Information-theoretic analysis of neural coding

Johnson, Don H.; Gruner, Charlotte M.

doi:10.1109/icassp.1998.681442

Cited by 39 publications

(75 citation statements)

References 34 publications

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“…The Kullback-Leibler distance characterizes how well two responses can be distinguished by an optimal classifer [2]. Kullback's result allows teasing apart components of a measured distance into those required for a rate change and those that might be required to reflect an interval distribution change and a dependence change.…”

Section: Discussionmentioning

confidence: 99%

When does interval coding occur?

Johnson

Glantz

2004

Neurocomputing

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During the stationary portion of neuron's spiking response to a stimulus, the stimulus could be coded in the average rate and, more elaborately, in the statistics of the sequence of interspike intervals. We use information processing theory to explicitly define when interval coding occurs and quantify the coding gain beyond rate coding provided by the interval code. We explicitly find the interval distribution commensurate with average rate coding. When we analyzed optomotor neural responses recorded from the crayfish eye, we found little interval coding occuring despite stimulus-induced changes from a unimodal to a bimodal interval distribution.

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Section: Discussionmentioning

confidence: 99%

When does interval coding occur?

Johnson

Glantz

2004

Neurocomputing

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“…One was added systematically to all bins before the normalization of distributions that would be in the denominator of a ratio to avoid division by zero; when sample number n Ͼ Ͼ 1, this is analogous to assuming a uniform previous distribution and then finding the mean of the posterior probability distribution by using Bayes' rule (cf. Gelman, 1995;Johnson et al, 2001). According to Bayes' rule, the previous distribution is multiplied by a likelihood and divided by the probability of all data to give the posterior distribution.…”

Section: Methodsmentioning

confidence: 99%

“…For our purposes this is advantageous because it allows us to quantify the potential asymmetry in observing changes in different directions, i.e., comparing low-to-high variance changes with high-to-low variance changes. D KL quantifies the intrinsic classification difficulty (Johnson et al, 2001). Although D KL does not give the absolute probability of error for a specific decoder, it does limit the ultimate performance of any classifier; a one-bit increase in D KL corresponds to a twofold decrease in error probability.…”

Section: Methodsmentioning

confidence: 99%

“…In this case, D KL (n) is given by the cumulative D KL as follows: We estimated the distributions P(⌬ i ͉ A ) and P(⌬ i ͉ B ) from 10,000 unique random samples of interval sequences from the steady-state fly and model neuron data. To de-bias and estimate the variance of D cKL , we applied the Bootstrap method (Press, 1992;Johnson et al, 2001) by drawing 10,000 sequences of n ISIs randomly with replacement from the initial set of 10,000 sequences. After repeating this procedure 200 -300 times, we found the mean and the SD .…”

Section: Methodsmentioning

confidence: 99%

“…Although D KL does not give the absolute probability of error for a specific decoder, it does limit the ultimate performance of any classifier; a one-bit increase in D KL corresponds to a twofold decrease in error probability. Given a decoding model with some set of parameters, for small parameter changes the smallest achievable mean-squared estimation error is inversely proportional to D KL ; this is the Cramer-Rao bound (Johnson et al, 2001).…”

Section: Methodsmentioning

confidence: 99%

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Decoding Stimulus Variance from a Distributional Neural Code of Interspike Intervals

Lundstrom¹,

Fairhall²

2006

J. Neurosci.

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The spiking output of an individual neuron can represent information about the stimulus via mean rate, absolute spike time, and the time intervals between spikes. Here we discuss a distinct form of information representation, the local distribution of spike intervals, and show that the time-varying distribution of interspike intervals (ISIs) can represent parameters of the statistical context of stimuli. For many sensory neural systems the mapping between the stimulus input and spiking output is not fixed but, rather, depends on the statistical properties of the stimulus, potentially leading to ambiguity. We have shown previously that for the adaptive neural code of the fly H1, a motion-sensitive neuron in the fly visual system, information about the overall variance of the signal is obtainable from the ISI distribution. We now demonstrate the decoding of information about variance and show that a distributional code of ISIs can resolve ambiguities introduced by slow spike frequency adaptation. We examine the precision of this distributional code for the representation of stimulus variance in the H1 neuron as well as in the Hodgkin-Huxley model neuron. We find that the accuracy of the decoding depends on the shapes of the ISI distributions and the speed with which they adapt to new stimulus variances.

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Structure-based color learning on a mobile robot under changing illumination

Sridharan

Stone

2007

Auton Robot

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A central goal of robotics and AI is to be able to deploy an agent to act autonomously in the real world over an extended period of time. To operate in the real world, autonomous robots rely on sensory information. Despite the potential richness of visual information from on-board cameras, many mobile robots continue to rely on non-visual sensors such as tactile sensors, sonar, and laser. This preference for relatively low-fidelity sensors can be attributed to, among other things, the characteristic requirement of realtime operation under limited computational resources. Illumination changes pose another big challenge. For true extended autonomy, an agent must be able to recognize for itself when to abandon its current model in favor of learning a new one; and how to learn in its current situation. We describe a self-contained vision system that works on-board a vision-based autonomous robot under varying illumination conditions. First, we present a baseline system capable of color segmentation and object recognition within the computational and memory constraints of the robot. This relies on manually labeled data and operates under constant and reasonably uniform illumination conditions. We then relax these limitations by introducing algorithms for (i) Autonomous planned color learning, where the robot uses the knowledge of its environment (position, size and shape of objects) to automatically generate a suitable motion sequence and learn the desired colors, and (ii) Illumination change detection and adaptation, where the robot recognizes for itself when the illumination conditions have changed sufficiently to warrant revising its knowledge of colors. Our al-M. Sridharan ( ) · P. Stone gorithms are fully implemented and tested on the Sony ERS-7 Aibo robots.

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Information-theoretic analysis of neural coding

Cited by 39 publications

References 34 publications

When does interval coding occur?

When does interval coding occur?

Decoding Stimulus Variance from a Distributional Neural Code of Interspike Intervals

Structure-based color learning on a mobile robot under changing illumination

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