Neural coding of naturalistic motion stimuli

Lewen, G. D.; Bialek, William; Steveninck, Rob R. de Ruyter van

doi:10.1088/0954-898x/12/3/305

Cited by 57 publications

(49 citation statements)

References 27 publications

(22 reference statements)

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“…Theories of sensory encoding suggest that neural circuits have evolved to operate efficiently under natural conditions (Simoncelli and Olshausen, 2001; Reinagel 2001). Previous studies have succeeded in predicting/decoding spikes evoked by passive presentation of natural sensory stimuli to anaesthetised/immobilised animals (Lewen et al 2001; Arabzadeh et al 2005; Pillow et al 2008; Mante et al 2008; Lottem and Azouz, 2011; Bale et al 2013), but it has been difficult to extend this approach to encompass natural, active movement of the sense organs. Here we have addressed this general issue, taking advantage of experimental possibilities recently created in the whisker system (O’Connor et al 2010a), and the ability of computational methods, such as GLMs, to uncover stimulus-response relationships even from data with complex statistical structure (Paninski et al 2007; Fairhall and Sompolinski, 2014).…”

Section: Discussionmentioning

confidence: 99%

Prediction of Primary Somatosensory Neuron Activity During Active Tactile Exploration

Campagner

Evans

Bale

et al. 2015

Preprint

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12Primary sensory neurons form the interface between world and brain. Their function is well-13 understood during passive stimulation but, under natural behaving conditions, sense organs 14 are under active, motor control. In an attempt to predict primary neuron firing under natural 15 conditions of sensorimotor integration, we recorded from primary mechanosensory neurons 16 of awake, head-fixed mice as they explored a pole with their whiskers, and simultaneously 17 measured both whisker motion and forces with high-speed videography. Using Generalised 18 Linear Models, we found that primary neuron responses were poorly predicted by kinematics 19 but well-predicted by rotational forces acting on the whisker: both during touch and free-air 20 whisker motion. These results are discrepant with previous studies of passive stimulation, but 21 could be reconciled by differences in the kinematics-force relationship between active and 22 passive conditions. Thus, simple statistical models can predict rich neural activity elicited by 23 natural, exploratory behaviour involving active movement of the sense organs. 24 25 26 45 force ('moment') acting on the whisker, but not by whisker angle and its derivativesa 46 finding at odds with passive stimulation studies (Gibson 1983, Lichtenstein et al 1990; Bale 47 et al 2013).48 49 50 4 RESULTS:51 Primary whisker neuron activity during object exploration is predicted by whisker 52 bending moment 53 We recorded the activity of single PWNs from awake mice ( Figure 1A, E`, Figure 1-figure 54 supplement 1) as they actively explored a metal pole with their whiskers (N = 20 units). At 55 the same time, we recorded whisker motion and whisker shape using high-speed videography 56 (1000 frames/s, Figure 1D, Figure 1-figure supplement 2). Since each PWN innervates a 57 single whisker follicle, we tracked the 'principal whisker' of each recorded unit from frame 58 to frame, and extracted both the angle and curvature of the principal whisker in each video 59 frame (total 1,496,033 frames; Figure 1B-E; Bale et al. 2015). Whiskers are intrinsically 60 curved, and the bending moment on a whisker is proportional to how much this curvature 61 changes due to object contact (Birdwell et al. 2007): we therefore used 'curvature change' as 62 a proxy for bending moment (O'Connor et al. 2010a). Whisker-pole contacts caused 63 substantial whisker bending (curvature change), partially correlated with the whisker angle 64 (Figures 1E, 4E) and, consistent with Szwed et al. (2003) and Leiser and Moxon (2007), 65 robust spiking (Figures 1E, 2E). 66To test between candidate encoding variables, our strategy was to determine how accurately 67 it was possible to predict PWN activity from either the angular position (kinematics) or 68 curvature change (mechanics) of each recorded unit's principal whisker. To predict spikes 69 from whisker state, we used Generalised Linear Models (GLMs) (Figure 2A). GLMs, driven 70 by whisker angle, have previously been shown to provide a simple but accurate description 71 of...

