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. Usage: The first step is to copy the Look-Up Tables of the neurons, from the following link < a href="https://dl.dropboxusercontent.com/u/71738784/Neuron_Models.rar">https://dl.dropboxusercontent.com/u/71738784/Neuron_Models.rar. 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 protocols. These model files were supplied by Alberto Antonietti. If you have any question/comments/feedback, please send me an email to email@example.com.