Local lateral inhibition: a key to spike synchronization?

Nischwitz, Alfred; Gl�nder, Helmut

doi:10.1007/s004220050194

Cited by 4 publications

(6 citation statements)

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“…In fact, transmission delays influence the performance of synchronization for both excitatory and inhibitory coupling [13,14,15]. In particular, it has been shown that for excitatory coupling, the presence of transmission delays can lead to desychronization [13,14,16,17]. To the best of our knowledge, this desynchronization was proved only for the case of two pulse-coupled oscillators, while for the case of N > 2 pulse-coupled oscillators, it was revealed only in simulations.…”

Section: Introductionmentioning

confidence: 89%

“…The assumption (A1) about the initial conditions is quite common for pulse-coupled networks with transmission delays [13,14,16,17]. The time t = 0 can be regarded as the moment at which fireflies assemble and begin to flash.…”

Section: Modelmentioning

confidence: 99%

“…For example, normally the transmission delays of most fireflies from sensors to motor actions of flashing are around 200ms (see [2]). In fact, transmission delays influence the performance of synchronization for both excitatory and inhibitory coupling [13,14,15]. In particular, it has been shown that for excitatory coupling, the presence of transmission delays can lead to desychronization [13,14,16,17].…”

Section: Introductionmentioning

confidence: 99%

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Desynchronization of pulse-coupled oscillators with delayed excitatory coupling

Chen²

2007

Nonlinearity

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Collective behavior of pulse-coupled oscillators has been investigated widely. As an example of pulse-coupled networks, fireflies display many kinds of flashing patterns. Mirollo and Strogatz (1990) proposed a pulse-coupled oscillator model to explain the synchronization of South East Asian fireflies (Pteroptyx malaccae). However, transmission delays were not considered in their model. In fact, the presence of transmission delays can lead to desychronization. In this paper, pulse-coupled oscillator networks with delayed excitatory coupling are studied. Our main result is that under reasonable assumptions, pulse-coupled oscillator networks with delayed excitatory coupling can not achieve complete synchronization, which can explain why another species of fireflies (Photinus pyralis) rarely synchronizes flashing. Finally, two numerical simulations are given. In the first simulation, we illustrate that even if all the initial phases are very close to each other, there could still be big variations in the times to process the pulses in the pipeline. It implies that asymptotical synchronization typically also cannot be achieved. In the second simulation, we exhibit a phenomenon of clustering synchronization.

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Section: Introductionmentioning

confidence: 89%

Section: Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Desynchronization of pulse-coupled oscillators with delayed excitatory coupling

Chen²

2007

Nonlinearity

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“…Research conducted on these mechanisms has so far largely been with rate-coded neural network models. However, theoretical analysis has shown how assemblies of spiking neurons may either synchronise or desynchronise depending upon the nature of their lateral interactions (Nischwitz and Glünder 1995 ). According to the ‘binding-by-synchrony’ hypothesis (Milner 1974 ; Engel et al 1991 ), such synchronised neural assemblies form a coherent stimulus percept, with psychophysics studies suggesting they are perceived as distinct from other assemblies with phase-shifted firing (Usher and Donnelly 1998 ).…”

Section: Introductionmentioning

confidence: 99%

STDP in lateral connections creates category-based perceptual cycles for invariance learning with multiple stimuli

Evans

Stringer

2014

Biol Cybern

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Learning to recognise objects and faces is an important and challenging problem tackled by the primate ventral visual system. One major difficulty lies in recognising an object despite profound differences in the retinal images it projects, due to changes in view, scale, position and other identity-preserving transformations. Several models of the ventral visual system have been successful in coping with these issues, but have typically been privileged by exposure to only one object at a time. In natural scenes, however, the challenges of object recognition are typically further compounded by the presence of several objects which should be perceived as distinct entities. In the present work, we explore one possible mechanism by which the visual system may overcome these two difficulties simultaneously, through segmenting unseen (artificial) stimuli using information about their category encoded in plastic lateral connections. We demonstrate that these experience-guided lateral interactions robustly organise input representations into perceptual cycles, allowing feed-forward connections trained with spike-timing-dependent plasticity to form independent, translation-invariant output representations. We present these simulations as a functional explanation for the role of plasticity in the lateral connectivity of visual cortex.