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Section: Discussionmentioning

confidence: 99%

Prediction of Primary Somatosensory Neuron Activity During Active Tactile Exploration

Campagner

Evans

Bale

et al. 2015

Preprint

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“…The result is an Responses of the blowfly H1 neuron to ninety different trajectories of angular velocity vs. time s k (t) converge on a common future at t = 0; each dot represents a single spike generated by H1 in response to these individual signals. Stimulus delivery and recordings as described in Ref [12]. Top right: Probability per unit time of observing a spike in response to trajectories that converge on three different common futures.…”

Section: Neural Coding Of Predictive Informationmentioning

confidence: 99%

Efficient representation as a design principle for neural coding and computation

Bialek

Steveninck

Tishby

2006

2006 IEEE International Symposium on Information Theory

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Does the brain construct an efficient representation of the sensory world? We review progress on this question, focusing on a series of experiments in the last decade which use fly vision as a model system in which theory and experiment can confront each other. Although the idea of efficient representation has been productive, clearly it is incomplete since it doesn't tell us which bits of sensory information are most valuable to the organism. We suggest that an organism which maximizes the (biologically meaningful) adaptive value of its actions given fixed resources should have internal representations of the outside world that are optimal in a very specific information theoretic sense: they maximize the information about the future of sensory inputs at a fixed value of the information about their past. This principle contains as special cases computations which the brain seems to carry out, and it should be possible to test this optimization directly. We return to the fly visual system and report the results of preliminary experiments that are in encouraging agreement with theory.

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“…a binary 83 response function [27,28,[41][42][43]. Binary response functions also offer a reasonable approximation of neural 84 behavior in several systems [28,44,45]. Therefore, we assumed that ON (OFF) neurons fire Poisson spikeswhere Θ is the Heaviside function.…”

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confidence: 99%

Functional diversity among sensory neurons from efficient coding principles

Gjorgjieva

Meister

Sompolinsky

2017

Preprint

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In many sensory systems the neural signal is coded by multiple parallel pathways, suggesting an evolutionary fitness benefit of general nature. A common pathway splitting is that into ON and OFF cells, responding to stimulus increments and decrements, respectively. According to efficient coding theory, sensory neurons have evolved to an optimal configuration for maximizing information transfer given the structure of natural stimuli and circuit constraints. Using the efficient coding framework, we describe two aspects of neural coding: how to optimally split a population into ON and OFF pathways, and how to allocate the firing thresholds of individual neurons given realistic noise levels, stimulus distributions and optimality measures. We find that populations of ON and OFF neurons convey equal information about the stimulus regardless of the ON/OFF mixture, once the thresholds are chosen optimally, independent of stimulus statistics and noise. However, an equal ON/OFF mixture is the most efficient as it uses the fewest spikes to convey this information. The optimal thresholds and coding efficiency, however, depend on noise and stimulus statistics if information is decoded by an optimal linear readout. With non-negligible noise, mixed ON/OFF populations reap significant advantages compared to a homogeneous population. The best coding performance is achieved by a unique mixture of ON/OFF neurons tuned to stimulus asymmetries and noise. We provide a theory for how different cell types work together to encode the full stimulus range using a diversity of response thresholds. The optimal ON/OFF mixtures derived from the theory accord with certain biases observed experimentally.

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Neural coding of naturalistic motion stimuli

Cited by 57 publications

References 27 publications

Prediction of Primary Somatosensory Neuron Activity During Active Tactile Exploration

Prediction of Primary Somatosensory Neuron Activity During Active Tactile Exploration

Efficient representation as a design principle for neural coding and computation

Functional diversity among sensory neurons from efficient coding principles

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