Discrimination thresholds for channel-coded systems

Snippe, H.P.; Koenderink, Jan J.

doi:10.1007/bf00204120

Cited by 93 publications

(78 citation statements)

References 34 publications

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“…The Fano factor (Fano 1947) is defined as the variance to mean ratio of the spike count distribution (i.e. the number of spikes) during a time window T (Snippe and Koenderink 1992). Note that the spike-count distribution is also referred to as the pulse-number distribution.…”

Section: Positive Isi Correlationsmentioning

confidence: 99%

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Nonrenewal spike train statistics: causes and functional consequences on neural coding

Ávila-Ǻkerberg

Chacron

2011

Exp Brain Res

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Many neurons display significant patterning in their spike trains (e.g. oscillations, bursting), and there is accumulating evidence that information is contained in these patterns. In many cases, this patterning is caused by intrinsic mechanisms rather than external signals. In this review, we focus on spiking activity that displays nonrenewal statistics (i.e. memory that persists from one firing to the next). Such statistics are seen in both peripheral and central neurons and appear to be ubiquitous in the CNS. We review the principal mechanisms that can give rise to nonrenewal spike train statistics. These are separated into intrinsic mechanisms such as relative refractoriness and network mechanisms such as coupling with delayed inhibitory feedback. Next, we focus on the functional roles for nonrenewal spike train statistics. These can either increase or decrease information transmission. We also focus on how such statistics can give rise to an optimal integration timescale at which spike train variability is minimal and how this might be exploited by sensory systems to maximize the detection of weak signals. We finish by pointing out some interesting future directions for research in this area. In particular, we explore the interesting possibility that synaptic dynamics might be matched with the nonrenewal spiking statistics of presynaptic spike trains in order to further improve information transmission.

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Section: Positive Isi Correlationsmentioning

confidence: 99%

“…In most cases, the output is the number of action potentials (i.e. the pulse number) that occurs within a given time window (Snippe and Koenderink 1992). Since neurons display variability in their responses to repeated presentations of the same stimulus (Stein et al 2005), the pulse number obeys a distribution with nonzero variance.…”

Section: Neural Codingmentioning

confidence: 99%

Nonrenewal spike train statistics: causes and functional consequences on neural coding

Ávila-Ǻkerberg

Chacron

2011

Exp Brain Res

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“…It shares similarities to population codes [25,29] and similar to their probabilistic interpretation [32] they approximate a kernel density estimator [5]. The mathematical proof has basically already been given in context of averaged shifted histograms [26].…”

Section: The Channel Representationmentioning

confidence: 92%

Approximative Coding Methods for Channel Representations

Öfjäll

Felsberg

2017

J Math Imaging Vis

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Most methods that address computer vision problems require powerful visual features. Many successful approaches apply techniques motivated from nonparametric statistics. The channel representation provides a framework for nonparametric distribution representation. Although early work has focused on a signal processing view of the representation, the channel representation can be interpreted in probabilistic terms, e.g., representing the distribution of local image orientation. In this paper, a variety of approximative channel-based algorithms for probabilistic problems are presented: a novel efficient algorithm for density reconstruction, a novel and efficient scheme for nonlinear gridding of densities, and finally a novel method for estimating Copula densities. The experimental results provide evidence that by relaxing the requirements for exact solutions, efficient algorithms are obtained.

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“…The approach for density estimation that we will apply in what follows, is based on the channel representation [19,39]. The channel representation is related to observations from biological systems, where receptive fields are spatially located and are only sensitive to certain value ranges, e.g.…”

Section: Channel Representationsmentioning

confidence: 99%

Real-time view-based pose recognition and interpolation for tracking initialization

Felsberg

Hedborg

2007

J Real-Time Image Proc

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In this paper we propose a new approach to realtime view-based pose recognition and interpolation. Pose recognition is particularly useful for identifying camera views in databases, video sequences, video streams, and live recordings. All of these applications require a fast pose recognition process, in many cases video real-time. It should further be possible to extend the database with new material, i.e., to update the recognition system online.The method that we propose is based on P-channels, a special kind of information representation which combines advantages of histograms and local linear models. Our approach is motivated by its similarity to information representation in biological systems but its main advantage is its robustness against common distortions such as clutter and occlusion. The recognition algorithm consists of three steps:1. low-level image features for color and local orientation are extracted in each point of the image 2. these features are encoded into P-channels by combining similar features within local image regions 3. the query P-channels are compared to a set of prototype P-channels in a database using a least-squares approach.The algorithm is applied in two scene registration experiments with fish-eye camera data, one for pose interpolation from synthetic images and one for finding the nearest view in a set of real images. The method compares favorable to SIFT-based methods, in particular concerning interpolation. The method can be used for initializing pose-tracking systems, either when starting the tracking or when the tracking has failed and the system needs to re-initialize. Due to its This work has been supported by the CENIIT project CAIRIS (http://www.cvl.isy.liu.se/Research/Object/CAIRIS), EC Grants IST-2003-004176 COSPAL and IST-2002-002013 MATRIS. This paper does not represent the opinion of the European Community, and the European Community is not responsible for any use which may be made of its contents.M. Felsberg · J. Hedborg Computer Vision Laboratory Linköping University, Sweden Tel.: +46-13-282460 Fax: +46-13-138526 E-mail: mfe@isy.liu.se real-time performance, the method can also be embedded directly into the tracking system, allowing a sensor fusion unit choosing dynamically between the frame-by-frame tracking and the pose recognition.

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Discrimination thresholds for channel-coded systems

Cited by 93 publications

References 34 publications

Nonrenewal spike train statistics: causes and functional consequences on neural coding

Nonrenewal spike train statistics: causes and functional consequences on neural coding

Approximative Coding Methods for Channel Representations

Real-time view-based pose recognition and interpolation for tracking initialization

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