Adaptive Rescaling Maximizes Information Transmission

Brenner, Naama; Bialek, William; Steveninck, Rob de Ruyter van

doi:10.1016/s0896-6273(00)81205-2

Cited by 590 publications

(641 citation statements)

References 20 publications

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“…In accordance with our findings it was shown in a recent study (Brenner et al 2000) that, in a certain dynamic range of motion stimulation, the peak response amplitude of a fly motion sensitive TC (the H1-cell) changes only relatively little when the standard deviation of white-noise velocity fluctuations is increased. This result was interpreted as a consequence of adaptive processes in the fly's motion pathway, which are assumed to rescale the input/output relation (Brenner et al 2000).…”

Section: Specificity Of Neuronal Responses Under Naturalistic and Simsupporting

confidence: 93%

“…This condition is realized when the function relating the response amplitude to the input level is matched to the statistical distribution of stimulus levels. This kind of matched coding was concluded to operate in the peripheral visual system of the fly (for a review see Laughlin 1994) as well as at the level of TCs (Brenner et al 2000). In contrast to the conclusion obtained for a fly TC when stimulated with random velocity fluctuations, our results indicate that matched coding cannot be the reason for the peculiar performance of the HSE cell under naturalistic stimulus conditions.…”

Section: Functional Aspectscontrasting

confidence: 79%

“…This result was interpreted as a consequence of adaptive processes in the fly's motion pathway, which are assumed to rescale the input/output relation (Brenner et al 2000). In contrast to this interpretation, our model simulations indicate that the near invariance of the response amplitude described by Brenner et al (2000) can be explained in a more parsimonious way as an emergent property of the computations underlying motion detection.…”

Section: Specificity Of Neuronal Responses Under Naturalistic and Simcontrasting

confidence: 63%

See 2 more Smart Citations

Neuronal processing of behaviourally generated optic flow: experiments and model simulations

Kern¹,

Lutterklas²,

Petereit³

et al. 2001

Network: Computation in Neural Systems

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The stimuli traditionally used for analysing visual information processing are much simpler than what an animal sees when moving in its natural environment. Therefore, we analysed in a previous study the performance of an identified neuron in the optomotor system of the fly by using as visual stimuli image sequences that were experienced by the animal while walking in a structured environment. These electrophysiological experiments revealed that the fly visual system computes from behaviourally generated optic flow a rather unambiguous representation of the animal's self-motion. In contrast to conclusions based on simple stimuli, the directions of turns are represented by an interneuron, the HSE cell, quite independent of the spatial layout of the environment and its textural properties when the cell is stimulated with behaviourally generated optic flow. This conclusion is substantiated here by further experimental evidence. Moreover, it is shown that the largely unambiguous responses of the HSE cell to behaviourally generated optic flow can be replicated to a large extent by a network model of the fly's visual motion pathway. These results stress the significance of naturalistic stimuli for analysing what is encoded by neuronal circuits under natural operating conditions.

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Section: Specificity Of Neuronal Responses Under Naturalistic and Simsupporting

confidence: 93%

Section: Functional Aspectscontrasting

confidence: 79%

Section: Specificity Of Neuronal Responses Under Naturalistic and Simcontrasting

confidence: 63%

See 1 more Smart Citation

Neuronal processing of behaviourally generated optic flow: experiments and model simulations

Kern¹,

Lutterklas²,

Petereit³

et al. 2001

Network: Computation in Neural Systems

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show abstract

“…Bialek and colleagues [61,62] have demonstrated that adaptation of the fly's H1 neuron to the variance of a white noise stimulus appears to optimize information trasmission.…”

Section: Extensionsmentioning

confidence: 99%

Vision and the statistics of the visual environment

Simoncelli¹

2003

Current Opinion in Neurobiology

276

207

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SummaryIt is widely believed that visual systems are optimized for the visual properties of the environment inhabited by the host organism. A specific instance of this principle known as the Efficient Coding Hypothesis holds that the purpose of early visual processing is to produce an efficient representation of the incoming visual signal. The theory provides a quantitative link between the statistical properties of the world and the structure of the visual system. As such, specific instances of this theory have been tested experimentally, and have be used to motivate and constrain models for early visual processing.

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“…But if the neural response depends on more than a single axis within the stimulus space, the STA will provide an insu cient and possibly misleading description. A number of authors have suggested the natural extension of examining higher-order statistical properties (and in particular, the covariance) of the spike-triggered ensemble of stimuli [2,3,8,9,11]. The idea is simple and intuitive: if the neural response is deter-mined by a projection onto a low-dimensional subspace (within the space of all stimuli), an analysis of the spike-triggered covariance might allow us to recover this subspace.…”

Section: Recovery Of the Linear Subspace Using Spike-triggered Covarimentioning

confidence: 99%

Spike-triggered characterization of excitatory and suppressive stimulus dimensions in monkey V1

et al. 2004

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Neurons in primary visual cortex are commonly characterized using linear models, or simple extensions of linear models. Speciÿcally, V1 simple cell responses are often characterized using a rectiÿed linear receptive ÿeld, and complex cell responses are often described as the sum of squared responses of two linear subunits. We examined this class of model directly by applying spike-triggered covariance analysis to responses of monkey V1 neurons under binary white noise stimulation. The analysis extracts a low-dimensional subspace of the full stimulus space that is primarily responsible for generation of the neural response, including both excitatory and suppressive components. We found no fewer than two excitatory dimensions in simple cells, and as many as seven dimensions in complex cells. For all cells, we also found suppressive dimensions that were at least equal in number to the excitatory dimensions. These results suggest that extensions to standard models are required to fully describe the response properties of cells in V1.

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Adaptive Rescaling Maximizes Information Transmission

Cited by 590 publications

References 20 publications

Neuronal processing of behaviourally generated optic flow: experiments and model simulations

Neuronal processing of behaviourally generated optic flow: experiments and model simulations

Vision and the statistics of the visual environment

Spike-triggered characterization of excitatory and suppressive stimulus dimensions in monkey V1

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