Brain-machine interfaces: an overview

Lebedev, Mikhail

doi:10.2478/s13380-014-0212-z

Cited by 75 publications

(45 citation statements)

References 79 publications

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“…A significant challenge for non-invasive experimental enhancement is getting around the isolating effects of the skull. Lebedev ( 2014 ) if this cannot be achieved, then very small invasive implants (Seo et al, 2013 ) may be an alternative solution.…”

Section: Toward the Connectomementioning

confidence: 99%

Experimental enhancement of neurphysiological function

Deca

Koene

2014

Front. Syst. Neurosci.

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Section: Toward the Connectomementioning

confidence: 99%

Experimental enhancement of neurphysiological function

Deca

Koene

2014

Front. Syst. Neurosci.

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“…This emerging concept aims to enhance device integration and function over chronic implant timeframes, with the 'living electrode' being one such example [6e8]. The development of these materials and devices require intensive testing at a number of stages to assess toxicity, material stability in the biological milieu, ability of the material to integrate and support the normal function of surrounding tissues and ultimately perform the desired function over periods of years [9] (For in depth reviews of current and future BCI devices see Lebedev [10] and Lebedev & Nicolelis [11]). …”

Section: Introductionmentioning

confidence: 99%

A critical review of cell culture strategies for modelling intracortical brain implant material reactions

et al. 2016

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“…Decoding algorithms assess to what extent the activity of a population of neurons can be used to 50 estimate an external variable. These methods are the basis for many brain-machine interface applications 51 (Lebedev, 2014;Schwartz, Cui, Weber, & Moran, 2006), but in most cases they may not fully capture 52 information contained in neural variability (Quian Quiroga & Panzeri, 2009). Since neural variability is 53 often non-Poisson and stimulus-dependent, trial-to-trial variability can carry information regarding the 54 stimulus/behavior beyond what is accounted for under the Poisson model or any model that assumes a 55 fixed mean-variance relationship.…”

Section: Introduction 32mentioning

confidence: 99%

Modeling stimulus-dependent variability improves decoding of population neural responses

Ghanbari

Christopher

Read

et al. 2017

Preprint

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Neural responses to repeated presentations of an identical stimulus often show substantial trial-totrial variability. Although the mean firing rate in response to different stimuli or during different movements (tuning curves) have been extensively modeled, the variability of neural responses can also have clear tuning independent of the tuning in the firing rate. This suggests that the variability carries information regarding the stimulus/movement beyond what is encoded in the mean firing rate. Here we demonstrate how taking variability into account can improve neural decoding.In a typical neural coding model spike counts are assumed to be Poisson with the mean response depending on an external variable, such as a stimulus/movement direction. Bayesian decoding methods then use the probabilities under these Poisson tuning models (the likelihood) to estimate the probability of each stimulus given the spikes on a given trial (the posterior). However, under the Poisson model, spike count variability is always exactly equal to the mean (Fano Factor = 1). Here we use the Conway-Maxwell-Poisson (COM-Poisson) model to more flexibly model how neural variability depends on external stimuli. This model contains the Poisson distribution as a special case, but has an additional parameter that allows both over-and underdispersed data, where the variance is greater than (Fano Factor >1) or less than (Fano Factor <1) the mean, respectively.We find that neural responses in both primary motor cortex (M1) and primary visual cortex (V1) have diverse tuning in both their mean firing rates and response variability. These tuning patterns can be accurately described by the COM-Poisson model, and, in both cortical areas, we find that a Bayesian decoder using the COM-Poisson models improves stimulus/movement estimation by 4-8% compared to the Poisson model. The additional layer of information in response variability thus appears to be an important part of the neural code. .For spike counts the distribution is a function of the intensity and dispersion parameters and with < 1 describing over-dispersion and > 1 describing under-dispersed data. Note that with = 1 the COM-Poisson is exactly the Poisson distribution. Here we use the COM-Poisson distribution as the noise model for a GLM [1] to estimate both the mean and variance of neural responses to varying stimuli/movement. In particular, we estimate parameters and γ that map stimulus/movement covariates ( ) and ( ) to neural responses using the link functions log ( ) = ( ) and log( ( )) = ( ) . This framework is in effect a dual-link GLM where both the mean and the variance depend on the stimulus/movement direction θ.Here we estimate the tuning curves using spline bases and maximum a posteriori (MAP) estimation with L2 regularization. Importantly, this approach allows us to model neural responses that are under-dispersed, overdispersed, or that contain intermingled under-and over-dispersed counts [2]. Using the tuning curve models to describe the likelihood of spike responses, ...

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Brain-machine interfaces: an overview

Cited by 75 publications

References 79 publications

Experimental enhancement of neurphysiological function

Experimental enhancement of neurphysiological function

A critical review of cell culture strategies for modelling intracortical brain implant material reactions

Modeling stimulus-dependent variability improves decoding of population neural responses

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