Spatial Memory in a Spiking Neural Network with Robot Embodiment

Lobov, Sergey; Zharinov, A.I.; Makarov, Valeri A.; Kazantsev, Victor B.

doi:10.3390/s21082678

Cited by 18 publications

(15 citation statements)

References 58 publications

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“…Figure 1A shows an example of the network reorganization provoked by a stimulus. Recently, it has been shown that there is an interplay between the anatomic architecture and functionality, and functional changes can drive the rebuilding of the network and vice versa ( Lobov et al, 2021b ).…”

Section: Spiking Neural Network As An Alternative For Building Reflec...mentioning

confidence: 99%

“…Spatial computing in small neural circuits and modular SNNs can simulate Pavlovian conditioning and operant learning in neurorobots ( Lobov et al, 2020b , 2021a ). Another possible way to implement spatial computations is cognitive maps and spatial memory with positive ( Ponulak and Hopfield, 2013 ) or negative ( Lobov et al, 2021b ) environmental stimuli ( Figure 1C ). Note that due to the presence of spontaneous activity in SNNs (unlike ANNs), they can “live” without external input, determining the “behavior” of neurorobots ( Lobov et al, 2020b , 2021a , 2021b ).…”

Section: Spiking Neural Network As An Alternative For Building Reflec...mentioning

confidence: 99%

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Toward Reflective Spiking Neural Networks Exploiting Memristive Devices

Makarov

Lobov

Shchanikov

et al. 2022

Front. Comput. Neurosci.

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The design of modern convolutional artificial neural networks (ANNs) composed of formal neurons copies the architecture of the visual cortex. Signals proceed through a hierarchy, where receptive fields become increasingly more complex and coding sparse. Nowadays, ANNs outperform humans in controlled pattern recognition tasks yet remain far behind in cognition. In part, it happens due to limited knowledge about the higher echelons of the brain hierarchy, where neurons actively generate predictions about what will happen next, i.e., the information processing jumps from reflex to reflection. In this study, we forecast that spiking neural networks (SNNs) can achieve the next qualitative leap. Reflective SNNs may take advantage of their intrinsic dynamics and mimic complex, not reflex-based, brain actions. They also enable a significant reduction in energy consumption. However, the training of SNNs is a challenging problem, strongly limiting their deployment. We then briefly overview new insights provided by the concept of a high-dimensional brain, which has been put forward to explain the potential power of single neurons in higher brain stations and deep SNN layers. Finally, we discuss the prospect of implementing neural networks in memristive systems. Such systems can densely pack on a chip 2D or 3D arrays of plastic synaptic contacts directly processing analog information. Thus, memristive devices are a good candidate for implementing in-memory and in-sensor computing. Then, memristive SNNs can diverge from the development of ANNs and build their niche, cognitive, or reflective computations.

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Section: Spiking Neural Network As An Alternative For Building Reflec...mentioning

confidence: 99%

Section: Spiking Neural Network As An Alternative For Building Reflec...mentioning

confidence: 99%

Toward Reflective Spiking Neural Networks Exploiting Memristive Devices

Makarov

Lobov

Shchanikov

et al. 2022

Front. Comput. Neurosci.

Self Cite

View full text Add to dashboard Cite

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“…One application is a model of spatial memory implemented in an SNN that was used on a robot moving in an environment with neutral and harmful regions. In that application, STDP rearranges the couplings in the SNN and forms spatial memory similar to cognitive maps associated with the negative experience that resulted in a learning robot to avoid harmful zones [ 14 ]. Another area of cognitive development is the application of SNN for associative learning of perceptual information.…”

Section: Introductionmentioning

confidence: 99%

Overview of Spiking Neural Network Learning Approaches and Their Computational Complexities

Pietrzak

Szczęsny

Huderek

et al. 2023

Sensors

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Spiking neural networks (SNNs) are subjects of a topic that is gaining more and more interest nowadays. They more closely resemble actual neural networks in the brain than their second-generation counterparts, artificial neural networks (ANNs). SNNs have the potential to be more energy efficient than ANNs on event-driven neuromorphic hardware. This can yield drastic maintenance cost reduction for neural network models, as the energy consumption would be much lower in comparison to regular deep learning models hosted in the cloud today. However, such hardware is still not yet widely available. On standard computer architectures consisting mainly of central processing units (CPUs) and graphics processing units (GPUs) ANNs, due to simpler models of neurons and simpler models of connections between neurons, have the upper hand in terms of execution speed. In general, they also win in terms of learning algorithms, as SNNs do not reach the same levels of performance as their second-generation counterparts in typical machine learning benchmark tasks, such as classification. In this paper, we review existing learning algorithms for spiking neural networks, divide them into categories by type, and assess their computational complexity.

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“…In theory, there should be a gap between molecular and cellular levels of implementation and its functionality at the cognitive level. Scholars proposed a variety of conceptual, mathematical, and computational models of neuronal networks pretending to implement cognitive functions, such as learning and memory [3][4][5][6][7][8][9]. Systems neuroscience views memory as a substantially complicated paradigm involving different types and forms.…”

Section: Introductionmentioning

confidence: 99%

Astrocytes mediate analogous memory in a multi-layer neuron–astrocyte network

Tsybina

Kastalskiy

Krivonosov

et al. 2022

Neural Comput & Applic

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Modeling the neuronal processes underlying short-term working memory remains the focus of many theoretical studies in neuroscience. In this paper, we propose a mathematical model of a spiking neural network (SNN) which simulates the way a fragment of information is maintained as a robust activity pattern for several seconds and the way it completely disappears if no other stimuli are fed to the system. Such short-term memory traces are preserved due to the activation of astrocytes accompanying the SNN. The astrocytes exhibit calcium transients at a time scale of seconds. These transients further modulate the efficiency of synaptic transmission and, hence, the firing rate of neighboring neurons at diverse timescales through gliotransmitter release. We demonstrate how such transients continuously encode frequencies of neuronal discharges and provide robust short-term storage of analogous information. This kind of short-term memory can store relevant information for seconds and then completely forget it to avoid overlapping with forthcoming patterns. The SNN is inter-connected with the astrocytic layer by local inter-cellular diffusive connections. The astrocytes are activated only when the neighboring neurons fire synchronously, e.g., when an information pattern is loaded. For illustration, we took grayscale photographs of people’s faces where the shades of gray correspond to the level of applied current which stimulates the neurons. The astrocyte feedback modulates (facilitates) synaptic transmission by varying the frequency of neuronal firing. We show how arbitrary patterns can be loaded, then stored for a certain interval of time, and retrieved if the appropriate clue pattern is applied to the input.

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Spatial Memory in a Spiking Neural Network with Robot Embodiment

Cited by 18 publications

References 58 publications

Toward Reflective Spiking Neural Networks Exploiting Memristive Devices

Toward Reflective Spiking Neural Networks Exploiting Memristive Devices

Overview of Spiking Neural Network Learning Approaches and Their Computational Complexities

Astrocytes mediate analogous memory in a multi-layer neuron–astrocyte network

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