26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3

RTHybrid: a standardized and open-source real-time software model library for experimental neuroscience

“…All of them are adapted to work under hard real-time restrictions. Moreover, calibration algorithms are integrated in the library to automate the adaptation of model amplitude and time scales to the living neuron behavioural range (Reyes-Sanchez et al, 2017).…”

“…Communication with DAQ devices is achieved through Comedi open-source drivers for National Instruments' and several other manufacturers' hardware (or Analogy drivers in the case of Xenomai) (Schleef et al, 2012). Automatic calibration and experiment automation algorithms are included to deal with the differences between models and living neurons in terms of temporal scale and amplitude (Reyes-Sanchez et al, 2017). They also cover other possible experimental complications such as the presence of signal drift.…”

“…Table 4 shows the currently included neuron models, which have been selected due to their rich intrinsic dynamics and suitability to build hybrid circuits. All of them have different characteristics and require also specific adaptations to work in real-time, which are handled automatically by auto-calibration algorithms (Reyes-Sanchez et al, 2017). Several numerical integration methods can be chosen by the user, including Euler, Heun, Order 4 Runge-Kutta (Press et al, 1988) and (6)5 Runge-Kutta (Hull et al, 1972).…”

“…Neuron model parameters were set to produce bursting behaviour and the duration of each burst was set to automatically match the living neuron activity. Signal amplitude and temporal scaling was automatically performed by RTHybrid calibration algorithms (Reyes-Sanchez et al, 2017). Figure 8 shows the validation tests results for the Rulkov and Izhikevich models, which are the ones with simpler mathematical descriptions, and Fig.…”

Spiking Central Pattern Generators through Reverse Engineering of Locomotion Patterns

“…The SCPGs are built with plausible neuron models known as spiking neurons, models that define the third generation of artificial neural networks [18]; these neuron models naturally receive and send spatio-temporal information as generating rhythmic patterns are required for CPGs. The SCPGs have been designed and implemented as locomotion systems for robotic platforms such as bipeds [19][20][21]25], quadrupeds [23,25] and hexapods [22,[24][25][26][27], where the design methodologies used in [19][20][21]27] tend to follow the phases proposed in [7], while in [22][23][24][25][26] reverse engineering methods are used. Basically, a reverse engineering method to design SCPG-based locomotion systems for robotic platforms uses either deterministic or stochastic optimization methods, which, given an input set of discretized rhythmic signals and a fixed spiking neuron model, are capable of defining a spiking neural network (SNN), including both synaptic connections and weights, that endogenously and periodically replicates the input set of discretized rhythmic signals, which contribute to locomotion of a robotic platform.…”