Stuart Yarrow PhD

Stuart Yarrow


Research Interests

My main interest is neural coding and measures for assessing the accuracy of neural codes.  Under the supervision of Peggy Series, I am currently investigating the relationship between Fisher information and true information theoretic measures in the context of population coding.  I am also particularly interested in audition and I'm currently working in collaboration with Khaleel Razak at UC Riverside on cortical representation of auditory spatial cues in bats.

Publications:
2015
  The influence of population size, noise strength and behavioral task on best-encoded stimulus for neurons with unimodal or monotonic tuning curves
Yarrow, S & Seriés, P 2015, 'The influence of population size, noise strength and behavioral task on best-encoded stimulus for neurons with unimodal or monotonic tuning curves' Frontiers in Computational Neuroscience, vol 9. DOI: 10.3389/fncom.2015.00018
Tuning curves and receptive fields are widely used to describe the selectivity of sensory neurons, but the relationship between firing rates and information is not always intuitive. Neither high firing rates nor high tuning curve gradients necessarily mean that stimuli at that part of the tuning curve are well represented by a neuron. Recent research has shown that trial-to-trial variability (noise) and population size can strongly affect which stimuli are most precisely represented by a neuron in the context of a population code (the best-encoded stimulus), and that different measures of information can give conflicting indications. Specifically, the Fisher information is greatest where the tuning curve gradient is greatest, such as on the flanks of peaked tuning curves, but the stimulus-specific information (SSI) is greatest at the tuning curve peak for small populations with high trial-to-trial variability. Previous research in this area has focussed upon unimodal (peaked) tuning curves, and in this article we extend these analyses to monotonic tuning curves. In addition, we examine how stimulus spacing in forced choice tasks affects the best-encoded stimulus. Our results show that, regardless of the tuning curve, Fisher information correctly predicts the best-encoded stimulus for large populations and where the stimuli are closely spaced in forced choice tasks. In smaller populations with high variability, or in forced choice tasks with widely-spaced choices, the best-encoded stimulus falls at the peak of unimodal tuning curves, but is more variable for monotonic tuning curves. Task, population size and variability all need to be considered when assessing which stimuli a neuron represents, but the best-encoded stimulus can be estimated on a case-by case basis using commonly available computing facilities.
General Information
Organisations: Edinburgh Neuroscience.
Authors: Yarrow, Stuart & Seriés, Peggy.
Keywords: (population code, SSI, fisher information, Chernoff, tuning curve, monotonic, unimodal. )
Publication Date: 17 Feb 2015
Publication Information
Category: Article
Journal: Frontiers in Computational Neuroscience
Volume: 9
ISSN: 1662-5188
Original Language: English
DOIs: 10.3389/fncom.2015.00018
2014
  Detecting and Quantifying Topography in Neural Maps
Yarrow, S, Seitz, AR, Seriès, P & Razak, K 2014, 'Detecting and Quantifying Topography in Neural Maps' PLoS Neglected Tropical Diseases, vol 9, no. 2, e87178. DOI: 10.1371%2Fjournal.pone.0087178

Topographic maps are an often-encountered feature in the brains of many species, yet there are no standard, objective procedures for quantifying topography. Topographic maps are typically identified and described subjectively, but in cases where the scale of the map is close to the resolution limit of the measurement technique, identifying the presence of a topographic map can be a challenging subjective task. In such cases, an objective topography detection test would be advantageous. To address these issues, we assessed seven measures (Pearson distance correlation, Spearman distance correlation, Zrehen's measure, topographic product, topological correlation, path length and wiring length) by quantifying topography in three classes of cortical map model: linear, orientation-like, and clusters. We found that all but one of these measures were effective at detecting statistically significant topography even in weakly-ordered maps, based on simulated noisy measurements of neuronal selectivity and sparse sampling of the maps. We demonstrate the practical applicability of these measures by using them to examine the arrangement of spatial cue selectivity in pallid bat A1. This analysis shows that significantly topographic arrangements of interaural intensity difference and azimuth selectivity exist at the scale of individual binaural clusters.


