scholarly journals Real-time spike sorting platform for high-density extracellular probes with ground-truth validation and drift correction

2017 ◽  
Author(s):  
James J. Jun ◽  
Catalin Mitelut ◽  
Chongxi Lai ◽  
Sergey L. Gratiy ◽  
Costas A. Anastassiou ◽  
...  
Keyword(s):  
Real Time ◽  
Ground Truth ◽  
Neural Population ◽  
Spike Sorting ◽  
Population Activity ◽  
Density Peak ◽  
Precise Control ◽  
Peak Finding ◽  
Single Spike ◽  
Loop Feedback

AbstractElectrical recordings from a large array of electrodes give us access to neural population activity with single-cell, single-spike resolution. These recordings contain extracellular spikes which must be correctly detected and assigned to individual neurons. Despite numerous spike-sorting techniques developed in the past, a lack of high-quality ground-truth datasets hinders the validation of spike-sorting approaches. Furthermore, existing approaches requiring manual corrections are not scalable for hours of recordings exceeding 100 channels. To address these issues, we built a comprehensive spike-sorting pipeline that performs reliably under noise and probe drift by incorporating covariance-based features and unsupervised clustering based on fast density-peak finding. We validated performance of our workflow using multiple ground-truth datasets that recently became available. Our software scales linearly and processes up to 1000-channel recording in real-time using a single workstation. Accurate, real-time spike sorting from large recording arrays will enable more precise control of closed-loop feedback experiments and brain-computer interfaces.

scholarly journals A Biologically Plausible Mechanism to Learn Clusters of Neural Activity

2018 ◽  
Author(s):  
Adrianna R. Loback ◽  
Michael J. Berry
Keyword(s):  
Ganglion Cell ◽  
Real Time ◽  
Neural Activity ◽  
Activity Pattern ◽  
Neural Population ◽  
Retinal Ganglion ◽  
Population Activity ◽  
Visual Areas ◽  
Cortical Hierarchy ◽  
Plausible Mechanism

When correlations within a neural population are strong enough, neural activity in early visual areas is organized into a discrete set of clusters. Here, we show that a simple, biologically plausible circuit can learn and then readout in real-time the identity of experimentally measured clusters of retinal ganglion cell population activity. After learning, individual readout neurons develop cluster tuning, meaning that they respond strongly to any neural activity pattern in one cluster and weakly to all other inputs. Different readout neurons specialize for different clusters, and all input clusters can be learned, as long as the number of readout units is mildly larger than the number of input clusters. We argue that this operation can be repeated as signals flow up the cortical hierarchy.

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

2015 ◽  
Vol 27 (7) ◽  
pp. 1438-1460 ◽  
Author(s):  
Xinyi Deng ◽  
Daniel F. Liu ◽  
Kenneth Kay ◽  
Loren M. Frank ◽  
Uri T. Eden
Keyword(s):  
Real Time ◽  
Point Process ◽  
Point Processes ◽  
Marked Point ◽  
Marked Point Processes ◽  
Marked Point Process ◽  
Spike Sorting ◽  
Decoding Algorithm ◽  
Population Activity ◽  
Spiking Activity

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. Because the unsupervised spike-sorting problem remains unsolved, we took an alternative approach that takes advantage of recent insights into 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’s rule to estimate spatial location from hippocampal neural activity. We validate our approach with a simulation study and 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 equivalent to 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.

scholarly journals Representation learning for neural population activity with Neural Data Transformers

2021 ◽  
Author(s):  
Joel Ye ◽  
Chethan Pandarinath
Keyword(s):  
Real Time ◽  
Representation Learning ◽  
Neural Population ◽  
Dynamical Structure ◽  
Population Activity ◽  
Neural Data ◽  
Reaching Task ◽  
Explicit Dynamics ◽  
Real Time Applications ◽  
Neural Population Activity

AbstractNeural population activity is theorized to reflect an underlying dynamical structure. This structure can be accurately captured using state space models with explicit dynamics, such as those based on recurrent neural networks (RNNs). However, using recurrence to explicitly model dynamics necessitates sequential processing of data, slowing real-time applications such as brain-computer interfaces. Here we introduce the Neural Data Transformer (NDT), a non-recurrent alternative. We test the NDT’s ability to capture autonomous dynamical systems by applying it to synthetic datasets with known dynamics and data from monkey motor cortex during a reaching task well-modeled by RNNs. The NDT models these datasets as well as state-of-the-art recurrent models. Further, its non-recurrence enables 3.9ms inference, well within the loop time of real-time applications and more than 6 times faster than recurrent baselines on the monkey reaching dataset. These results suggest that an explicit dynamics model is not necessary to model autonomous neural population dynamics.Codegithub.com/snel-repo/neural-data-transformers.

scholarly journals On the use of calcium deconvolution algorithms in practical contexts

2019 ◽  
Author(s):  
Mathew H. Evans ◽  
Rasmus S. Petersen ◽  
Mark D. Humphries
Keyword(s):  
Time Series ◽  
Calcium Imaging ◽  
Barrel Cortex ◽  
Ground Truth ◽  
Neural Population ◽  
Imaging Data ◽  
Population Activity ◽  
Ground Truth Data ◽  
Electrophysiological Recordings ◽  
Order Of Magnitude

AbstractCalcium imaging is a powerful tool for capturing the simultaneous activity of large populations of neurons. Here we determine the extent to which our inferences of neural population activity, correlations, and coding depend on our choice of whether and how we deconvolve the calcium time-series into spike-driven events. To this end, we use a range of deconvolution algorithms to create nine versions of the same calcium imaging data obtained from barrel cortex during a pole-detection task. Seeking suitable values for the deconvolution algorithms’ parameters, we optimise them against ground-truth data, and find those parameters both vary by up to two orders of magnitude between neurons and are sensitive to small changes in their values. Applied to the barrel cortex data, we show that a substantial fraction of the processing methods fail to recover simple features of population activity in barrel cortex already established by electrophysiological recordings. Raw calcium time-series contain an order of magnitude more neurons tuned to features of the pole task; yet there is also qualitative disagreement between deconvolution methods on which neurons are tuned to the task. Finally, we show that raw and processed calcium time-series qualitatively disagree on the structure of correlations within the population and the dimensionality of its joint activity. Collectively, our results show that properties of neural activity, correlations, and coding inferred from calcium imaging are sensitive to the choice of if and how spike-evoked events are recovered. We suggest that quantitative results obtained from population calcium-imaging be verified across multiple processed forms of the calcium time-series.

