Waves in Isotropic Totalistic Cellular Automata: Application to Real-Time Robot Navigation

Calvo, Carlos; Villacorta-Atienza, José Antonio; Mironov, Vasily I.; Gallego, Víctor; Makarov, Valeri A.

doi:10.1142/s0219525916500120

Cited by 9 publications

(14 citation statements)

References 30 publications

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“…In order to bring the discussion in line with the question of neuronal selectivity, consider the stimuli sets (6), (7) with Y = {x M +1 }, and suppose that stimuli s(·, x i ), i = 1, . .…”

Section: Resultsmentioning

confidence: 99%

High-Dimensional Brain: A Tool for Encoding and Rapid Learning of Memories by Single Neurons

et al. 2018

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Codifying memories is one of the fundamental problems of modern Neuroscience. The functional mechanisms behind this phenomenon remain largely unknown. Experimental evidence suggests that some of the memory functions are performed by stratified brain structures such as the hippocampus. In this particular case, single neurons in the CA1 region receive a highly multidimensional input from the CA3 area, which is a hub for information processing. We thus assess the implication of the abundance of neuronal signalling routes converging onto single cells on the information processing. We show that single neurons can selectively detect and learn arbitrary information items, given that they operate in high dimensions. The argument is based on stochastic separation theorems and the concentration of measure phenomena. We demonstrate that a simple enough functional neuronal model is capable of explaining: (i) the extreme selectivity of single neurons to the information content, (ii) simultaneous separation of several uncorrelated stimuli or informational items from a large set, and (iii) dynamic learning of new items by associating them with already "known" ones. These results constitute a basis for organization of complex memories in ensembles of single neurons. Moreover, they show that no a priori assumptions on the structural organization of neuronal ensembles are necessary for explaining basic concepts of static and dynamic memories.

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“…In order to bring the discussion in line with the question of neuronal selectivity, consider the stimuli sets (6), (7) with Y = {x M +1 }, and suppose that stimuli s(·, x i ), i = 1, . .…”

Section: Resultsmentioning

confidence: 99%

High-Dimensional Brain: A Tool for Encoding and Rapid Learning of Memories by Single Neurons

et al. 2018

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“…Thus, the coupling structure becomes centrifugal, which promotes traveling waves covering all the network and hence generating a population spike. We note that such an emergent function can be useful for driving robots [6]. It is basic for generation of compact cognitive maps [34,35].…”

Section: Discussionmentioning

confidence: 99%

Noise Enhanced Signaling in STDP Driven Spiking-Neuron Network

Lobov

Zhuravlev

Makarov

et al. 2017

Math. Model. Nat. Phenom.

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Population spike signaling is widely observed both in intact brain and neuronal cultures. Experimental evidence suggests that a locally applied electrical stimulus can shape the network architecture and thus the neuronal response. However, there is no clue on how this process can be controlled. Here we study a realistic model of a culture of cortical-like neurons with spike timing dependent plasticity. We show that a stimulus applied at a corner of the culture can rebuild synaptic couplings. Then the network eventually switches from a turbulent-like asynchronous spiking to an ordered population spike signaling mode. The structural analysis shows that the stimulus potentiates centrifugal couplings, which promotes spiking waves traveling outwards the stimulus location. This phenomenon can be catalyzed by noise of an intermediate strength. We predict that matured cultures with high connectivity are more susceptible to reconfiguration and generation of a population spike response than young cultures with low connectivity. We also report on an intermittent synchronization causing switches between two quasi-stable states: generation of time-locked population spikes and turbulent spiking. In the turbulent mode the stimulus excites patches of spiking activity randomly traveling in the network. Such a regime can be implemented through a large scale looping of couplings backwards to the stimulus location. We anticipate that the robust mechanisms of shaping the network architecture discussed here can also be effective in more complex preparations and studies of the relationship between network structure and function.

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“…To generate a GCM, we simultaneously (i) predict the objects' movements and (ii) simulate all possible subject's actions matched with the objects' movements. Both calculations must be done by the subject faster than the time scale of the dynamic situation (for more detail see Calvo et al, 2016). To account for this internal processing, besides the "real" time t in the workspace W, we introduce the "mental" time τ used for calculations in the hand-space H. For convenience, we also introduce the discrete time n ∈ N 0 related to the continuous time by τ = δn, where δ is the time step.…”

Section: Neural Network Generating Generalized Cognitive Maps In Handmentioning

confidence: 99%

Semantic Knowledge Representation for Strategic Interactions in Dynamic Situations

et al. 2020

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Evolved living beings can anticipate the consequences of their actions in complex multilevel dynamic situations. This ability relies on abstracting the meaning of an action. The underlying brain mechanisms of such semantic processing of information are poorly understood. Here we show how our novel concept, known as time compaction, provides a natural way of representing semantic knowledge of actions in time-changing situations. As a testbed, we model a fencing scenario with a subject deciding between attack and defense strategies. The semantic content of each action in terms of lethality, versatility, and imminence is then structured as a spatial (static) map representing a particular fencing (dynamic) situation. The model allows deploying a variety of cognitive strategies in a fast and reliable way. We validate the approach in virtual reality and by using a real humanoid robot.

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Waves in Isotropic Totalistic Cellular Automata: Application to Real-Time Robot Navigation

Cited by 9 publications

References 30 publications

High-Dimensional Brain: A Tool for Encoding and Rapid Learning of Memories by Single Neurons

High-Dimensional Brain: A Tool for Encoding and Rapid Learning of Memories by Single Neurons

Noise Enhanced Signaling in STDP Driven Spiking-Neuron Network

Semantic Knowledge Representation for Strategic Interactions in Dynamic Situations

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