Synchronization in a Network of Spiking Neural Oscillators with Plastic Connectivity

Bazhanova, M. V.; Krylova, Nadia; Kazantsev, Victor B.; Храмов, А. Е.; Lobov, Sergey

doi:10.1007/s11141-021-10054-2

Cited by 6 publications

(3 citation statements)

References 54 publications

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“…The burst frequency became equal to the stimulation rate (Figure 4B, bottom panel). We used such a frequency lock to automatically detect the beginning of synchronization while determining the time required for synchronization [36,48]. At the same time, the vector of global network memory changed its direction to the stimulus location outwards (Figure 4B, thick red vector g st ), and we can speak about storing stimulus information on the global network scale.…”

Section: Stimulation Of Snn Is An Iteration Of Recording Into Network Memorymentioning

confidence: 99%

Spatial Memory in a Spiking Neural Network with Robot Embodiment

Lobov

Zharinov

Makarov

et al. 2021

Sensors

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Cognitive maps and spatial memory are fundamental paradigms of brain functioning. Here, we present a spiking neural network (SNN) capable of generating an internal representation of the external environment and implementing spatial memory. The SNN initially has a non-specific architecture, which is then shaped by Hebbian-type synaptic plasticity. The network receives stimuli at specific loci, while the memory retrieval operates as a functional SNN response in the form of population bursts. The SNN function is explored through its embodiment in a robot moving in an arena with safe and dangerous zones. We propose a measure of the global network memory using the synaptic vector field approach to validate results and calculate information characteristics, including learning curves. We show that after training, the SNN can effectively control the robot’s cognitive behavior, allowing it to avoid dangerous regions in the arena. However, the learning is not perfect. The robot eventually visits dangerous areas. Such behavior, also observed in animals, enables relearning in time-evolving environments. If a dangerous zone moves into another place, the SNN remaps positive and negative areas, allowing escaping the catastrophic interference phenomenon known for some AI architectures. Thus, the robot adapts to changing world.

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Section: Stimulation Of Snn Is An Iteration Of Recording Into Network Memorymentioning

confidence: 99%

Spatial Memory in a Spiking Neural Network with Robot Embodiment

Lobov

Zharinov

Makarov

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Among different dynamic networks, pulse-coupled spike oscillators attract special attention since they are similar to networks of neurons. [2][3][4][5][6][7][8][9][10] In our previous study, 11 we examined a simple network of chemical excitable micro-cells, in which the Belousov-Zhabotinsky (BZ) reaction 12,13 was used. We demonstrated that a special architecture of coupling allowed us to change the strength of the coupling between excitable cells according to Hebb's rules, 14,15 and, thus, to introduce ''memory'' and ''learning'' into such networks.…”

Section: Introductionmentioning

confidence: 99%

“…Among different dynamic networks, pulse-coupled spike oscillators attract special attention since they are similar to networks of neurons. 2–10…”

Section: Introductionmentioning

confidence: 99%