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This work was supported by European Union Human Brain Project Specific Grant Agreement 3 (H2020-RIA. 945539); European Union Neuro Cerebellar Recurrent Network for Motor Sequence Learning in Neurorobotics: NEUSEQBOT (891774); European Union and Junta de Andalucía, CEREBIO (P18-FR-2378); FEDER-Junta de Andalucía (A-TIC-276-UGR18); and the National Grants INTSENSO & SPIKEAGE (MICINN-FEDER-PID2019-109991GB-I00 & PID2020-113422GA-I00)., The presence of computation and transmission-variable time delays within a robotic control loop is a major cause of instability, hindering safe human-robot interaction (HRI) under these circumstances. Classical control theory has been adapted to counteract the presence of such variable delays; however, the solutions provided to date cannot cope with HRI robotics inherent features. The highly nonlinear dynamics of HRI cobots (robots intended for human interaction in collaborative tasks), together with the growing use of flexible joints and elastic materials providing passive compliance, prevent traditional control solutions from being applied. Conversely, human motor control natively deals with low power actuators, nonlinear dynamics, and variable transmission time delays. The cerebellum, pivotal to human motor control, is able to predict motor commands by correlating current and past sensorimotor signals, and to ultimately compensate for the existing sensorimotor human delay (tens of milliseconds). This work aims at bridging those inherent features of cerebellar motor control and current robotic challenges—namely, compliant control in the presence of variable sensorimotor delays. We implement a cerebellar-like spiking neural network (SNN) controller that is adaptive, compliant, and robust to variable sensorimotor delays by replicating the cerebellar mechanisms that embrace the presence of biological delays and allow motor learning and adaptation., European Union Human Brain Project Specific Grant Agreement 3 (H2020-RIA. 945539), European Union Neuro Cerebellar Recurrent Network for Motor Sequence Learning in Neurorobotics: NEUSEQBOT (891774), European Union and Junta de Andalucía, CEREBIO (P18-FR-2378), FEDER-Junta de Andalucía (A-TIC-276-UGR18), National Grants INTSENSO & SPIKEAGE (MICINN-FEDER-PID2019-109991GB-I00 & PID2020-113422GA-I00)

This article integrates work from authors from different research groups and has been funded by the EU Grant HBP (H2020 SGA3. 945539), the Spanish Ministry of Economy and Competitiveness (RTI2018-095993-B-I00), the national grant INTSENSO (MICINN-FEDER-PID2019-109991GB-I00), the regional grants of Junta de Andalucia (CEREBIO: JA FEDER P18-FR-2378, P18-RT-1193, and A-TIC-276-UGR18), and the University of Almeria (UAL18-TIC-A020-B)., This article extends a recent methodological workflow for creating realistic and computationally efficient neuron models whilst capturing essential aspects of singleneuron dynamics. We overcome the intrinsic limitations of the extant optimization methods by proposing an alternative optimization component based on multimodal algorithms. This approach can natively explore a diverse population of neuron model configurations. In contrast to methods that focus on a single global optimum, the multimodal method allows directly obtaining a set of promising solutions for a single but complex multi-feature objective function. The final sparse population of candidate solutions has to be analyzed and evaluated according to the biological plausibility and their objective to the target features by the expert. In order to illustrate the value of this approach, we base our proposal on the optimization of cerebellar granule cell (GrC) models that replicate the essential properties of the biological cell. Our results show the emerging variability of plausible sets of values that this type of neuron can adopt underlying complex spiking characteristics. Also, the set of selected cerebellar GrC models captured spiking dynamics closer to the reference model than the single model obtained with off-the-shelf parameter optimization algorithms used in our previous article. The method hereby proposed represents a valuable strategy for adjusting a varied population of realistic and simplified neuron models. It can be applied to other kinds of neuron models and biological contexts., EU Grant HBP H2020 SGA3. 945539, Spanish Ministry of Economy and Competitiveness RTI2018-095993-B-I00, national grant INTSENSO MICINN-FEDER-PID2019-109991GB-I00, Junta de Andalucia CEREBIO: JA FEDER P18-FR-2378 P18-RT-1193 A-TIC-276-UGR18, University of Almeria UAL18-TIC-A020-B

