Optimal information transmission in nonlinear arrays through suprathreshold stochastic resonance

McDonnell, Mark D.; Stocks, Nigel G.; Pearce, Charles E. M.; Abbott, Derek

doi:10.1016/j.physleta.2005.11.068

Cited by 76 publications

(62 citation statements)

References 22 publications

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“…Even though the 5-kHz stimulus is below the Nyquist frequency, the response of a cochlear nerve to the sampled signal would be expected to have harmonics at 1 kHz and above because of its nonlinear Ball-or-nothing^response. To some extent, this will be mitigated by the lowpass filtering action of a cochlear nerve fiber, the linearizing effect of neural noise (Stocks et al 1996), and the linearizing effect of having a distribution of nerve thresholds (McDonnell et al 2006). Nonetheless, in a computational study using a leaky-integrate-and-fire nerve model we found that the nerve fibers did phase-lock to the 1-kHz harmonic (results not shown).…”

Section: Methodsmentioning

confidence: 91%

The Effect of Gaussian Noise on the Threshold, Dynamic Range, and Loudness of Analogue Cochlear Implant Stimuli

Morse

Nunn

et al. 2006

JARO

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The deliberate addition of Gaussian noise to cochlear implant signals has previously been proposed to enhance the time coding of signals by the cochlear nerve. Potentially, the addition of an inaudible level of noise could also have secondary benefits: it could lower the threshold to the information-bearing signal, and by desynchronization of nerve discharges, it could increase the level at which the informationbearing signal becomes uncomfortable. Both these effects would lead to an increased dynamic range, which might be expected to enhance speech comprehension and make the choice of cochlear implant compression parameters less critical (as with a wider dynamic range, small changes in the parameters would have less effect on loudness). The hypothesized secondary effects were investigated with eight users of the Clarion cochlear implant; the stimulation was analogue and monopolar. For presentations in noise, noise at 95% of the threshold level was applied simultaneously and independently to all the electrodes. The noise was found in two-alternative forcedchoice (2AFC) experiments to decrease the threshold to sinusoidal stimuli (100 Hz, 1 kHz, 5 kHz) by about 2.0 dB and increase the dynamic range by 0.7 dB. Furthermore, in 2AFC loudness balance experiments, noise was found to decrease the loudness of moderate to intense stimuli. This suggests that loudness is partially coded by the degree of phase-locking of cochlear nerve fibers. The overall gain in dynamic range was modest, and more complex noise strategies, for example, using inhibition between the noise sources, may be required to get a clinically useful benefit.

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Section: Methodsmentioning

confidence: 91%

The Effect of Gaussian Noise on the Threshold, Dynamic Range, and Loudness of Analogue Cochlear Implant Stimuli

Morse

Nunn

et al. 2006

JARO

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“…The fact that the LIF model is a type I neuron model has traditionally made it a better model of cortical neurons (Koch, 1999), though studies indicate that some neurons in mammalian (rat) cortex exhibit both type I and type II properties (Tateno et al, 2004;Tsubo et al, 2007). Previous numerical experiments that have examined stochastic resonance and heterogeneity together have used binary threshold units in their simulations (Stocks, 2000;McDonnell et al, 2006), which are qualitatively very similar to type II neurons but without the temporal component. The results of these experiments may have limited applicability to cortical neural systems.…”

Section: Discussionmentioning

confidence: 99%

“…More recent work showed that heterogeneity allows neurons to use combinatorial coding schemes (Osborne et al, 2008) and in general increase the information encoded by a population (Shamir and Sompolinsky, 2006;Chelaru and Dragoi, 2008;Padmanabhan and Urban, 2010;Ecker et al, 2011). In the field of stochastic resonance, the effects of noise in a heterogeneous population have been examined, but only in specific cases such as for input signals much larger than the range of heterogeneity (Stocks, 2000) or for very large levels of noise (McDonnell et al, 2006). Furthermore, research into stochastic resonance with heterogeneity has focused on populations of binary threshold units, which fail to capture the dynamics of actual neurons.…”

Section: Introductionmentioning

confidence: 99%

The Competing Benefits of Noise and Heterogeneity in Neural Coding

2014

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Noise and heterogeneity are both known to benefit neural coding. Stochastic resonance describes how noise, in the form of random fluctuations in a neuron's membrane voltage, can improve neural representations of an input signal. Neuronal heterogeneity refers to variation in any one of a number of neuron parameters, and is also known to increase the information content of a population. We explore the interaction between noise and heterogeneity and find that their benefits to neural coding are not independent. Specifically, a neuronal population better represents an input signal when either noise or heterogeneity is added, but adding both does not always improve representation further. To explain this phenomenon, we propose that noise and heterogeneity operate using two shared mechanisms: 1) temporally desynchronizing the firing of neurons in the population, and 2) linearizing the response of a population to a stimulus. We first characterize the effects of noise and heterogeneity on the information content of populations of either leaky integrate-and-fire or FitzHugh-Nagumo neurons. We then examine how the mechanisms of desynchronization and linearization produce these effects, and find that they work to distribute information equally across all neurons in the population, both in terms of signal timing (desynchronization) and in terms of signal amplitude (linearization). Without noise or heterogeneity, all neurons encode the same aspects of the input signal; adding noise or heterogeneity allows neurons to encode complementary aspects of the input signal, thereby increasing information content. The simulations detailed in this paper highlight the importance of heterogeneity and noise in population coding, demonstrate their complex interactions in terms of the information content of neurons, and explain these effects in terms of underlying mechanisms.

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“…16 although they considered systems that are quite different to Poisson neurons. They also observed phase transition like sequences that resulted in M -ary codes.…”

Section: Resultsmentioning

confidence: 99%

A hierarchy of phase transitions in optimal neuronal coding: from binary to M -ary discrete optimal codes

2007

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We have investigated how optimal coding for neural systems changes with the time available for decoding. Optimization was in terms of maximizing information transmission. We have estimated the parameters for Poisson neurons that optimize Shannon transinformation with the assumption of rate coding. We observed a hierarchy of phase transitions from binary coding, for small decoding times, toward discrete (M -ary) coding with two, three and more quantization levels for larger decoding times. We postulate that the presence of subpopulations with specific neural characteristics could be a signiture of an optimal population coding scheme and we use the mammalian auditory system as an example.

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Optimal information transmission in nonlinear arrays through suprathreshold stochastic resonance

Cited by 76 publications

References 22 publications

The Effect of Gaussian Noise on the Threshold, Dynamic Range, and Loudness of Analogue Cochlear Implant Stimuli

The Effect of Gaussian Noise on the Threshold, Dynamic Range, and Loudness of Analogue Cochlear Implant Stimuli

The Competing Benefits of Noise and Heterogeneity in Neural Coding

A hierarchy of phase transitions in optimal neuronal coding: from binary to M -ary discrete optimal codes

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