 publications

(89 citation statements)

References 79 publications

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“…Nonetheless, recent advances in our understanding of the cerebellar circuitry suggest that numerous additional factors are likely to contribute to cerebellar motor learning. There is good evidence to suggest, for example, that synaptic connections made by interneurons and even Purkinje cells are plastic and contribute to motor learning (34)(35)(36)(37)(38). Moreover, during performance of motor tasks such as smooth eye pursuit (39) or alternate wrist movements (33), the firing rate of a Purkinje cell is smoothly modulated both below and above its resting spontaneous rate, suggesting that Purkinje cells can also encode information in decreases in firing rate.…”

Section: Discussionmentioning

confidence: 99%

The advantages of linear information processing for cerebellar computation

and

Khodakhah

2009

Proc. Natl. Acad. Sci. U.S.A.

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Purkinje cells can encode the strength of parallel fiber inputs in their firing by using 2 fundamentally different mechanisms, either as pauses or as linear increases in firing rate. It is not clear which of these 2 encoding mechanisms is used by the cerebellum. We used the pattern-recognition capacity of Purkinje cells based on the Marr-Albus-Ito theory of cerebellar learning to evaluate the suitability of the linear algorithm for cerebellar information processing. Here, we demonstrate the simplicity and versatility of pattern recognition in Purkinje cells linearly encoding the strength of parallel fiber inputs in their firing rate. In contrast to encoding patterns with pauses, Purkinje cells using the linear algorithm could recognize a large number of both synchronous and asynchronous input patterns in the presence or absence of inhibitory synaptic transmission. Under all conditions, the number of patterns recognized by Purkinje cells using the linear algorithm was greater than that achieved by encoding information in pauses. Linear encoding of information also allows neurons of deep cerebellar nuclei to use a simple averaging mechanism to significantly increase the computational capacity of the cerebellum. We propose that the virtues of the linear encoding mechanism make it well suited for cerebellar computation.cerebellum ͉ motor learning ͉ Purkinje cell P urkinje cells receive Ͼ150,000 parallel fiber synaptic inputs (1) that provide them with a vast and broad spectrum of information. These inputs are integrated with the spontaneous activity of Purkinje cells to provide the sole output of the computational circuitry of the cerebellar cortex. The mechanism by which this information is encoded by Purkinje cells is fundamental to theories of cerebellar computation. It has been recently demonstrated that Purkinje cells can encode this information by using 2 different mechanisms (2), either as pauses in their activity (3) or as linear increases in their firing rate (4) (see also Fig. 1B). Although, in principle, cerebellar computation can be based on either of these 2 encoding schemes, it is unlikely that they are used concurrently because they require fundamentally different decoding mechanisms. It has not been established whether either of these 2 mechanisms is used by the cerebellum, nor is it even known how they directly compare in their ability to encode information.Pattern recognition was proposed in a pair of seminal papers by Marr and Albus (5, 6) to be the mechanism by which cerebellar Purkinje cells learn motor tasks. Based on this theory, the more patterns the cerebellum recognizes, the higher its computational power and ability to fine-tune and learn motor tasks. Numerous attempts have been made to estimate the pattern recognition capacity of Purkinje cells (3,(5)(6)(7)(8), and recently it has been used to evaluate the suitability of an encoding mechanism in cerebellar computation (3).A recent evaluation of a detailed Purkinje cell model suggested that optimal pattern recognition capacity is obtained ...

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Section: Discussionmentioning

confidence: 99%

The advantages of linear information processing for cerebellar computation

and

Khodakhah

2009

Proc. Natl. Acad. Sci. U.S.A.

