Neural Network Dynamics

Vogels, Tim P.; Rajan, Kanaka; Abbott, L. F.

doi:10.1146/annurev.neuro.28.061604.135637

Cited by 454 publications

(390 citation statements)

References 76 publications

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“…The most direct evidence is provided by the work of , Beggs & Plenz (2003 reported critical behavior in slices of cortical tissue, in the form of "avalanches" of neuronal discharges. This type of activity was subsequently also ascertained in intact cortical tissue of primates, and supports the contention that neuronal avalanches are an organizing principle of cell assemblies in cortical tissue (Plenz & Thiagarajan , 2006; for a discussion, see Vogels et al, 2005). The "avalanches" observed by these investigators meet the criteria for Self-organized Criticality which signifies their scale invariance: thus, the extent of neuron assemblies encompasses spatial dimension at any scale, including very large-range connections, potentially covering major expanses of cortical tissue.…”

Section: Dynamics In Dynamic Core and The Global Neuronal Workpacesupporting

confidence: 70%

Brain dynamics across levels of organization

Werner

2007

Journal of Physiology-Paris

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After presenting evidence that the electrical activity recorded from the brain surface can reflect metastable state transitions of neuronal configurations at the mesoscopic level, I will suggest that their patterns may correspond to the distinctive spatio-temporal activity in the Dynamic Core (DC) and the Global Neuronal Workspace (GNW), respectively, in the models of the Edelman group on the one hand, and of Dehaene-Changeux, on the other. In both cases, the recursively reentrant activity flow in intracortical and cortical-subcortical neuron loops plays an essential and distinct role. Reasons will be given for viewing the temporal characteristics of this activity flow as signature of Self-Organized Criticality (SOC), notably in reference to the dynamics of neuronal avalanches. This point of view enables the use of statistical Physics approaches for exploring phase transitions, scaling and universality properties of DC and GNW, with relevance to the macroscopic electrical activity in EEG and EMG.

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Section: Dynamics In Dynamic Core and The Global Neuronal Workpacesupporting

confidence: 70%

Brain dynamics across levels of organization

Werner

2007

Journal of Physiology-Paris

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“…Moving beyond the issue of brain localization, our commitment to dynamic systems theory also factors in to our emphasis on sustained activation peaks or "bump" attractors (Amari, 1977; see also, e.g., Vogels, Rajan & Abbott, 2005). One advantage to using dynamic field models is that this class of neural networks have been quasi-formally analyzed (Amari, 1977).…”

Section: The Dft Is Grounded By Neural Principlesmentioning

confidence: 99%

Generalizing the dynamic field theory of spatial cognition across real and developmental time scales

Simmering¹,

Schutte²,

Spencer³

2008

Brain Research

115

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Within cognitive neuroscience, computational models are designed to provide insights into the organization of behavior while adhering to neural principles. These models should provide sufficient specificity to generate novel predictions while maintaining the generality needed to capture behavior across tasks and/or time scales. This paper presents one such model, the Dynamic Field Theory (DFT) of spatial cognition, showing new simulations that provide a demonstration proof that the theory generalizes across developmental changes in performance in four tasks-the Piagetian A-not-B task, a sandbox version of the A-not-B task, a canonical spatial recall task, and a position discrimination task. Model simulations demonstrate that the DFT can accomplish both specificity-generating novel, testable predictions-and generality-spanning multiple tasks across development with a relatively simple developmental hypothesis. Critically, the DFT achieves generality across tasks and time scales with no modification to its basic structure and with a strong commitment to neural principles. The only change necessary to capture development in the model was an increase in the precision of the tuning of receptive fields as well as an increase in the precision of local excitatory interactions among neurons in the model. These small quantitative changes were sufficient to move the model through a set of quantitative and qualitative behavioral changes that span the age range from 8 months to 6 years and into adulthood. We conclude by considering how the DFT is positioned in the literature, the challenges on the horizon for our framework, and how a dynamic field approach can yield new insights into development from a computational cognitive neuroscience perspective.

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“…A number of studies have shown that networks of sparsely connected spiking model neurons can produce highly irregular, chaotic activity without any external source of noise Sompolinsky, 1996, 1998;Brunel, 2000;Mehring et al, 2003;Lerchner et al, 2004;Vogels et al, 2005). These networks allow for the study of signal propagation without the unrealistic injection of external noise.…”

Section: Introductionmentioning

confidence: 99%

Signal Propagation and Logic Gating in Networks of Integrate-and-Fire Neurons

Vogels¹,

Abbott²

2005

J. Neurosci.

440

458

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Transmission of signals within the brain is essential for cognitive function, but it is not clear how neural circuits support reliable and accurate signal propagation over a sufficiently large dynamic range. Two modes of propagation have been studied: synfire chains, in which synchronous activity travels through feedforward layers of a neuronal network, and the propagation of fluctuations in firing rate across these layers. In both cases, a sufficient amount of noise, which was added to previous models from an external source, had to be included to support stable propagation. Sparse, randomly connected networks of spiking model neurons can generate chaotic patterns of activity. We investigate whether this activity, which is a more realistic noise source, is sufficient to allow for signal transmission. We find that, for rate-coded signals but not for synfire chains, such networks support robust and accurate signal reproduction through up to six layers if appropriate adjustments are made in synaptic strengths. We investigate the factors affecting transmission and show that multiple signals can propagate simultaneously along different pathways. Using this feature, we show how different types of logic gates can arise within the architecture of the random network through the strengthening of specific synapses.

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Neural Network Dynamics

Cited by 454 publications

References 76 publications

Brain dynamics across levels of organization

Brain dynamics across levels of organization

Generalizing the dynamic field theory of spatial cognition across real and developmental time scales

Signal Propagation and Logic Gating in Networks of Integrate-and-Fire Neurons

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