The influence of population size, noise strength and behavioral task on best-encoded stimulus for neurons with unimodal or monotonic tuning curves

Yarrow, Stuart; Seriès, Peggy

doi:10.3389/fncom.2015.00018

Cited by 11 publications

(9 citation statements)

References 44 publications

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“…Whereas bell-shaped tuning curves-as in our encoding layer-may be optimal for representing locations within a topographic map, sigmoid-shaped tuning curves-as in our decoding layer-are optimal for representing specific values (Sanger, 2003). Consistent with this, the Fisher information of sigmoidal tuning curves is centered around a single point in feature space (Yarrow and Series, 2015). We therefore reasoned that sigmoid-shaped tuning curves provide a natural means by which the somatosensory cortex could represent a unit of tactile space.…”

Section: A Spatial Function For Large Somatosensory Receptive Fieldsmentioning

confidence: 64%

A neural surveyor to map touch on the body

Miller

Fabio

Beers

et al. 2020

Preprint

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Perhaps the most recognizable sensory map in all of neuroscience is the somatosensory homunculus. Though it seems straightforward, this simple representation belies the complex link between an activation in somatosensory Area 3b and the associated touch location on the body. Any isolated activation is spatially ambiguous without a neural decoder that can read its position within the entire map, though how this is computed by neural networks is unknown.We propose that somatosensory cortex implements multilateration, a common computation used by surveying and GPS systems to localize objects. Specifically, to decode touch location on the body, the somatosensory system estimates the relative distance between the afferent input and the body's joints. We show that a simple feedforward neural network which captures the receptive field properties of somatosensory cortex implements a Bayes-optimal multilateral decoder via a combination of bell-shaped (Area 3b) and sigmoidal (Areas 1/2) tuning curves. Simulations demonstrated that this decoder produced a unique pattern of localization variability between two joints that was not produced by other known neural decoders. Finally, we identify this neural signature of multilateration in actual psychophysical experiments, suggesting that it is a candidate computational mechanism underlying tactile localization. take place in the frontal and parietal cortices (Burnod et al., 1999;Crawford et al., 2004;Medendorp et al., 2005;Pesaran et al., 2006).Equally crucial to localizing objects in the environment is localizing objects on the personal space of the body. Despite over 180 years of research on the sense of touch (Weber, 1834), the computations underlying tactile localization remain largely unknown. Recent accounts have suggested that tactile localization requires two computational steps Medina and Coslett, 2010). First, afferent input must be localized within a topographic map in

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Section: A Spatial Function For Large Somatosensory Receptive Fieldsmentioning

confidence: 64%

A neural surveyor to map touch on the body

Miller

Fabio

Beers

et al. 2020

Preprint

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“…It is widely believed that neural networks can overcome high variability in individual neurons by implementing population coding schemes (Abbott and Dayan, 1999). Conversely, as individual neurons become more variable, the size of the population required for optimal decoding increases (Yarrow and Seriès, 2015). For place coding in temporal lobe epilepsy – individual place cells become more variable, and the population size encoding animal position is reduced, which would collectively deteriorate spatial information coding in cell assembles.…”

Section: Discussionmentioning

confidence: 99%

The impact of pathological high-frequency oscillations on hippocampal network activity in rats with chronic epilepsy

Ewell

Fischer

Leibold

et al. 2019

eLife

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In epilepsy, brain networks generate pathological high-frequency oscillations (pHFOs) during interictal periods. To understand how pHFOs differ from normal oscillations in overlapping frequency bands and potentially perturb hippocampal processing, we performed high-density single unit and local field potential recordings from hippocampi of behaving rats with and without chronic epilepsy. In epileptic animals, we observed two types of co-occurring fast oscillations, which by comparison to control animals we could classify as ‘ripple-like’ or ‘pHFO’. We compared their spectral characteristics, brain state dependence, and cellular participants. Strikingly, pHFO occurred irrespective of brain state, were associated with interictal spikes, engaged distinct subnetworks of principal neurons compared to ripple-like events, increased the sparsity of network activity, and initiated both general and immediate disruptions in spatial information coding. Taken together, our findings suggest that events that result in pHFOs have an immediate impact on memory processes, corroborating the need for proper classification of pHFOs to facilitate therapeutic interventions that selectively target pathological activity.

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“…A large body of literature in theoretical and experimental neuroscience investigated tuning curve shape under the aspect of optimal coding (Seung and Sompolinsky, 1993;Brunel and Nadal, 1998;Panzeri et al, 1999;Eurich et al, 2000;Bethge et al, 2002Bethge et al, , 2003Todorov, 2002;Sanger, 2003;Harper and McAlpine, 2004;Johnson and Ray, 2004;Seriès et al, 2004;Lánskỳ and Greenwood, 2005;Brown and Bäcker, 2006;Montemurro and Panzeri, 2006;Toyoizumi et al, 2006;McDonnell and Stocks, 2008;Geisler et al, 2009;Nikitin et al, 2009;Yarrow and Seriès, 2015). Also the width of tuning curves was analyzed from an information theoretical viewpoint.…”

Section: Functional Role Of Tuning Curve Widthmentioning

confidence: 99%

Adaptive Tuning Curve Widths Improve Sample Efficient Learning

Meier

Dang-Nhu

Steger

2020

Front. Comput. Neurosci.

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Natural brains perform miraculously well in learning new tasks from a small number of samples, whereas sample efficient learning is still a major open problem in the field of machine learning. Here, we raise the question, how the neural coding scheme affects sample efficiency, and make first progress on this question by proposing and analyzing a learning algorithm that uses a simple reinforce-type plasticity mechanism and does not require any gradients to learn low dimensional mappings. It harnesses three bio-plausible mechanisms, namely, population codes with bell shaped tuning curves, continous attractor mechanisms and probabilistic synapses, to achieve sample efficient learning. We show both theoretically and by simulations that population codes with broadly tuned neurons lead to high sample efficiency, whereas codes with sharply tuned neurons account for high final precision. Moreover, a dynamic adaptation of the tuning width during learning gives rise to both, high sample efficiency and high final precision. We prove a sample efficiency guarantee for our algorithm that lies within a logarithmic factor from the information theoretical optimum. Our simulations show that for low dimensional mappings, our learning algorithm achieves comparable sample efficiency to multi-layer perceptrons trained by gradient descent, although it does not use any gradients. Furthermore, it achieves competitive sample efficiency in low dimensional reinforcement learning tasks. From a machine learning perspective, these findings may inspire novel approaches to improve sample efficiency. From a neuroscience perspective, these findings suggest sample efficiency as a yet unstudied functional role of adaptive tuning curve width.

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The influence of population size, noise strength and behavioral task on best-encoded stimulus for neurons with unimodal or monotonic tuning curves

Cited by 11 publications

References 44 publications

A neural surveyor to map touch on the body

A neural surveyor to map touch on the body

The impact of pathological high-frequency oscillations on hippocampal network activity in rats with chronic epilepsy

Adaptive Tuning Curve Widths Improve Sample Efficient Learning

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