Abstract
The way long-term synaptic plasticity regulates neuronal spike patterns is not completely understood. This issue is especially relevant for the cerebellum, which is endowed with several forms of long-term synaptic plasticity and has been predicted to operate as a timing and a learning machine. Here we have used a computational model to simulate the impact of multiple distributed synaptic weights in the cerebellar granular-layer network. In response to mossy fiber (MF) bursts, synaptic weights at multiple connections played a crucial role to regulate spike number and positioning in granule cells. The weight at MF to granule cell synapses regulated the delay of the first spike and the weight at MF and parallel fiber to Golgi cell synapses regulated the duration of the time-window during which the first-spike could be emitted. Moreover, the weights of synapses controlling Golgi cell activation regulated the intensity of granule cell inhibition and therefore the number of spikes that could be emitted. First-spike timing was regulated with millisecond precision and the number of spikes ranged from zero to three. Interestingly, different combinations of synaptic weights optimized either first-spike timing precision or spike number, efficiently controlling transmission and filtering properties. These results predict that distributed synaptic plasticity regulates the emission of quasi-digital spike patterns on the millisecond time-scale and allows the cerebellar granular layer to flexibly control burst transmission along the MF pathway.
Highlights
By operating in a continuously changing environment, neuronal networks have evolved precise processes regulating the number and positioning of spikes (Rieke et al, 1999)
In order to investigate the consequences of distributed synaptic plasticity on spike timing, we have analyzed the impact of weights at the different synapses of the granular layer in a spiking network model (Figure 1)
Background activity was generated by random low-frequency activity in mossy fiber (MF) and three-spike 100-Hz bursts were used to elicit network responses to impulsive stimulation
Summary
By operating in a continuously changing environment, neuronal networks have evolved precise processes regulating the number and positioning of spikes (Rieke et al, 1999). Spike timing has been revealed in afferent sensory pathways and in cortical networks (Mackevicius et al, 2012), in which millisecond-scale correlations among neurons are thought to improve information storage capacity and computational capabilities (Petersen et al, 2009; Eldawlatly and Oweiss, 2011; Kimura et al, 2011). Spike timing can be controlled by long-term synaptic plasticity (Nieus et al, 2006), which regulates the strength and dynamic properties of synaptic connections. It is not clear how differential distribution of synaptic weights could fine-tune spike timing in central networks expressing multiple distributed forms of synaptic plasticity. GrCs are endowed with specific ionic mechanisms capable of controlling the delay and persistence of spike emission (D’Angelo et al, 2001). The outstanding timing capabilities of this system have been summarized into the “time-window” hypothesis, which considers how these mechanisms compete with feed-forward synaptic inhibition mediated by Golgi cells (GoCs) in order to control spike emission
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