A FPGA-Based Neuromorphic Locomotion System for Multi-Legged Robots

Spiking Central Pattern Generators through Reverse Engineering of Locomotion Patterns

“…For the SCPG design problem, two types of representations have been proposed: one as a Context-Free Grammar (CFG) in Backus-Naur Form (BNF) and another as a Christiansen Grammar (CG) to use GE (in [25,26]) and a variant called Christiansen Grammar Evolution (CGE) [38] (in [23,24]), respectively. In general, both representations describe languages that define the presynaptic connectivity (including weights) of a specific spiking neuron; the common structure of expected words of two grammars is as follows:…”

“…The fitness function is usually defined by the problem; to solve this, three fitness functions based on SPIKE [39] (used in [23,24,26]) and Victor-Purpura distances [40] (used in [25]) to compare similarity between generated spike train and target spike train to guide the search process have been proposed. Basically, first and second distance-based fitness functions are for generating one SCPG per locomotion gait; the difference is that the second one looks for minimal presynaptic connectivity and the first one does not care about number of presynaptic connections.…”

“…Lately, SCPG-based locomotion systems for hexapods, quadrupeds and bipeds were designed by Guerra-Hernandez et al in [25]. In his work, there was proposed a locomotion gait for biped robots following the Rostro's idea, and SNNs were designed by using the discrete spiking neuron model and a variant of stochastic reverse engineering (Section 4.2) proposed by Espinal et al The reviewed works are summarized in Table 1, including robotic platforms and processing boards as well as reverse engineering method and spiking neuron model used.…”

“…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.…”

Digital Implementation of a Spiking Convolutional Neural Network for Tumor Detection

“…A high accuracy digital implementation makes it possible to develop networks with high dynamic range and stability. Recently, in order to realize neural system models, reconfigurable digital platforms have been utilized [15]- [22]. Critical challenges of the digital implementation include Through FPGA it is possible to achieve lower power consumption [3,4,5].…”