General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Yarrow, Stuart, Seitz, Aaron R., Seriès, Peggy & Razak, Khaleel.
Number of pages: 14
Publication Date: 5 Feb 2014
Publication Information
Category: Article
Journal: PLoS Neglected Tropical Diseases
Volume: 9
Issue number: 2
ISSN: 1935-2727
Original Language: English
DOIs: 10.1371%2Fjournal.pone.0087178
2013
  Detecting and Quantifying Topographic Order in the Brain
Yarrow, S, Razak, K, Seitz, AR & Series, P 2013, 'Detecting and Quantifying Topographic Order in the Brain' Computational and Systems Neuroscience (Cosyne) 2013, Salt Lake City, Utah, United States, 28/02/13 - 5/03/13, .
Topographic maps are an often-encountered feature in the brains of many species. The degree and spatial scale of smooth topographic organisation in neural maps vary greatly, as do the sampling density and coverage of techniques used to measure maps. An objective method for quantifying topographic order would be valuable for evaluating differences between, e.g. experimental and control conditions, developmental stages, hemispheres, individuals or species; to date, no such method has been applied to experimentally-characterised maps. Neural maps are typically identified and described subjectively, but in cases where the scale of the map is close to the resolution limit of the measurement technique, just identifying the presence of a map can be a challenging subjective task. In such cases, an objective map detection test would be advantageous.

To address these issues, we assessed seven measures (Pearson distance correlation, Spearman distance correlation, Zrehen measure, topographic product, topological correlation, wiring length and path length) by quantifying topographic order in three classes of cortical map model: linear gradient, orientation-like, and randomly scattered homogeneous clusters. We found that the first five of these measures were sensitive to weakly-ordered maps and effective at detecting statistically significant topographic order, based on noisy simulated measurements of neuronal selectivity and sparse spatial sampling of the maps.

