Statistical Encoding Model for a Primary Motor Cortical Brain-Machine Interface

Shoham, Sharon; Paninski, Liam; Fellows, Matthew R.; Hatsopoulos, Nicholas G.; Donoghue, John P.; Normann, Richard A.

doi:10.1109/tbme.2005.847542

Cited by 76 publications

(77 citation statements)

References 56 publications

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“…These techniques have been used for basic neuroscience research (Kass et al, 2011;Okatan et al, 2005;Eldawlatly et al, 2009;Berger et al, 2011;Jenison et al, 2011;So et al, 2012), to improve biophysical neural models (Ahrens et al, 2008;Meng et al, 2011;Mensi et al, 2012), or to design better BMIs (Shoham et al, 2005;Srinivasan et al, , 2007Truccolo et al, 2008;Wang and Principe, 2010;Saleh et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

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Likelihood Methods for Point Processes with Refractoriness

Citi

Brown

et al. 2014

Neural Computation

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Likelihood-based encoding models founded on point-processes have received significant attention in the literature because of their ability to significantly improve neural decoding in Brain-Machine Interface applications. We propose an approximation to the likelihood of a point-process model of neurons which holds under assumptions about the continuous time process that are physiologically reasonable for neural spike trains: the presence of a refractory period, the predictability of the conditional intensity function, and its integrability. These are properties which apply to a large class of pointprocesses arising in applications other than neuroscience. The proposed approach has several advantages over state-of-the-art conventional ones. In particular, one can use 1 standard fitting procedures for generalized linear models (GLMs) based on iterativelyreweighted least-squares (IRWLS) while improving the accuracy of the approximation to the likelihood and reducing bias. As a result, the proposed approach can use a larger bin size to achieve the same accuracy as conventional approaches would with a smaller bin size. This is particularly important when analyzing neural data with high mean and instantaneous firing rates. We demonstrate these claims on simulated and real neural spiking activity. By allowing a substantive increase in the required bin size, our algorithm has the potential of lowering the barrier to the use of point-process methods in an increasing number of neural engineering applications.

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Section: Discussionmentioning

confidence: 99%

“…to help to design algorithms for inferring behaviour/stimulus from previously unseen neural data, a processed referred to as decoding and commonly used in the design of brain-machine interfaces (BMIs) (Shoham et al, 2005;Srinivasan et al, 2007;Sanchez et al, 2008).…”

mentioning

confidence: 99%

Likelihood Methods for Point Processes with Refractoriness

Citi

Brown

et al. 2014

Neural Computation

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“…The Poisson variability test is one example. Another example arises naturally in reconstruction paradigms (Brown et al, 1998;Zhang et al, 1998;Brockwell et al, 2004;Wu et al, 2004;Shoham et al, 2005), in which the relationship between behavioral or external variables and the firing rate are explicitly encoded in a model. Once fitted to data, such models can be used to predict, or reconstruct, the behavioral variables from the spike times.…”

Section: Modelsmentioning

confidence: 99%

“…Once fitted to data, such models can be used to predict, or reconstruct, the behavioral variables from the spike times. However, evaluating the validity of Poisson spike count models by model fitting (Brown et al, 1998(Brown et al, , 2002Shoham et al, 2005) requires that the postulated firing rate models are valid and that their parameters have accurately been estimated. Justifying such an assumption will often be difficult.…”

Section: Modelsmentioning

confidence: 99%

Spike Count Reliability and the Poisson Hypothesis

Amarasingham¹,

Chen²,

Geman³

et al. 2006

J. Neurosci.

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The variability of cortical activity in response to repeated presentations of a stimulus has been an area of controversy in the ongoing debate regarding the evidence for fine temporal structure in nervous system activity. We present a new statistical technique for assessing the significance of observed variability in the neural spike counts with respect to a minimal Poisson hypothesis, which avoids the conventional but troubling assumption that the spiking process is identically distributed across trials. We apply the method to recordings of inferotemporal cortical neurons of primates presented with complex visual stimuli. On this data, the minimal Poisson hypothesis is rejected: the neuronal responses are too reliable to be fit by a typical firing-rate model, even allowing for sudden, time-varying, and trial-dependent rate changes after stimulus onset. The statistical evidence favors a tightly regulated stimulus response in these neurons, close to stimulus onset, although not further away.

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“…GLM methods are available in nearly every statistical package and have the optimality properties and statistical inference framework common to all likelihood-based techniques. State-space modeling is a highly flexible signal processing paradigm (Durbin and Koopman 2001;Fahrmeir and Tutz 2001;Kitagawa and Gersch 1996;Mendel 1995) that has been applied in studies of neural dynamics including neural receptive field plasticity (Brown et al 2001;Frank et al 2002Frank et al , 2004, neural coding analyses, neural spike train decoding (Barbieri et al 2004;Brockwell et al 2004;Brown et al 1998;Deneve et al 2007;Eden et al 2004;Ergun et al 2007;Paninski et al 2004;Smith and Brown 2003;Wu et al 2006), the design of control algorithms for brain-machine interfaces and neural prostheses (Shoham et al 2005;Srinivasan et al 2006Srinivasan et al , 2007aWu et al 2006;Yu et al 2007), and analyses of learning (Smith et al 2004(Smith et al , 2005(Smith et al , 2007Wirth et al 2003). Although combined GLM and state-space methods have been developed in the statistics literature (Dey et al 2000;Fahrmeir and Tutz 2001), these combined methods have not been used to analyze between-trial and within-trial dynamics of individual neurons.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Between-Trial and Within-Trial Neural Spiking Dynamics

Czanner

Eden

Wirth

et al. 2008

Journal of Neurophysiology

100

135

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activity from a specific brain region across multiple trials in response to the same stimulus or execution of the same behavioral task is a common neurophysiology protocol. The raster plots of the spike trains often show strong between-trial and within-trial dynamics, yet the standard analysis of these data with the peristimulus time histogram (PSTH) and ANOVA do not consider between-trial dynamics. By itself, the PSTH does not provide a framework for statistical inference. We present a state-space generalized linear model (SS-GLM) to formulate a point process representation of between-trial and within-trial neural spiking dynamics. Our model has the PSTH as a special case. We provide a framework for model estimation, model selection, goodness-of-fit analysis, and inference. In an analysis of hippocampal neural activity recorded from a monkey performing a location-scene association task, we demonstrate how the SS-GLM may be used to answer frequently posed neurophysiological questions including, What is the nature of the between-trial and within-trial task-specific modulation of the neural spiking activity? How can we characterize learning-related neural dynamics? What are the timescales and characteristics of the neuron's biophysical properties? Our results demonstrate that the SS-GLM is a more informative tool than the PSTH and ANOVA for analysis of multiple trial neural responses and that it provides a quantitative characterization of the between-trial and withintrial neural dynamics readily visible in raster plots, as well as the less apparent fast (1-10 ms), intermediate (11-20 ms), and longer (Ͼ20 ms) timescale features of the neuron's biophysical properties.

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Statistical Encoding Model for a Primary Motor Cortical Brain-Machine Interface

Cited by 76 publications

References 56 publications

Likelihood Methods for Point Processes with Refractoriness

Likelihood Methods for Point Processes with Refractoriness

Spike Count Reliability and the Poisson Hypothesis

Analysis of Between-Trial and Within-Trial Neural Spiking Dynamics

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