| | Models | Description |
| 1. |
Adaptive robotic control driven by a versatile spiking cerebellar network (Casellato et al. 2014)
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" ... We have coupled a realistic cerebellar spiking neural network (SNN) with a real robot and challenged it in multiple diverse sensorimotor tasks. ..." |
| 2. |
Cerebellar cortex oscil. robustness from Golgi cell gap jncs (Simoes de Souza and De Schutter 2011)
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" ... Previous one-dimensional network modeling of the cerebellar granular layer has been successfully
linked with a range of cerebellar cortex oscillations observed in vivo. However, the recent discovery of gap
junctions between Golgi cells (GoCs), which may cause oscillations by themselves, has raised the question of how
gap-junction coupling affects GoC and granular-layer oscillations. To investigate this question, we developed a
novel two-dimensional computational model of the GoC-granule cell (GC) circuit with and without gap junctions
between GoCs. ..." |
| 3. |
Cerebellar gain and timing control model (Yamazaki & Tanaka 2007)(Yamazaki & Nagao 2012)
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This paper proposes a hypothetical computational mechanism for unified gain and timing control in the cerebellum. The hypothesis is justified by computer simulations of a large-scale spiking network model of the cerebellum. |
| 4. |
Cerebellar granular layer (Maex and De Schutter 1998)
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Circuit model of the granular layer representing a one-dimensional array of single-compartmental granule cells (grcs) and Golgi cells (Gocs). This paper examines the effects of feedback inhibition (grc -> Goc -> grc) versus feedforward inhibition (mossy fibre -> Goc -> grc) on synchronization and oscillatory behaviour. |
| 5. |
Cerebellar Model for the Optokinetic Response (Kim and Lim 2021)
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We consider a cerebellar spiking neural network for the optokinetic response (OKR). Individual granule (GR) cells exhibit diverse spiking patterns which are in-phase, anti-phase, or complex out-of-phase with respect to their population-averaged firing activity. Then, these diversely-recoded signals via parallel fibers (PFs) from GR cells are effectively depressed by the error-teaching signals via climbing fibers from the inferior olive which are also in-phase ones. Synaptic weights at in-phase PF-Purkinje cell (PC) synapses of active GR cells are strongly depressed via strong long-term depression (LTD), while those at anti-phase and complex out-of-phase PF-PC synapses are weakly depressed through weak LTD. This kind of ‘‘effective’’ depression at the PF-PC synapses causes a big modulation in firings of PCs, which then exert effective inhibitory coordination on the vestibular nucleus (VN) neuron (which evokes OKR). For the firing of the VN neuron, the learning gain degree, corresponding to the modulation gain ratio, increases with increasing the learning cycle, and it saturates. |
| 6. |
Distributed synaptic plasticity and spike timing (Garrido et al. 2013)
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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 bursts, synaptic weights at multiple connections played a crucial role to regulate spike number and positioning in granule cells. 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 mossy fiber pathway. |
| 7. |
Fast oscillations in inhibitory networks (Maex, De Schutter 2003)
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We observed a new phenomenon of resonant synchronization in computer-simulated networks of inhibitory neurons in which the synaptic current has a delayed onset, reflecting finite spike propagation and synaptic transmission times. At the resonant level of network excitation, all neurons fire synchronously and rhythmically with a period approximately four times the mean delay of the onset of the inhibitory synaptic current. ... By varying the axonal delay of the inhibitory connections, networks with a realistic synaptic kinetics can be tuned to frequencies from 40 to >200 Hz. ... We conclude that the delay of the synaptic current is the primary parameter controlling the oscillation frequency of inhibitory networks and propose that delay-induced synchronization is a mechanism for fast brain rhythms that depend on intact inhibitory synaptic transmission. |
| 8. |
Network model of the granular layer of the cerebellar cortex (Maex, De Schutter 1998)
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We computed the steady-state activity of a large-scale model of the granular layer of the rat cerebellum. Within a few tens of milliseconds after the start of random mossy fiber input, the populations of Golgi and granule cells became entrained in a single synchronous oscillation, the basic frequency of which ranged from 10 to 40 Hz depending on the average rate of firing in the mossy fiber population. ... The synchronous, rhythmic firing pattern was robust over a broad range of biologically realistic parameter values and to parameter randomization. Three conditions, however, made the oscillations more transient and could desynchronize the entire network in the end: a very low mossy fiber activity, a very dominant excitation of Golgi cells through mossy fiber synapses (rather than through parallel fiber synapses), and a tonic activation of granule cell GABAA receptors (with an almost complete absence of synaptically induced inhibitory postsynaptic currents). The model predicts that, under conditions of strong mossy fiber input to the cerebellum, Golgi cells do not only control the strength of parallel fiber activity but also the timing of the individual spikes. Provided that their parallel fiber synapses constitute an important source of excitation, Golgi cells fire rhythmically and synchronized with granule cells over large distances along the parallel fiber axis. See paper for more and details. |
| 9. |
Spike burst-pause dynamics of Purkinje cells regulate sensorimotor adaptation (Luque et al 2019)
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"Cerebellar Purkinje cells mediate accurate eye movement
coordination. However, it remains unclear how oculomotor
adaptation depends on the interplay between the characteristic
Purkinje cell response patterns, namely tonic, bursting, and
spike pauses. Here, a spiking cerebellar model assesses the role
of Purkinje cell firing patterns in vestibular ocular
reflex (VOR) adaptation. The model captures the cerebellar
microcircuit properties and it incorporates spike-based synaptic
plasticity at multiple cerebellar sites. ..." |
