This is the README for the model associated with the publication:

1. Casellato C., Antonietti A., Garrido J.A., Carrillo R.R., Luque
   N.R., Ros E., Pedrocchi A., D'Angelo E. (2014) Adaptive Robotic
   Control Driven by a Versatile Spiking Cerebellar Network. PLOS ONE.
   DOI: 10.1371/journal.pone.0112265

The cerebellum is involved in a large number of different neural
processes, especially in associative learning and in fine motor
control. To develop a comprehensive theory of sensorimotor learning
and control, it is crucial to determine the neural basis of coding and
plasticity embedded into the cerebellar neural circuit and how they
are translated into behavioral outcomes in learning paradigms.
Learning has to be inferred from the interaction of an embodied system
with its real environment, and the same cerebellar principles derived
from cell physiology have to be able to drive a variety of tasks of
different nature, calling for complex timing and movement patterns.
We have coupled a realistic cerebellar spiking neural network (SNN)
with a real robot and challenged it in multiple diverse sensorimotor
tasks. Encoding and decoding strategies based on neuronal firing rates
were applied. Adaptive motor control protocols with acquisition and
extinction phases have been designed and tested, including an
associative Pavlovian task (Eye blinking classical conditioning), a
vestibulo-ocular task and a perturbed arm reaching task operating in
closed-loop. The SNN processed in real-time mossy fiber inputs as
arbitrary contextual signals, irrespective of whether they conveyed a
tone, a vestibular stimulus or the position of a limb. A bidirectional
long-term plasticity rule implemented at parallel fibers-Purkinje cell
synapses modulated the output activity in the deep cerebellar nuclei.
In all the tasks, the neurorobot learned to adjust timing and gain of
the motor responses by tuning its output discharge. It succeeded in
reproducing how human biological systems acquire, extinguish and
express knowledge of a noisy and changing world. By varying
stimuli/perturbations patterns, real-time control robustness and
generalizability were validated. The implicit spiking dynamics of the
cerebellar model fulfill timing, prediction and learning functions.


The first step is to copy the Look-Up Tables of the neurons, from the
following link
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Both the .cfg and .dat files need to be in the directory where the
EDLUT_CEREBELLUM.exe is launched.

Then, using a Windows O.S. (32bit or 64bit) you can simply execute the
file EDLUT3.exe and choose in the prompt the protocol you want to test
(1: ISI = 200 ms, 2: ISI = 300 ms, 3: ISI = 400 ms).

 Files included in this zip:

The following files are included:

- EDLUT_CEREBELLUM.exe: the EDLUT-based cerebellar simulator used for
  the three simulations.

- EBCC_200ms_ANALYSIS.m: Matlab script to analyze the results from the
  EDLUT output files (they will be saved in the sub-folder
  /SAVED_FILES) for the protocol with ISI = 200ms. It show the raster
  plots of MF, PC, DCN and IO, the firing rate plots in 2D and 3D, the
  CR percentage evaluation and the weights modification at the level
  of PF-PC synapses.
- net.cfg: The network description files for EDLUT simulator.
- weights.dat: The initial synaptic weights file of the three

These model files were supplied by Alberto Antonietti. If you have any
question/comments/feedback, please send me an email to