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. A detailed Purkinje cell model reproduces the three spike-firing patterns that are shown to regulate the cerebellar output. Our results suggest that pauses following Purkinje complex spikes (bursts) encode transient disinhibition of targeted medial vestibular nuclei, critically gating the vestibular signals conveyed by mossy fibres. This gating mechanism accounts for early and coarse VOR acquisition, prior to the late reflex consolidation. In addition, properly timed and sized Purkinje cell bursts allow the ratio between long-term depression and potentiation (LTD/LTP) to be finely shaped at mossy fibre-medial vestibular nuclei synapses, which optimises VOR consolidation. Tonic Purkinje cell firing maintains the consolidated VOR through time. Importantly, pauses are crucial to facilitate VOR phase-reversal learning, by reshaping previously learnt synaptic weight distributions. Altogether, these results predict that Purkinje spike burst-pause dynamics are instrumental to VOR learning and reversal adaptation.Author SummaryCerebellar Purkinje cells regulate accurate eye movement coordination. However, it remains unclear how cerebellar-dependent oculomotor adaptation depends on the interplay between Purkinje cell characteristic response patterns: tonic, high-frequency bursting, and post-complex spike pauses. We explore the role of Purkinje spike burst-pause dynamics in VOR adaptation. A biophysical model of Purkinje cell is at the core of a spiking network model, which captures the cerebellar microcircuit properties and incorporates spike-based synaptic plasticity mechanisms at different cerebellar sites. We show that Purkinje spike burst-pause dynamics are critical for (1) gating the vestibular-motor response association during VOR acquisition; (2) mediating the LTD/LTP balance for VOR consolidation; (3) reshaping synaptic efficacy distributions for VOR phase-reversal adaptation; (4) explaining the reversal VOR gain discontinuities during sleeping.

Time-driven simulation methods in traditional CPU architectures perform well and precisely when simulating small-scale spiking neural networks. Nevertheless, they still have drawbacks when simulating large-scale systems. Conversely, event-driven simulation methods in CPUs and time-driven simulation methods in graphic processing units (GPUs) can outperform CPU time-driven methods under certain conditions. With this performance improvement in mind, we have developed an event-and-time-driven spiking neural network simulator suitable for a hybrid CPU-GPU platform. Our neural simulator is able to efficiently simulate bio-inspired spiking neural networks consisting of different neural models, which can be distributed heterogeneously in both small layers and large layers or subsystems. For the sake of efficiency, the low-activity parts of the neural network can be simulated in CPU using event-driven methods while the high-activity subsystems can be simulated in either CPU (a few neurons) or GPU (thousands or millions of neurons) using time-driven methods. In this brief, we have undertaken a comparative study of these different simulation methods. For benchmarking the different simulation methods and platforms, we have used a cerebellar-inspired neural-network model consisting of a very dense granular layer and a Purkinje layer with a smaller number of cells (according to biological ratios). Thus, this cerebellar-like network includes a dense diverging neural layer (increasing the dimensionality of its internal representation and sparse coding) and a converging neural layer (integration) similar to many other biologically inspired and also artificial neural networks.

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fninf. 2017.00007/full#supplementary-material, Modeling and simulating the neural structures which make up our central neural system is instrumental for deciphering the computational neural cues beneath. Higher levels of biological plausibility usually impose higher levels of complexity in mathematical modeling, from neural to behavioral levels. This paper focuses on overcoming the simulation problems (accuracy and performance) derived from using higher levels of mathematical complexity at a neural level. This study proposes different techniques for simulating neural models that hold incremental levels of mathematical complexity: leaky integrate-and-fire (LIF), adaptive exponential integrate-and-fire (AdEx), and Hodgkin-Huxley (HH) neural models (ranged from low to high neural complexity). The studied techniques are classified into two main families depending on how the neural-model dynamic evaluation is computed: the event-driven or the time-driven families. Whilst event-driven techniques pre-compile and store the neural dynamics within look-up tables, time-driven techniques compute the neural dynamics iteratively during the simulation time. We propose two modifications for the event-driven family: a look-up table recombination to better cope with the incremental neural complexity together with a better handling of the synchronous input activity. Regarding the time-driven family, we propose a modification in computing the neural dynamics: the bi-fixed-step integration method. This method automatically adjusts the simulation step size to better cope with the stiffness of the neural model dynamics running in CPU platforms. One version of this method is also implemented for hybrid CPU-GPU platforms. Finally, we analyze how the performance and accuracy of these modifications evolve with increasing levels of neural complexity. We also demonstrate how the proposed modifications which constitute the main contribution of this study systematically outperform the traditional event- and time-driven techniques under increasing levels of neural complexity., This study was supported by the European Union NR (658479-Spike Control), the Spanish National Grant NEUROPACT (TIN2013-47069-P) and by the Spanish National Grant PhD scholarship (AP2012-0906). We gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan GPUs for current EDLUT development.

