Integrator or coincidence detector? The role of the cortical neuron revisited

König, Peter; Engel, Andreas K.; Singer, Wolf

doi:10.1016/s0166-2236(96)80019-1

Cited by 611 publications

(441 citation statements)

References 37 publications

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“…Gamma-band synchronization has been linked to several cognitive functions in a range of studies (see Engel et al, 1992Herrmann et al, 2004;Singer and Gray, 1995;Tallon-Baudry, 2003;Tallon-Baudry and Bertrand, 1999 for reviews). Furthermore, it has been shown that the precise timing of pre-and post-synaptic activation determines long-term changes in synaptic strength (Bi and Poo, 1998;Markram et al, 1997) and that synchronization of synaptic inputs in the gamma band directly enhances their effective synaptic strength (König et al, 1996;Salinas and Sejnowski, 2001). Regardless of the exact roles of gamma oscillations in cognitive functioning (cf.…”

Section: Introductionmentioning

confidence: 99%

EEG gamma-band synchronization in visual coding from childhood to old age: Evidence from evoked power and inter-trial phase locking

Werkle‐Bergner

Shing

Müller

et al. 2009

Clinical Neurophysiology

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a b s t r a c tObjective: To investigate lifespan age differences in neuronal mechanisms of visual coding in the context of perceptual discrimination. Methods: We recorded EEG from 17 children (10-12 years), 16 younger adults (20-26 years), and 17 older adults (70-76 years) during a simple choice-reaction task requiring discrimination of squares and circles of different sizes. We examined age-group differences in the effect of stimulus size on early ERP components, evoked gamma-band power, and inter-trial phase-stability in the gamma band as assessed by the phase-locking index (PLI). Results: In the absence of age differences in discrimination accuracy, we observed reliable age differences in patterns of ERP, evoked gamma power, and PLI. P1 and N1 peak amplitudes were larger and the peak latencies longer in children than in adults. Children also showed lower levels of evoked power and PLI than adults. Older adults showed smaller increments in evoked power with increasing stimulus size than younger adults, but similar amounts of phase locking for small-and medium-sized stimuli as younger adults. Conclusions: The relative importance of different coding mechanisms in early visual areas changes from childhood to old age. Due to synaptic overproduction and immature myelination, the visual system of children is less entrained by incoming information, resulting in less synchronized neuronal responses. Adults primarily rely on sparse representations formed through experience-dependent temporally synchronized neuronal interactions. In old age, senescent decline in neuronal density and neurotransmitter availability further increase the reliance on temporally synchronized processing. Significance: Findings from this study defy the notion that sensory aging consists in a reversal of sensory development in childhood, and point to a high degree of age specificity in mechanisms of visual coding.

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Section: Introductionmentioning

confidence: 99%

EEG gamma-band synchronization in visual coding from childhood to old age: Evidence from evoked power and inter-trial phase locking

Werkle‐Bergner

Shing

Müller

et al. 2009

Clinical Neurophysiology

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“…In recent years, there has been much research devoted to the problem of how to generate efferent spikes with a CV between 0.5 and 1 (see for example Brown et al, 1999;Feng, 1997;Feng & Brown, 2000a,b;Feng & Tirozzi, 2000;Konig, Engel & Singer, 1996;Softky & Koch, 1993). In particular, it is pointed out in Softky and Koch (1993) that it is impossible for the IF model and some biophysical models to generate spikes with a CV between 0.5 and 1 if the inputs are exclusively excitatory and independent.…”

Section: Numerical Resultsmentioning

confidence: 99%

Is the integrate-and-fire model good enough?—a review

Feng

2001

Neural Networks

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We review some recent results on the behaviour of the integrate-and-®re (IF) model, the FitzHugh±Nagumo (FHN) model, a simpli®ed version of the FHN (IF-FHN) model and the Hodgkin±Huxley (HH) model with correlated inputs. The effect of inhibitory inputs on the model behaviour is also taken into account. Here, inputs exclusively take the form of diffusion approximation and correlated inputs mean correlated synaptic inputs (Sections 2 and 3). It is found that the IF and HH models respond to correlated inputs in totally opposite ways, but the IF-FHN model shows similar behaviour to the HH model. Increasing inhibitory input to single neuronal models, such as the FHN model and the HH model can sometimes increase their ®ring rates, which we termed inhibition-boosted ®ring (IBF). Using the IF model and the IF-FHN model, we theoretically explore how and when IBF can happen. The computational complexity of the IF-FHN model is very similar to the conventional IF model, but the former captures some interesting and essential features of biophysical models and could serve as a better model for spiking neuron computation. q

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“…• Temporal Coincidence Coding [21,30,19,25] Tighter coincidence of spikes recorded from different neurons represent higher stimulus intensity, with spike occurrences being modulated by local field oscillation [23]. A temporal coincidence code has the advantage that it leads to shorter "switching times," because it avoids the need to measure a frequency, which requires to observe multiple spikes.…”

Section: Neural Information Processingmentioning

confidence: 99%

Soft Pattern Mining in Neuroscience

Borgelt

2013

Synergies of Soft Computing and Statistics for Intelligent Data Analysis

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While the lower-level mechanisms of neural information processing (in biological neural networks) are fairly well understood, the principles of higher-level processing remain a topic of intense debate in the neuroscience community. With many theories competing to explain how stimuli are encoded in nerve signal (spike) patterns, data analysis tools are desired by which proper tests can be carried out on recorded parallel spike trains. This paper surveys how pattern mining methods, especially soft methods that tackle the core problems of temporal imprecision and selective participation, can help to test the temporal coincidence coding hypothesis. Future challenges consist in extending these methods, in particular to the case of spatio-temporal coding.

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Integrator or coincidence detector? The role of the cortical neuron revisited

Cited by 611 publications

References 37 publications

EEG gamma-band synchronization in visual coding from childhood to old age: Evidence from evoked power and inter-trial phase locking

EEG gamma-band synchronization in visual coding from childhood to old age: Evidence from evoked power and inter-trial phase locking

Is the integrate-and-fire model good enough?—a review

Soft Pattern Mining in Neuroscience

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