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“…In the context of networks of spiking neurons, the inclusion of synaptic delays favors the emergence of collective rhythms. In particular cases, 288,183,76,249 , the inclusion of interaction delays leads the dynamics from an initially asynchronous state to a fully synchronous one. Although synaptic delays could also mediate more complex transitions 251,77 , it is widely acknowledged that they are a fundamental component mediating the emergence of collective rhythms 31,29,30,211 as well as their locking properties 19,150 .…”

Section: Chaos and Synchrony In Delayed Neuronal Network: Discussion ...mentioning

confidence: 99%

Oscillations in routing and chaos

Palmigiano¹

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Date of oral examination:iii * This and the following sections will be largely focused, with very few exceptions, on the analysis of oscillatory activity reported from extra cellular recordings (local field potentials (LFP) and multi-unitactivity (MUA)) in mammals, generally rodents, cats or monkeys. * The distribution of functional roles linked to the theta band over time was presented in 47 , more than thirty roles were linked to the rhythm in the 1960s * The quote continues: " There is no direct proof that synchrony leads to changes in behavior when it is selectively altered. Such direct evidence has only been obtained in the olfactory system of insects. Stopfer et al 240 showed that odor discrimination deteriorates if synchronization patterns among olfactory bulb cells are disturbed. The search for similar evidence in the vertebrate brain is a daunting challenge, but future studies in this direction are a priority." . * Spike Field coherence as used in 87 , is defined as the power spectrum of the spike-triggered average normalized by the mean power spectrum over the LFP segments used to obtain the STA * Granger Causality measures the reduction of unexplained variance of a process x at time t when considering only the past values compared to when also the past values of a process y are included. Significant Granger Causality is used here and elsewhere as a synonym of causal influence.† FEF having faster responses than V4 was also reported in a feature-attention task, in which no frequency dependence was analyzed 290 * By delta connections it is meant that the input current to a neuron at time t as a consequence of an incoming spike at time t0 is modeled as I ∝ δ(t − t0).† The LIF neuron is defined as τm V = −V + I, where τm is the membrane time constant, V is the voltage and I is a constant input current. For the model to be completed, a threshold Vt and a reset Vr voltage values have as well to be defined. * The QIF neuron, defined as τM V = V 2 + I, will be studied in detail in Chapter 3.† The f-I curve is the dependence of the firing rate of the neuron to an increasing input current. Neurons with a Type I neuronal excitability 122 as the one studied here, can achieve arbitrarily low firing rates * They also studied the dynamics in the vicinity of the instability and provided analytical expressions for corrections to the oscillation frequency. * GABA A mediates fast inhibition.† AMPA is a glutamate receptor mediating fast excitation. * The number of connections K is much smaller than the number of neurons N † The number of connections K is no longer required to be much smaller than N * The first Lyapunov exponent is the rate of divergence of two trajectories as a consequence of an infinitesimal perturbation. Rigorous definitions will be given in the Methods of Chapter 3. * Generally, when referring to balanced state networks, the following is assumed: N neurons are connected with on average K other neurons, and the weight of the connections J scales like 1/ √ K * As opposed to the stable dynamics shown for t...

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Local lateral inhibition: a key to spike synchronization?

Cited by 4 publications

References 0 publications

Desynchronization of pulse-coupled oscillators with delayed excitatory coupling

Desynchronization of pulse-coupled oscillators with delayed excitatory coupling

STDP in lateral connections creates category-based perceptual cycles for invariance learning with multiple stimuli

Oscillations in routing and chaos

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