This paper presents how a simple cerebellumlike architecture can infer corrective models in the framework of a control task when manipulating objects that significantly affect the dynamics model of the system. The main motivation of this paper is to evaluate a simplified bio-mimetic approach in the framework of a manipulation task. More concretely, the paper focuses on how the model inference process takes place within a feedforward control loop based on the cerebellar structure and on how these internal models are built up by means of biologically plausible synaptic adaptation mechanisms. This kind of investigation may provide clues on how biology achieves accurate control of non-stiff-joint robot with low-power actuators which involve controlling systems with high inertial components. This paper studies how a basic temporal-correlation kernel including long-term depression (LTD) and a constant long-term potentiation (LTP) at parallel fiber-Purkinje cell synapses can effectively infer corrective models. We evaluate how this spike-timing-dependent plasticity correlates sensorimotor activity arriving through the parallel fibers with teaching signals (dependent on error estimates) arriving through the climbing fibers from the inferior olive. This paper addresses the study of how these LTD and LTP components need to be well balanced with each other to achieve accurate learning. This is of interest to evaluate the relevant role of homeostatic mechanisms in biological systems where adaptation occurs in a distributed manner. Furthermore, we illustrate how the temporal-correlation kernel can also work in the presence of transmission delays in sensorimotor pathways. We use a cerebellumlike spiking neural network which stores the corrective models as well-structured weight patterns distributed among the parallel fibers to Purkinje cell connections.

Local features such as phase, energy and orientation have been widely used in the literature for multiple computer vision applications such as edge detection, texture analysis, motion estimation, depth computation, etc. They can be computed using wavelet image decomposing based on Quadrature filters tuned at different scales and orientations. Although this represents a very powerful image analysis technique, its feasibility for real-time domain applications is still beyond current computer capabilities due to the high demand of computational resources. In this article, we present an architecture developed for real-time processing of these features using reconfigurable hardware. Although, to the best of our knowledge, previous implementations of Gabor or steerable filters on chip already exist, none of them provides energy, phase, and orientation information at the same time. We show how to implement quadrature filters based on Gaussian derivatives. We develop a hardware friendly technique to interpolate b...

The efficient simulation of spiking neural networks (SNN) remains an open challenge. Current SNN computing engines are still far away from simulating systems of millions of neurons efficiently. This contribution describes a computing scheme that takes full advantage of the massive parallel processing resources available at FPGA devices. The computing engine adopts an event-driven simulation scheme and an efficient next-event-to-go searching method to achieve high performance. We have designed a pipelined datapath, in order to compute several events in parallel avoiding idle computing resources. The system is able to compute approximately 2.5 million spikes per second. The whole computing machine is composed only of an FPGA device and five external memory SRAM chips. Therefore, the presented approach is of high interest for simulation experiments that require embedded simulation engines (for instance, in robotic experiments with autonomous agents).

