Dynamic range of hypercubic stochastic excitable media

Assis, Vladimir R. V.; Copelli, Mauro

doi:10.1103/physreve.77.011923

Cited by 34 publications

(48 citation statements)

References 38 publications

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“…This signal compression property, or enhancement of dynamic range, is a general property of excitable media and has proven very robust against variations in the topology of the medium and the level of modeling, from cellular automata to compartmental conductance-based models [21][22][23][24][25][26][27][28][29][30][31][32][33]. Furthermore, the idea that dynamic range can be enhanced in neuronal excitable media has received support from experiments in very different setups [34,35], which again suggests that the phenomenon is robust.…”

Section: Introductionmentioning

confidence: 99%

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Statistical physics approach to dendritic computation: The excitable-wave mean-field approximation

2012

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We analytically study the input-output properties of a neuron whose active dendritic tree, modeled as a Cayley tree of excitable elements, is subjected to Poisson stimulus. Both single-site and two-site mean-field approximations incorrectly predict a nonequilibrium phase transition which is not allowed in the model. We propose an excitable-wave mean-field approximation which shows good agreement with previously published simulation results [Gollo et al., PLoS Comput. Biol. 5, e1000402 (2009)] and accounts for finite-size effects. We also discuss the relevance of our results to experiments in neuroscience, emphasizing the role of active dendrites in the enhancement of dynamic range and in gain control modulation.

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Section: Introductionmentioning

confidence: 99%

“…The dynamic range is one of the features of the response function which has received attention in the literature in recent years [21][22][23][24][25][26][27][28][29][30][31][32][33]35]. Here it serves the purpose of summarizing the quality of the EW mean-field approximation in comparison with model simulations.…”

Section: Excitable-wave Mean-field Approximationmentioning

confidence: 99%

Statistical physics approach to dendritic computation: The excitable-wave mean-field approximation

2012

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“…4). For the multi-dimensional case, mean-field approximations also exist to determine the maximum dynamic range a system can attain [38]. Our results demonstrate that the dynamic range of young neurons decreases as more dendritic compartments are removed.…”

Section: Discussionmentioning

confidence: 91%

Dynamical effects of dendritic pruning implicated in aging and neurodegeneration: Towards a measure of neuronal reserve

Kirch

Gollo

2020

Preprint

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Aging is a main risk factor for neurodegenerative disorders including Alzheimer's disease. It is often accompanied by reduced cognitive functions, gray-matter volume, and dendritic integrity. Although age-related brain structural changes have been observed across multiple scales, their functional implications remain largely unknown. Here we simulate the aging effects on neuronal morphology as dendritic pruning and characterize its dynamical implications. Utilizing a minimal computational modeling approach, we simulate the dynamics of detailed digitally reconstructed pyramidal neurons of humans obtained from the online repository Neuromorpho.org. We show that as aging progressively affects neuronal integrity, neuronal firing rate is reduced, which causes a reduction in energy consumption, energy efficiency, and dynamic range. Pruned neurons require less energy but their function is often impaired, which can explain the diminished ability to distinguish between similar experiences (pattern separation) in older people. Our measures indicate that the resilience of neuronal dynamics is neuron-specific, heterogeneous, and strongly affected by dendritic topology and the centrality of the soma. Based on the emergent neuronal dynamics, we propose to classify the effects of dendritic deterioration, and put forward that soma centrality measures neuronal reserve. Moreover, our findings suggest that increasing dendritic excitability could partially mitigate the dynamical effects of aging.

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“…They also tell us that these results are robust for larger values of M and qualitatively similar for ρ < 1. This edge of chaos is particularly interesting, since it has been shown that this kind of transition can be optimal for certain magnitudes such as computational capacity [11] and dynamic range of sensitivity to stimuli [12]. To illustrate how this is also the case here, we store a set of M patterns and then show the system a randomly chosen one every certain number of time steps.…”

Section: Model and Resultsmentioning

confidence: 99%

Nonequilibrium Behavior in Neural Networks: Criticality and Optimal Performance

Torres

Johnson

Mejías

et al. 2010

Advances in Cognitive Neurodynamics (II)

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Abstract. We present a general theory which allows one to study the effects on emergent, cooperative behavior of a complex interplay between different dynamic processes that occur in actual systems at the neuron, synapse and network levels. We consider synaptic changes at different time scales from less than the millisecond to the scale of learning, and the possibility of finding a fraction of silent neurons. For some limits of interest, the fixed-point solutions or memories then loose stability and the system shows enhancement of its response to changing external stimuli for particular network topologies and dynamical memories. We observe at the edge of chaos that the network activity becomes critical in the sense that the relevant quantities show non-trivial, power-law distributions. We also describe the effect of activity-dependent synaptic processes on the network storage capacity.

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Dynamic range of hypercubic stochastic excitable media

Cited by 34 publications

References 38 publications

Statistical physics approach to dendritic computation: The excitable-wave mean-field approximation

Statistical physics approach to dendritic computation: The excitable-wave mean-field approximation

Dynamical effects of dendritic pruning implicated in aging and neurodegeneration: Towards a measure of neuronal reserve

Nonequilibrium Behavior in Neural Networks: Criticality and Optimal Performance

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