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Journal of Computational Neuroscience

Erschienen in:

01.02.2011

Automating the design of informative sequences of sensory stimuli

verfasst von: Jeremy Lewi, David M. Schneider, Sarah M. N. Woolley, Liam Paninski

Erschienen in: Journal of Computational Neuroscience | Ausgabe 1/2011

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Abstract

Adaptive stimulus design methods can potentially improve the efficiency of sensory neurophysiology experiments significantly; however, designing optimal stimulus sequences in real time remains a serious technical challenge. Here we describe two approximate methods for generating informative stimulus sequences: the first approach provides a fast method for scoring the informativeness of a batch of specific potential stimulus sequences, while the second method attempts to compute an optimal stimulus distribution from which the experimenter may easily sample. We apply these methods to single-neuron spike train data recorded from the auditory midbrain of zebra finches, and demonstrate that the resulting stimulus sequences do in fact provide more information about neuronal tuning in a shorter amount of time than do more standard experimental designs.

Vorheriger Artikel Characterizing the fine structure of a neural sensory code through information distortion

Nächster Artikel A metric space approach to the information channel capacity of spike trains

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The careful reader will have noticed that Eq. (15) depends on the future responses r _t + i, through the projected Fisher information D(r _t + i, ρ _t + i). However, recall that we are taking the expectation of Eq. (15), by plugging into Eq. (4), and the expectation of each term in Eq. (15) may be computed directly using the methods described above, without violating any causality constraints.

In Lewi et al. (2009), we discuss analytical methods for computing the optimal stimuli over sets of infinite cardinality, e.g., ellipsoidal sets of bounded squared norm in stimulus space. These methods relied strongly on the one-dimensional nature of the projected stimulus ρ _t and are unfortunately not applicable in the b > 1 case, where the projected stimulus is b-dimensional instead of just one-dimensional.

79 × 20 coefficients of the STRF + 8 spike history coefficients + 1 bias term = 1,589 unknown parameters.

If C _t is low-rank then \(C_t^{-1}\) is infinite in some directions, and the derivation will not hold because the contribution of \(C_t^{-1}\) will not become negligible as b→ ∞. In this case we can simply use a truncated design: i.e., we maximize the information in directions for which our prior uncertainty is not zero. To accomplish this we simply project \({\ensuremath{\theta}}\) into the lower-dimensional space corresponding to the space spanned by non-zero eigenvectors of C _t. Alternately, in the case that C _t has some very small but positive eigenvalues, it may be possible to approach the full objective function directly, though we have not pursued this direction systematically.

It is also worth noting that the log term will typically be much smaller than the other terms when the stimulus dimension d _s is large, since the first three terms in Eq. (25) scale linearly with d _s.

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Titel: Automating the design of informative sequences of sensory stimuli
verfasst von: Jeremy Lewi
David M. Schneider
Sarah M. N. Woolley
Liam Paninski
Publikationsdatum: 01.02.2011
Verlag: Springer US
Erschienen in: Journal of Computational Neuroscience / Ausgabe 1/2011
Print ISSN: 0929-5313
Elektronische ISSN: 1573-6873
DOI: https://doi.org/10.1007/s10827-010-0248-1

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