A computing platform is described for simulating arbitrary networks of spiking neurons in real time. A hybrid computing scheme is adopted that uses both software and hardware components to manage the tradeoff between flexibility and computational power; the neuron model is implemented in hardware and the network model and the learning are implemented in software. The incremental transition of the software components into hardware is supported. We focus on a spike response model (SRM) for a neuron where the synapses are modeled as input-driven conductances. The temporal dynamics of the synaptic integration process are modeled with a synaptic time constant that results in a gradual injection of charge. This type of model is computationally expensive and is not easily amenable to existing software-based event-driven approaches. As an alternative we have designed an efficient time-based computing architecture in hardware, where the different stages of the neuron model are processed in parallel. Further improvements occur by computing multiple neurons in parallel using multiple processing units. This design is tested using reconfigurable hardware and its scalability and performance evaluated. Our overall goal is to investigate biologically realistic models for the real-time control of robots operating within closed action-perception loops, and so we evaluate the performance of the system on simulating a model of the cerebellum where the emulation of the temporal dynamics of the synaptic integration process is important.

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 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 and perturbations patterns, real-time control robustness and generalizability were validated. The implicit spiking dynamics of the cerebellar model fulfill timing, prediction and learning functions., European Union (Human Brain Project) REALNET FP7-ICT270434 CEREBNET FP7-ITN238686 HBP-604102

The cerebellum is one of the parts of the brain which has aroused more curiosity amongst neuroscientists. Indeed, several forms of plasticity mechanisms have been reported at several sites of the cerebellar model circuitry, thus including plasticity mechanisms not just at parallel fibers (a well-accepted plasticity site) but also at synaptic inputs of deep cerebellar nuclei (DCN) (from mossy fibers (MFs) [1,2]and Purkinje cells (PCs) [3,4]). The Marr and Albus model already hypothesized that parallel fiber (PFs) →Purkinje cell synapses presented both long-term potentiation (LTP) [5,6] and long-term depression (LTD) [5-7] plasticity so as to correlate the activity at parallel fibers with the incoming error signal through climbing fibers. Nevertheless, in subsequent studies, it has been demonstrated that many sites in the cerebellum show traces of plasticity [8-10]. But the way in which those distributed plasticity mechanisms may improve the operational capabilities of the cerebellum is still an open issue. In this work we propose that the synaptic plasticity of mossy-fiber-to-deep-cerebellar-nucleus-cell and Purkinje-cell-to-deep-cerebellar-nucleus-cell may regulate the effect of Purkinje-cell activity on the cerebellar output, behaving as a distributed homeostatic mechanism [11]. The plasticity at the DCN afferents helps to keep the Purkinje-cell activity in an adequate range independently of the magnitude required for the cerebellar output, thus improving the precision of this output signal. Since these plasticity mechanisms are capable of adapting the cerebellar behavior in the long-term, it is of necessity the presence of fast feedback for motor activities in order to perform precise movements. Thus, the presented work also explores the control implication that the inferior-olive-to-deep-cerebellar-nucleus-cell connection may possess in conjunction with the previously suggested plasticity mechanisms. As it is widely assumed, the climbing fiber activity that our cerebellar model implements is considered to be a teaching signal (targeting Purkinje cells). But we also explore its potential role as a control signal over the cerebellar output (targeting the deep cerebellar nuclei). To investigate all these proposals, we have embedded the cerebellar model in a feed-forward control loop which is connected to a simulated 3-degree-of-freedom robot model.

This paper presents a control model for object manipulation. Properties of objects and environmental conditions influence the motor control and learning. System dynamics depend on an unobserved external context, for example, work load of a robot manipulator. The dynamics of a robot arm change as it manipulates objects with different physical properties, for example, the mass, shape, or mass distribution. We address active sensing strategies to acquire object dynamical models with a radial basis function neural network (RBF). Experiments are done using a real robot’s arm, and trajectory data are gathered during various trials manipulating different objects. Biped robots do not have high force joint servos and the control system hardly compensates all the inertia variation of the adjacent joints and disturbance torque on dynamic gait control. In order to achieve smoother control and lead to more reliable sensorimotor complexes, we evaluate and compare a sparse velocity-driven versus a dense position-driven control scheme.

This paper presents how a plausible cerebellum-like architecture can abstract corrective models in the framework of a robot control task when manipulating objects that significantly affect the dynamics of the system. The presented scheme is adequate to control non-stiff-joint robots with low-power actuators which involve controlling systems with high inertial components. We evaluate the way in which the cerebellum stores a model in the granule layer, how its microstructure can efficiently abstract models and deliver accurate corrective torques for increasing precision during object manipulation. Particularly we study how input sensory-motor representations can enhance model abstraction capabilities during accurate movements, making use of explicit (model-related input labels) and implicit model representations (sensory signals). Finally we focus on how our cerebellum model (using a temporal correlation kernel) properly deals with transmission delays in sensory-motor pathways.

