A sequential Monte Carlo approach to estimate biophysical neural models from spikes

Meng, Liang Kim; Kramer, Mark A.; Eden, Uri T.

doi:10.1088/1741-2560/8/6/065006

Cited by 25 publications

(28 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Point process adaptive filters using sequential Monte Carlo approximations to the posterior density have been developed in previous literature (Ergun et al, 2007; Meng et al, 2011). Here we provide a pseudo-code description of the algorithm with extension to marked point processes.…”

Section: 1 Hippocampal Data Collection and Preprocessingmentioning

confidence: 99%

Clusterless Decoding of Position from Multiunit Activity Using a Marked Point Process Filter

et al. 2015

View full text Add to dashboard Cite

Point process filters have been applied successfully to decode neural signals and track neural dynamics. Traditionally, these methods assume that multiunit spiking activity has already been correctly spike-sorted. As a result, these methods are not appropriate for situations where sorting cannot be performed with high precision such as real-time decoding for brain-computer interfaces. As the unsupervised spike-sorting problem remains unsolved, we took an alternative approach that takes advantage of recent insights about clusterless decoding. Here we present a new point process decoding algorithm that does not require multiunit signals to be sorted into individual units. We use the theory of marked point processes to construct a function that characterizes the relationship between a covariate of interest (in this case, the location of a rat on a track) and features of the spike waveforms. In our example, we use tetrode recordings, and the marks represent a four-dimensional vector of the maximum amplitudes of the spike waveform on each of the four electrodes. In general, the marks may represent any features of the spike waveform. We then use Bayes’ rule to estimate spatial location from hippocampal neural activity. We validate our approach with a simulation study and with experimental data recorded in the hippocampus of a rat moving through a linear environment. Our decoding algorithm accurately reconstructs the rat’s position from unsorted multiunit spiking activity. We then compare the quality of our decoding algorithm to that of a traditional spike-sorting and decoding algorithm. Our analyses show that the proposed decoding algorithm performs equivalently or better than algorithms based on sorted single-unit activity. These results provide a path toward accurate real-time decoding of spiking patterns that could be used to carry out content-specific manipulations of population activity in hippocampus or elsewhere in the brain.

show abstract

Section: 1 Hippocampal Data Collection and Preprocessingmentioning

confidence: 99%

Clusterless Decoding of Position from Multiunit Activity Using a Marked Point Process Filter

et al. 2015

View full text Add to dashboard Cite

show abstract

“…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%

Likelihood Methods for Point Processes with Refractoriness

Citi

Brown

et al. 2014

Neural Computation

View full text Add to dashboard Cite

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.

show abstract

“…Our open loop approach is most appropriate for situations where we cannot precisely measure the membrane potential, such as in extracellular recording. While methods exist for estimating such potentials, and other variables such as ion channel conductances, from discrete spike times, they tend to be computationally intensive and introduce an additional set of complications (Koyama and Paninski, 2009; Paninski et al, 2009; Meng et al, 2011). Other approaches abstract the biophysics and try to capture the timing of spikes relative to inputs and spiking history without explicit dynamical representation, such as general linear models (Truccolo et al, 2005; Lawhern et al, 2010).…”

Section: Discussionmentioning

confidence: 99%

“…A prominent remaining hurdle is the system identification step for finding (β k , α k ). While not considered here, a number of empirical approaches could be used for this purpose (Koyama and Paninski, 2009; Paninski et al, 2009; Meng et al, 2011). The testing of our method in an experimental preparation is the subject of ongoing work.…”

Section: Discussionmentioning

confidence: 99%

Control strategies for underactuated neural ensembles driven by optogenetic stimulation

Ching¹,

Ritt²

2013

Front. Neural Circuits

View full text Add to dashboard Cite

Motivated by experiments employing optogenetic stimulation of cortical regions, we consider spike control strategies for ensembles of uncoupled integrate and fire neurons with a common conductance input. We construct strategies for control of spike patterns, that is, multineuron trains of action potentials, up to some maximal spike rate determined by the neural biophysics. We emphasize a constructive role for parameter heterogeneity, and find a simple rule for controllability in pairs of neurons. In particular, we determine parameters for which common drive is not limited to inducing synchronous spiking. For large ensembles, we determine how the number of controllable neurons varies with the number of observed (recorded) neurons, and what collateral spiking occurs in the full ensemble during control of the subensemble. While complete control of spiking in every neuron is not possible with a single input, we find that a degree of subensemble control is made possible by exploiting dynamical heterogeneity. As most available technologies for neural stimulation are underactuated, in the sense that the number of target neurons far exceeds the number of independent channels of stimulation, these results suggest partial control strategies that may be important in the development of sensory neuroprosthetics and other neurocontrol applications.

show abstract

A sequential Monte Carlo approach to estimate biophysical neural models from spikes

Cited by 25 publications

References 45 publications

Clusterless Decoding of Position from Multiunit Activity Using a Marked Point Process Filter

Clusterless Decoding of Position from Multiunit Activity Using a Marked Point Process Filter

Likelihood Methods for Point Processes with Refractoriness

Control strategies for underactuated neural ensembles driven by optogenetic stimulation

Contact Info

Product

Resources

About