Local lateral inhibition: a key to spike synchronization?

Nischwitz, Alfred; Glünder, Helmut

doi:10.1007/bf00201473

Cited by 44 publications

(34 citation statements)

References 37 publications

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“…Thus, temporal information may be very important for neural function. Integrate-and-fire neurons have been studied extensively in this role because their simplicity makes it possible to study synchronization behavior in large-scale networks [2,8,11,13].…”

Section: Introductionmentioning

confidence: 99%

“…Some of this work assumes that spikes are transmitted instantaneously [2], but others take into account axonal conduction delays [11,13]. It turns out that the delay characteristic of different types of connections (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…excitatory and inhibitory) determine the synchronization behavior in a highly interconnected network of neurons. The main results are: (1) excitatory connections with no delay cause synchrony [2,9,11], (2) excitatory connections with delay cause desynchrony [13], (3) inhibitory connections without delay cause desynchrony [11,13], and (4) inhibitory connections with delay cause synchrony [11,12,13,17]. Previous models of spiking neurons have either adapted or selected the axonal delays to regulate synchronization behavior [7,16].…”

Section: Introductionmentioning

confidence: 99%

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The role of postsynaptic potential decay rate in neural synchrony

Choe

Miikkulainen

2003

Neurocomputing

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The role of postsynaptic potential decay rate in neural synchrony

Choe

Miikkulainen

2003

Neurocomputing

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“…Theoretical studies in that direction mainly focused on the synchronization of coupled oscillatory subsystems 1,3,4,7] they usually neglected more complex dynamical modes, that are known to exist already on the single-unit level 2,7]. Recently we described synchronization phenomena -considerably more complex than synchronized oscillations -in two small mutually coupled recurrent networks comprising graded response neurons 5,6].…”

Section: Introductionmentioning

confidence: 99%

Generalized types of synchronization in networks of spiking neurons

Wennekers

Pasemann

2001

Neurocomputing

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The synchronization of neural signals has been proposed as a temporal coding scheme in distributed cortical networks. Theoretical studies in that direction mainly focused on the synchronization of coupled oscillatory subsystems. In the present w ork we s h o w that several complex types of synchronization previously described for graded response neurons appear similarly also in biologically realistic networks of spiking and compartmental neurons. This includes synchronized complex spatio-temporal behavior, partial and generalized synchronization. The results suggest a similarly rich spatio-temporal behavior in real neural systems and may guide experimental research towards the study of complex modes of synchronization and their neuromodulation.

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“…The effect is that the simulation time becomes dependent on the level of spiking, and on the degree of interconnection. This technique has been used by [2,[87][88][89] for simulating large numbers of neurons. In addition, Grassman and Cyprian [89] have developed special purpose hardware to support this.…”

Section: Point Neuronsmentioning

confidence: 99%

Implementing Neural Models in Silicon

Smith

Handbook of Nature-Inspired and Innovative Computing

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Neural models are used in both computational neuroscience and in pattern recognition. The aim of the first is understanding of real neural systems, and of the second is gaining better, possibly brain-like performance for systems being built. In both cases, the highly parallel nature of the neural system contrasts with the sequential nature of computer systems, resulting in slow and complex simulation software. More direct implementation in hardware (whether digital or analogue) holds out the promise of faster emulation both because hardware implementation is inherently faster than software, and because the operation is much more parallel. There are costs to this: modifying the system (for example to test out variants of the system) is much harder when a full application specific integrated circuit has been built. Fast emulation can permit direct incorporation of a neural model into a system, permitting realtime input and output. Appropriate selection of implementation technology can help to make interfacing the system to external devices simpler. We review the technologies involved, and discuss some example systems. Why implement neural models in silicon?There are two primary reasons for implementing neural models: one is to attempt to gain better, and possibly brain-like performance for some system, and the other is to study how some particular neural model performs. Current computer systems do not approach brain-like system performance in many areas (sensing, motor control, and pattern recognition, for example, to say nothing of the higher level capabilities of mammalian brains). There has been considerable research into how the neural system attains its capabilities. Implementing neural systems in silicon can permit direct applications of this research by permitting neural models to run rapidly enough to be applied directly to data. It is true that increases in workstation performance have allowed some software implementations of neural models to run in real time, but the highly parallel nature of neural systems, coupled with increasing interest in the application of more sophisticated (and computationally more expensive) neural models has caused interest in more direct implementation to be maintained. Interest in applying neural models to sensory and sensory-motor systems has made attaining real-time performance a critical factor. Real sensory systems are highly parallel, with multiple parallel channels of information, so that even though each channel might be implementable in real-time in software, implementing multiple channels implies hardware implementation.The study of how particular models of neural systems perform is one aspect of computational neuroscience. Such studies are usually carried out in software, as this allows easy alteration of and experimentation with systems. However, models of the highly parallel architecture of neural systems run slowly on standard 1

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Local lateral inhibition: a key to spike synchronization?

Cited by 44 publications

References 37 publications

The role of postsynaptic potential decay rate in neural synchrony

The role of postsynaptic potential decay rate in neural synchrony

Generalized types of synchronization in networks of spiking neurons

Implementing Neural Models in Silicon

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