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“…Moreover, our previous simulations have not incorporated the LTD of inhibitory synapses that can be induced when the PC receives coincident CF input [2], and that could potentially counteract the effect of the depression of the excitatory PF synapses. Here, we study the effect of inhibitory synaptic plasticity on pattern recognition, and we explore a variation of our original learning rule that has been adapted to result in a better match to experimental data on LTD induction in slices [2,3].To study the effect of plasticity at the synapses between inhibitory interneurons and PCs, we presented the model with feed-forward inhibitory input, which followed the excitatory input with a time delay of 1.4 ms [1,2]. Initially, we chose an inhibition/excitation ratio of one, in the range of experimental observations in vitro [2].…”

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confidence: 99%

“…Here, we study the effect of inhibitory synaptic plasticity on pattern recognition, and we explore a variation of our original learning rule that has been adapted to result in a better match to experimental data on LTD induction in slices [2,3].…”

mentioning

confidence: 99%

“…To study the effect of plasticity at the synapses between inhibitory interneurons and PCs, we presented the model with feed-forward inhibitory input, which followed the excitatory input with a time delay of 1.4 ms [1,2]. Initially, we chose an inhibition/excitation ratio of one, in the range of experimental observations in vitro [2].…”

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Determinants of pattern recognition by cerebellar Purkinje cells

et al. 2008

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Many theories of cerebellar function assume that longterm depression (LTD) of parallel fiber (PF) synapses enables Purkinje cells (PCs) to learn to recognize PF activity patterns. According to the classic view, a PC can store and learn to distinguish PF activity patterns that have been presented repeatedly together with climbing fibre (CF) input to the cell. The resulting LTD of the PF synapses is often assumed to lead to a decreased rate of PC simple spike firing, a reduction in the inhibition of their target neurons in the deep cerebellar nuclei and thus an increased output from the cerebellum. We have recently shown by combining computer simulations with electrophysiological recordings in slices and in awake behaving mice that the readout of learned patterns in PCs may operate in a fundamentally different way. Our simulations and experiments predict that the best criterion to distinguish between learned and novel patterns is the duration of a pause in firing that occurs after presentation of a pattern, with shorter pauses in response to learned patterns [1].Although our previous simulations have used a biophysically detailed PC model that has been tuned to generate realistic behaviours under in vitro and in vivo conditions, we have applied a simplified learning rule where the AMPA receptor conductance of an active PF synapse is halved every time a PF pattern is learned. Moreover, our previous simulations have not incorporated the LTD of inhibitory synapses that can be induced when the PC receives coincident CF input [2], and that could potentially counteract the effect of the depression of the excitatory PF synapses. Here, we study the effect of inhibitory synaptic plasticity on pattern recognition, and we explore a variation of our original learning rule that has been adapted to result in a better match to experimental data on LTD induction in slices [2,3].To study the effect of plasticity at the synapses between inhibitory interneurons and PCs, we presented the model with feed-forward inhibitory input, which followed the excitatory input with a time delay of 1.4 ms [1,2]. Initially, we chose an inhibition/excitation ratio of one, in the range of experimental observations in vitro [2]. We then introduced LTD at the inhibitory synapses and evaluated the pattern recognition performance for varying numbers of learned patterns. We found that the performance was unaffected by the presence of inhibitory LTD, even in the extreme case when the inhibitory plasticity was restricted to the presentation of learned PF patterns. Our simulations predict that LTD based pattern recognition is very robust in the presence of LTD at inhibitory synapses.By dividing the synaptic weights of active PFs by two for every pattern that was learned, our original learning rule could result in very small AMPA receptor conductances for large numbers of learned patterns. However, LTD induction in cerebellar slices hardly ever results in the depression of responses to less than 50% of the pre-induction baseline [2,3]. We studied the effect o...

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Plasticity in the Cerebellar Cortex

Broussard¹

2013

The Cerebellum

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Linking Synaptic Plasticity and Spike Output at Excitatory and Inhibitory Synapses onto Cerebellar Purkinje Cells

Cited by 98 publications

References 79 publications

The advantages of linear information processing for cerebellar computation

The advantages of linear information processing for cerebellar computation

Determinants of pattern recognition by cerebellar Purkinje cells

Plasticity in the Cerebellar Cortex

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