Permitted and Forbidden Sets in Symmetric Threshold-Linear Networks

Hahnloser, Richard H. R.; Seung, H. Sebastian

doi:10.1162/089976603321192103

Cited by 169 publications

(152 citation statements)

References 18 publications

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“…If I − W is positive semi-definite and b is in its range space, a line attractor exists [22]. To prove global exponential stability of this line attractor, arrange the eigenvalues λi of I − W in increasing order, with λ2 > λ1 = 0.…”

Section: ✷ Example 23mentioning

confidence: 99%

On partial contraction analysis for coupled nonlinear oscillators

2004

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We describe a simple but general method to analyze networks of coupled identical nonlinear oscillators, and study applications to fast synchronization, locomotion, and schooling. Specifically, we use nonlinear contraction theory to derive exact and global (rather than linearized) results on synchronization, anti-synchronization and oscillator-death. The method can be applied to coupled networks of various structures and arbitrary size. For oscillators with positive-definite diffusion coupling, it can be shown that synchronization always occur globally for strong enough coupling strengths, and an explicit upper bound on the corresponding threshold can be computed through eigenvalue analysis. The discussion also extends to the case when network structure varies abruptly and asynchronously, as in "flocks" of oscillators or dynamic elements.

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Section: ✷ Example 23mentioning

confidence: 99%

On partial contraction analysis for coupled nonlinear oscillators

2004

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“…Baseline firing rates and interspike interval (ISI) distributions can influence neuronal computations, coding, and synaptic dynamics (Softky and Koch, 1993;Hahnloser et al, 2003;Castro-Alamancos, 2004). Firing rates are generally higher in awake than in anesthetized subjects (Chen et al, 2009), but wakefulness is not a unitary state and the effects of different awake states on the activity of cortical subpopulations are relatively unstudied.…”

Section: Introductionmentioning

confidence: 99%

Getting Drowsy? Alert/Nonalert Transitions and Visual Thalamocortical Network Dynamics

Bereshpolova¹,

Stoelzel²,

Zhuang³

et al. 2011

J. Neurosci.

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The effects of different EEG brain states on spontaneous firing of cortical populations are not well understood. Such state shifts may occur frequently under natural conditions, and baseline firing patterns can impact neural coding (e.g., signal-to-noise ratios, sparseness of coding). Here, we examine the effects of spontaneous transitions from alert to nonalert awake EEG states in the rabbit visual cortex (5 s before and after the state-shifts). In layer 4, we examined putative spiny neurons and fast-spike GABAergic interneurons; in layer 5, we examined corticotectal neurons. We also examined the behavior of retinotopically aligned dorsal lateral geniculate nucleus (LGNd) neurons, usually recorded simultaneously with the above cortical populations. Despite markedly reduced firing and sharply increased bursting in the LGNd neurons following the transition to the nonalert state, little change occurred in the spiny neurons of layer 4. However, fast-spike neurons of layer 4 showed a paradoxical increase in firing rates as thalamic drive decreased in the nonalert state, even though some of these cells received potent monosynaptic input from the same LGNd neurons whose rates were reduced. The firing rates of corticotectal neurons of layer 5, similarly to spiny cells of layer 4, were not state-dependent, but these cells did become more bursty in the nonalert state, as did the fast-spike cells. These results show that spontaneous firing rates of midlayer spiny populations are remarkably conserved following the shift from alert to nonalert states, despite marked reductions in excitatory thalamic drive and increased activity in local fast-spike inhibitory interneurons.

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“…In the present days, there are colossal measures of data recorded by the banks and examining them requires complex estimations. A few machine learning calculations [1], [3], [4], [5], [6], [7], [8], [9], [45], assumes an imperative part in settling on an astute choice to anticipate the bicycle sharing data. We played out the item metric examination on the given instructive gathering.…”

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confidence: 99%

Bike Sharing Prediction using Deep Neural Networks

Thirumalai

Koppuravuri

2017

JOIV : Int. J. Inform. Visualization

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Abstract-In this paper, we will use deep neural networks for predicting the bike sharing usage based on previous years usage data. We will use because deep neural nets for getting higher accuracy. Deep neural nets are quite different from other machine learning techniques; here we can add many numbers of hidden layers to improve the accuracy of our prediction and the model can be trained in the way we want such that we can achieve the results we want. Nowadays many AI experts will say that deep learning is the best AI technique available now and we can achieve some unbelievable results using this technique. Now we will use that technique to predict bike sharing usage of a rental company to make sure they can take good business decisions based on previous years data.

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Permitted and Forbidden Sets in Symmetric Threshold-Linear Networks

Cited by 169 publications

References 18 publications

On partial contraction analysis for coupled nonlinear oscillators

On partial contraction analysis for coupled nonlinear oscillators

Getting Drowsy? Alert/Nonalert Transitions and Visual Thalamocortical Network Dynamics

Bike Sharing Prediction using Deep Neural Networks

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