We demonstrated the practical applicability of these measures by using them to examine the arrangement of spatial cue selectivity in pallid bat primary auditory cortex5,6. This analysis shows for the first time that statistically significant systematic representations of inter-aural intensity difference and source azimuth exist at the scale of individual binaural clusters. An analysis based on these measures could be applied in any situation where it is useful to demonstrate the presence of a neural map, or to quantify the degree of order in a map.
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Yarrow, Stuart, Razak, Khaleel, Seitz, Aaron R. & Series, Peggy.
Publication Date: 2013
Publication Information
Category: Poster
Original Language: English
2012
  Fisher and Shannon Information in Finite Neural Populations
Yarrow, S & Series, P 2012, 'Fisher and Shannon Information in Finite Neural Populations' 9th Annual Annual Computational and Systems Neurscience Meeting (COSYNE 2012), Salt Lake City, United States, 23/02/12 - 26/02/12, .
The precision of the neural code is commonly investigated using two different families of statistical measures: (i) Shannon mutual information and derived quantities when investigating very small populations of neurons and (ii) Fisher information when studying large populations. These statistical tools are no longer the preserve of theorists, and are being applied by experimental research groups in the analysis of empirical data. Although the relationship between information theoretic and Fisher-based measures in the limit of infinite neural populations is relatively well understood, how these measures compare in finite size populations has not yet been systematically explored. We aim to close this gap. We are particularly interested in understanding which stimuli are best encoded (in terms of discrimination) by a given neuron within a population and how this depends on the chosen measure. We use a novel Monte Carlo approach to compute a stimulus-specific decomposition of the mutual information (the stimulus-specific information) for model populations of up to 256 neurons and show that Fisher information can be
used to accurately estimate both mutual information and stimulus-specific information (SSI) for populations of the order of 100 neurons, even in the presence of biologically realistic variability, noise correlations and experimentally relevant integration times. According to both measures, the stimuli that are best encoded are then those falling at the flanks of the neuron’s tuning curve. In populations of less than around 50 neurons, however, Fisher information can be misleading.
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Yarrow, Stuart & Series, Peggy.
Number of pages: 1
Publication Date: Feb 2012
Publication Information
Category: Poster
Original Language: English
  Fisher and Shannon Information in Finite Neural Populations
Yarrow, S, Challis, E & Seriès, P 2012, 'Fisher and Shannon Information in Finite Neural Populations' Neural Computation, vol 24, no. 7, pp. 1740-1780. DOI: 10.1162/NECO_a_00292
The precision of the neural code is commonly investigated using two families of statistical measures: Shannon mutual information and derived quantities when investigating very small populations of neurons and Fisher information when studying large populations. These statistical tools are no longer the preserve of theorists and are being applied by experimental research groups in the analysis of empirical data. Although the relationship between information-theoretic and Fisher-based measures in the limit of infinite populations is relatively well understood, how these measures compare in finite-size populations has not yet been systematically explored. We aim to close this gap. We are particularly interested in understanding which stimuli are best encoded by a given neuron within a population and how this depends on the chosen measure. We use a novel Monte Carlo approach to compute a stimulus-specific decomposition of the mutual information (the SSI) for populations of up to 256 neurons and show that Fisher information can be used to accurately estimate both mutual information and SSI for populations of the order of 100 neurons, even in the presence of biologically realistic variability, noise correlations, and experimentally relevant integration times. According to both measures, the stimuli that are best encoded are those falling at the flanks of the neuron's tuning curve. In populations of fewer than around 50 neurons, however, Fisher information can be misleading.
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Yarrow, Stuart, Challis, Edward & Seriès, Peggy.
Number of pages: 41
Pages: 1740-1780
Publication Date: 19 Mar 2012
Publication Information
Category: Article
Journal: Neural Computation
Volume: 24
Issue number: 7
ISSN: 0899-7667
Original Language: English
DOIs: 10.1162/NECO_a_00292
2011
  Fisher and Shannon information in finite neural populations
Yarrow, S & Series, P 2011, 'Fisher and Shannon information in finite neural populations' Mathematical Neuroscience 2011, Edinburgh, United Kingdom, 11/04/11 - 13/04/11, .
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Yarrow, Stuart & Series, Peggy.
Publication Date: Apr 2011
Publication Information
Category: Poster
Original Language: English
2010
  A population code for sound locations in the auditory cortex
Razak, K, Seitz, AR, Yarrow, S & Series, P 2010, 'A population code for sound locations in the auditory cortex' Gordon Research Conference - Auditory System, New London, United States, 13/06/10 - 18/06/10, .
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Razak, Khaleel, Seitz, Aaron R., Yarrow, Stuart & Series, Peggy.
Publication Date: Jun 2010
Publication Information
Category: Poster
Original Language: English
2008
  Fisher vs Shannon information in Populations of Neurons
Challis, EAL, Series, P & Yarrow, S 2008, Fisher vs Shannon information in Populations of Neurons. in Deuxième conférence francaise de Neurosciences Computationnelles, "Neurocomp08".
The accuracy of the neural code is commonly investigated using two different measures: (i) Shannon mutual information and derived quantities when investigating very small populations of neurons and (ii) Fisher information when studying large populations. How these measures compare in finite size populations has not been systematically explored. We here aim at filling this gap. We are particularly interested in understanding which stimuli are best encoded by a given neuron in a population and how this depends on the chosen measure. In models of independent neurons, we find that the predictions of Fisher information and of a stimulus-specific decomposition of Shannon information (the SSI) agree very well, even for relatively small population sizes. According to both measures, the stimuli that are best encoded are then those falling at the flanks of the neuronés tuning curve.
General Information
Organisations: Neuroinformatics DTC.
Authors: Challis, Edward A. L., Series, Peggy & Yarrow, Stuart.
Keywords: (Neural coding, Population coding. )
Publication Date: 1 Oct 2008
Publication Information
Category: Conference contribution
Original Language: English

Projects:
Cortical coding of Auditory Space in the Mouse (PhD)