Cerebellar Model for the Optokinetic Response (Kim and Lim 2021)

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Accession:267334
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.
Reference:
1 . Kim SY, Lim W (2021) Effect of Diverse Recoding of Granule Cells on Optokinetic Response in A Cerebellar Ring Network with Synaptic Plasticity Neural Networks 134:173-204
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Spiking neural network;
Brain Region(s)/Organism: Cerebellum;
Cell Type(s): Cerebellum interneuron granule GLU cell; Cerebellum interneuron Golgi GABA cell; Cerebellum Purkinje GABA cell; Inferior olive neuron; Vestibular nucleus neuron;
Channel(s):
Gap Junctions:
Receptor(s): AMPA; NMDA; Gaba;
Gene(s):
Transmitter(s): Glutamate; Gaba;
Simulation Environment: C or C++ program;
Model Concept(s): Learning; Sensory processing; Effective Optokinetic Response (OKR);
Implementer(s): Kim, Sang-Yoon; Lim, Woochang;
Search NeuronDB for information about:  Cerebellum Purkinje GABA cell; Cerebellum interneuron granule GLU cell; Cerebellum interneuron Golgi GABA cell; AMPA; NMDA; Gaba; Gaba; Glutamate;
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OKR
README.txt
CI.c
IPSR.c
raster_plot.c
                            
This is the readme file for the model associated with the following paper:
      Kim SY, Lim W (2021) Effect of Diverse Recoding of Granule Cells on Optokinetic Response in A 
      Cerebellar Ring Network with Synaptic Plasticity. Neural Networks 134: 173-204

Implementation of simulation work in the above paper was done by Kim and Lim, to whom questions 
should be addressed; sykim@icn.re.kr or wclim@icn.re.kr

Our cerebellar spiking neural network is well shown in Fig. 2 of the above paper. The following source 
codes for simulation in the above paper were written in the C programming language:
      (1) raster_plot.c: code to get the raster plots of spikes for the constituent cells [Figs. 4(a), 5, 7(a), 
          8(a), 9, 11(a), 12(a), 13(b), 14(a), 17(a)]
      (2) IPSR.c: code to get the IPSR (instantaneous population spike rate) for the constituent cells 
          [Figs. 4(b), 5, 11(b)-(d2), 14(b), 15(c)-(d), 17(b)]
      (3) CI.c: code to get the conjunction index (CI) [Figs. 6, 14(c)-(e), 17(c)]

Refer to Tables A.1 – A.4 for the system parameter values. There was one variable: connection  
probability p_c from the Golgi cells to the granule cells; we mainly considered the optimal case of 
p_c=0.06 where the spiking patterns of the granule cells are the most diverse. 

Then, using the GCC complier we ran the above source codes on personal computers with Intel i5-
10210U CPUs (1.6 GHz) and 8 GB of RAM. The number of used personal computers vary (from 1 to 70) 
depending on the type of jobs. 

Using the OriginPro 2016 (Graphing & Analysis Software), we handled the output data, and all the figures 
in the above paper were made.