Around half of the neurons of a human brain are granule cells (approximately 10 11 granule neurons) [Kandel, E.R., Schwartz, J.H., Jessell, T.M., 2000. Principles of Neural Science. McGraw-Hill Professional Publishing, New York]. In order to study in detail the functional role of the intrinsic features of this cell we have developed a pre-compiled behavioural model based on the simplified granule-cell model of Bezzi et al. [Bezzi, M., Nieus, T., Arleo, A., D’Angelo, E., Coenen, O.J.-M.D., 2004. Information transfer at the mossy fiber—granule cell synapse of the cerebellum. 34th Annual Meeting. Society for Neuroscience, San Diego, CA, USA]. We can use an efficient event-driven simulation scheme based on lookup tables (EDLUT) [Ros, E., Carrillo, R.R., Ortigosa, E.M., Barbour, B., Ags, R., 2006. Event-driven simulation scheme for spiking neural networks using lookup tables to characterize neuronal dynamics. Neural Computation 18 (12), 2959–2993]. For this purpose it is necessary to compile into tables the data obtained through a massive numerical calculation of the simplified cell model. This allows network simulations requiring minimal numerical calculation. There are three major features that are considered functionally relevant in the simplified granule cell model: bursting, subthreshold oscillations and resonance. In this work we describe how the cell model is compiled into tables keeping these key properties of the neuron model. © 2008 Elsevier Ireland Ltd. All rights reserved.

We describe a neural network model of the cerebellum based on integrate-and-fire spiking neurons with conductance-based synapses. The neuron characteristics are derived from our earlier detailed models of the different cerebellar neurons. We tested the cerebellum model in a real-time control application with a robotic platform. Delays were introduced in the different sensorimotor pathways according to the biological system. The main plasticity in the cerebellar model is a spike-timing dependent plasticity (STDP) at the parallel fiber to Purkinje cell connections. This STDP is driven by the inferior olive (IO) activity, which encodes an error signal using a novel probabilistic low frequency model. We demonstrate the cerebellar model in a robot control system using a target-reaching task. We test whether the system learns to reach different target positions in a non-destructive way, therefore abstracting a general dynamics model. To test the system’s ability to self-adapt to different dynamical situations, we present results obtained after changing the dynamics of the robotic platform significantly (its friction and load). The experimental results show that the cerebellar-based system is able to adapt dynamically to different contexts. © 2008 Published by Elsevier Ireland Ltd.

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 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 and perturbations patterns, real-time control robustness and generalizability were validated. The implicit spiking dynamics of the cerebellar model fulfill timing, prediction and learning functions.

Nearly all neuronal information processing and interneuronal communication in the brain involves action potentials, or spikes, which drive the short-term synaptic dynamics of neurons, but also their long-term dynamics, via synaptic plasticity. In many brain structures, action potential activity is considered to be sparse. This sparseness of activity has been exploited to reduce the computational cost of large-scale network simulations, through the development of event-driven simulation schemes. However, existing event-driven simulations schemes use extremely simplified neuronal models. Here, we implement and evaluate critically an event-driven algorithm (ED-LUT) that uses precalculated look-up tables to characterize synaptic and neuronal dynamics. This approach enables the use of more complex (and realistic) neuronal models or data in representing the neurons, while retaining the advantage of high-speed simulation. We demonstrate the method's application for neurons containing exponential synaptic conductances, thereby implementing shunting inhibition, a phenomenon that is critical to cellular computation. We also introduce an improved two-stage event-queue algorithm, which allows the simulations to scale efficiently to highly connected networks with arbitrary propagation delays. Finally, the scheme readily accommodates implementation of synaptic plasticity mechanisms that depend on spike timing, enabling future simulations to explore issues of long-term learning and adaptation in large-scale networks., This work has been supported by the EU projects SpikeFORCE (IST-2001-35271), SENSOPAC (IST-028056) and the Spanish National Grant (DPI-2004-07032)