Relationship Between the Brain and the Central Pattern Generator Based on Recurrent Neural Network

Author(s):  
Qiang Lu
2014 ◽  
Vol 62 (10) ◽  
pp. 1497-1516 ◽  
Author(s):  
Duc Trong Tran ◽  
Ig Mo Koo ◽  
Yoon Haeng Lee ◽  
Hyungpil Moon ◽  
Sangdeok Park ◽  
...  

2021 ◽  
Vol 17 (9) ◽  
pp. e1009344
Author(s):  
Lars Keuninckx ◽  
Axel Cleeremans

We show how anomalous time reversal of stimuli and their associated responses can exist in very small connectionist models. These networks are built from dynamical toy model neurons which adhere to a minimal set of biologically plausible properties. The appearance of a “ghost” response, temporally and spatially located in between responses caused by actual stimuli, as in the phi phenomenon, is demonstrated in a similar small network, where it is caused by priming and long-distance feedforward paths. We then demonstrate that the color phi phenomenon can be present in an echo state network, a recurrent neural network, without explicitly training for the presence of the effect, such that it emerges as an artifact of the dynamical processing. Our results suggest that the color phi phenomenon might simply be a feature of the inherent dynamical and nonlinear sensory processing in the brain and in and of itself is not related to consciousness.


2019 ◽  
Author(s):  
Daniel Miner ◽  
Christian Tetzlaff

AbstractIn the course of everyday life, the brain must store and recall a huge variety of representations of stimuli which are presented in an ordered or sequential way. The processes by which the ordering of these various things is stored and recalled are moderately well understood. We use here a computational model of a cortex-like recurrent neural network adapted by a multitude of plasticity mechanisms. We first demonstrate the learning of a sequence. Then, we examine the influence of different types of distractors on the network dynamics during the recall of the encoded ordered information being ordered in a sequence. We are able to broadly arrive at two distinct effect-categories for distractors, arrive at a basic understanding of why this is so, and predict what distractors will fall into each category.


2017 ◽  
Vol 14 (4) ◽  
pp. 172988141772344 ◽  
Author(s):  
Gang Wang ◽  
Xi Chen ◽  
Shi-Kai Han

Although quite a few central pattern generator controllers have been developed to regulate the locomotion of terrestrial bionic robots, few studies have been conducted on the central pattern generator control technique for amphibious robots crawling on complex terrains. The present article proposes a central pattern generator and feedforward neural network-based self-adaptive gait control method for a crab-like robot locomoting on complex terrain under two reflex mechanisms. In detail, two nonlinear ordinary differential equations are presented at first to model a Hopf oscillator with limit cycle effects. Having Hopf oscillators as the basic units, a central pattern generator system is proposed for the waveform-gait control of the crab-like robot. A tri-layer feedforward neural network is then constructed to establish the one-to-one mapping between the central pattern generator rhythmic signals and the joint angles. Based on the central pattern generator system and feedforward neural network, two reflex mechanisms are put forward to realize self-adaptive gait control on complex terrains. Finally, experiments with the crab-like robot are performed to verify the waveform-gait generation and transition performances and the self-adaptive locomotion capability on uneven ground.


2016 ◽  
Vol 116 (4) ◽  
pp. 1728-1742 ◽  
Author(s):  
Akira Sakurai ◽  
Paul S. Katz

The nudibranch mollusc, Dendronotus iris, swims by rhythmically flexing its body from left to right. We identified a bilaterally represented interneuron, Si3, that provides strong excitatory drive to the previously identified Si2, forming a half-center oscillator, which functions as the central pattern generator (CPG) underlying swimming. As with Si2, Si3 inhibited its contralateral counterpart and exhibited rhythmic bursts in left-right alternation during the swim motor pattern. Si3 burst almost synchronously with the contralateral Si2 and was coactive with the efferent impulse activity in the contralateral body wall nerve. Perturbation of bursting in either Si3 or Si2 by current injection halted or phase-shifted the swim motor pattern, suggesting that they are both critical CPG members. Neither Si2 nor Si3 exhibited endogenous bursting properties when activated alone; activation of all four neurons was necessary to initiate and maintain the swim motor pattern. Si3 made a strong excitatory synapse onto the contralateral Si2 to which it is also electrically coupled. When Si3 was firing tonically but not exhibiting bursting, artificial enhancement of the Si3-to-Si2 synapse using dynamic clamp caused all four neurons to burst. In contrast, negation of the Si3-to-Si2 synapse by dynamic clamp blocked ongoing swim motor patterns. Together, these results suggest that the Dendronotus swim CPG is organized as a “twisted” half-center oscillator in which each “half” is composed of two excitatory-coupled neurons from both sides of the brain, each of which inhibits its contralateral counterpart. Consisting of only four neurons, this is perhaps the simplest known network oscillator for locomotion.


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