An Area-Efficient 128-Channel Spike Sorting Processor for Real-Time Neural Recording With $0.175~\mu$ W/Channel in 65-nm CMOS

2019 ◽  
Vol 27 (1) ◽  
pp. 126-137 ◽  
Author(s):  
Anh Tuan Do ◽  
Seyed Mohammad Ali Zeinolabedin ◽  
Dongsuk Jeon ◽  
Dennis Sylvester ◽  
Tony Tae-Hyoung Kim
Keyword(s):  
Real Time ◽  
Neural Recording ◽  
Spike Sorting ◽  
Area Efficient

scholarly journals Latent factors and dynamics in motor cortex and their application to brain-machine interfaces

2018 ◽  
Author(s):  
Chethan Pandarinath ◽  
K. Cora Ames ◽  
Abigail A Russo ◽  
Ali Farshchian ◽  
Lee E Miller ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Movement Planning ◽  
Neural Population ◽  
Great Effort ◽  
Single Neurons ◽  
Latent Factors ◽  
Brain Machine Interfaces ◽  
Population Activity ◽  
Systems Perspective ◽  
Behaving Monkeys

In the fifty years since Evarts first recorded single neurons in motor cortex of behaving monkeys, great effort has been devoted to understanding their relation to movement. Yet these single neurons exist within a vast network, the nature of which has been largely inaccessible. With advances in recording technologies, algorithms, and computational power, the ability to study network-level phenomena is increasing exponentially. Recent experimental results suggest that the dynamical properties of these networks are critical to movement planning and execution. Here we discuss this dynamical systems perspective, and how it is reshaping our understanding of the motor cortices. Following an overview of key studies in motor cortex, we discuss techniques to uncover the “latent factors” underlying observed neural population activity. Finally, we discuss efforts to leverage these factors to improve the performance of brain-machine interfaces, promising to make these findings broadly relevant to neuroengineering as well as systems neuroscience.

scholarly journals Interactive Virtual Humans in Real-Time Virtual Environment

2006 ◽  
Vol 5 (2) ◽  
pp. 15-24 ◽  
Author(s):  
Nadia Magnenat-Thalmann ◽  
Arjan Egges
Keyword(s):  
Real Time ◽  
Linear Representation ◽  
Virtual Humans ◽  
Precise Control ◽  
Ongoing Work ◽  
The Face ◽  
Input Motion ◽  
High Level ◽  
Joint Motions ◽  
Vast Area

In this paper, we will present an overview of existing research in the vast area of IVH systems. We will also present our ongoing work on improving the expressive capabilities of IVHs. Because of the complexity of interaction, a high level of control is required over the face and body motions of the virtual humans. In order to achieve this, current approaches try to generate face and body motions from a high-level description. Although this indeed allows for a precise control over the movement of the virtual human, it is difficult to generate a natural-looking motion from such a high-level description. Another problem that arises when animating IVHs is that motions are not generated all the time. Therefore a flexible animation scheme is required that ensures a natural posture even when no animation is playing. We will present MIRAnim, our animation engine, which uses a combination of motion synthesis from motion capture and a statistical analysis of prerecorded motion clips. As opposed to existing approaches that create new motions with limited flexibility, our model adapts existing motions, by automatically adding dependent joint motions. This renders the animation more natural, but since our model does not impose any conditions on the input motion, it can be linked easily with existing gesture synthesis techniques for IVHs. Because we use a linear representation for joint orientations, blending and interpolation is done very efficiently, resulting in an animation engine especially suitable for real-time applications

scholarly journals Cortical state transitions and stimulus response evolve along stiff and sloppy parameter dimensions, respectively

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Adrian Ponce-Alvarez ◽  
Gabriela Mochol ◽  
Ainhoa Hermoso-Mendizabal ◽  
Jaime de la Rocha ◽  
Gustavo Deco
Keyword(s):  
Collective Behavior ◽  
Neural Population ◽  
State Transitions ◽  
Population Activity ◽  
Sensory Inputs ◽  
Stimulus Response ◽  
Spontaneous Neuronal Activity ◽  
The Brain ◽  
Shed Light ◽  
Neural Population Activity

Previous research showed that spontaneous neuronal activity presents sloppiness: the collective behavior is strongly determined by a small number of parameter combinations, defined as ‘stiff’ dimensions, while it is insensitive to many others (‘sloppy’ dimensions). Here, we analyzed neural population activity from the auditory cortex of anesthetized rats while the brain spontaneously transited through different synchronized and desynchronized states and intermittently received sensory inputs. We showed that cortical state transitions were determined by changes in stiff parameters associated with the activity of a core of neurons with low responses to stimuli and high centrality within the observed network. In contrast, stimulus-evoked responses evolved along sloppy dimensions associated with the activity of neurons with low centrality and displaying large ongoing and stimulus-evoked fluctuations without affecting the integrity of the network. Our results shed light on the interplay among stability, flexibility, and responsiveness of neuronal collective dynamics during intrinsic and induced activity.

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