High-Dimensional Cluster Analysis with the Masked EM
          Algorithm

Kadir, Shabnam N.; Goodman, Dan F. M.; Harris, Kenneth D.

doi:10.1162/neco_a_00661

Cited by 286 publications

(272 citation statements)

References 26 publications

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“…Electrophysiological data were recorded to disk for offline analysis. Pre-clusters of putative single cells were estimated automatically using KLUSTAKWIK 1.7 [37] (available at http://klusta-team.github.io/klustakwik/). Final categorizations of single units were identified manually using the MCLUST 3.5 spike sorting software (A.D.R., software available at http://redishlab.neuroscience.umn.edu/mclust/MClust.…”

Section: (D) Electrophysiological Recordingmentioning

confidence: 99%

A functional difference in information processing between orbitofrontal cortex and ventral striatum during decision-making behaviour

Stott¹,

Redish

2014

Phil. Trans. R. Soc. B

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Both orbitofrontal cortex (OFC) and ventral striatum (vStr) have been identified as key structures that represent information about value in decision-making tasks. However, the dynamics of how this information is processed are not yet understood. We recorded ensembles of cells from OFC and vStr in rats engaged in the spatial adjusting delay-discounting task , a decision-making task that involves a trade-off between delay to and magnitude of reward. Ventral striatal neural activity signalled information about reward before the rat's decision, whereas such reward-related signals were absent in OFC until after the animal had committed to its decision. These data support models in which vStr is directly involved in action selection, but OFC processes decision-related information afterwards that can be used to compare the predicted and actual consequences of behaviour.

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Section: (D) Electrophysiological Recordingmentioning

confidence: 99%

A functional difference in information processing between orbitofrontal cortex and ventral striatum during decision-making behaviour

Stott¹,

Redish

2014

Phil. Trans. R. Soc. B

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“…Offline, we detected action potentials by thresholding the voltage trace at 5.0 (MT) or 3.5 (V1) SDs from the mean. We decomposed the waveforms into a wavelet-based feature space (Quiroga et al 2004) and used KlustaKwik (Kadir et al 2014) for automated clustering. The resulting clusters were checked and fine-tuned manually using custom-made cluster and waveform visualization software.…”

Section: Electrophysiologymentioning

confidence: 99%

Evidence and Counterevidence in Motion Perception

Duijnhouwer

Krekelberg

2015

Cereb. Cortex

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Sensory neurons gather evidence in favor of the specific stimuli to which they are tuned, but they could improve their sensitivity by also taking counterevidence into account. The Bours-Lankheet model for motion detection uses counterevidence that relies on a specific combination of the ON and OFF channels in the early visual system. Specifically, the model detects pairs of flashes that occur separated in space and time. If the flashes have the same contrast polarity, they are interpreted as evidence in favor of the corresponding motion. But if they have opposite contrasts, they are interpreted as evidence against it. This mechanism provides an explanation for reverse-phi (the perceived reversal of an apparent motion stimulus due to periodic contrastinversions) that is a conceptual departure from the standard explanations of the effect. Here, we investigate this counterevidence mechanism by measuring directional tuning curves of neurons in the primary visual and middle temporal cortex areas of awake, behaving macaques using constant-contrast and inverting-contrast moving dot stimuli. Our electrophysiological data support the Bours-Lankheet model and suggest that the counterevidence computation occurs at an early stage of neural processing not captured by the standard models.

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“…Here, spike sorting can achieve data reduction with no information loss, provided a good classification accuracy can be achieved [4]. Spike sorting is however typically performed off-line using computationally demanding methods [5], [6]. Recently, algorithms are emerging that are computationally-efficient [7], [8] towards efficient onnode implementations (see general architecture in Fig.…”

Section: Introductionmentioning

confidence: 99%

Clockless continuous-time neural spike sorting: Method, implementation and evaluation

Liu

Pereira

Constandinou

2016

2016 IEEE International Symposium on Circuits and Systems (ISCAS)

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Abstract-In this paper, we present a new method for neural spike sorting based on Continuous Time (CT) signal processing. A set of CT based features are proposed and extracted from CT sampled pulses, and a complete event-driven spike sorting algorithm that performs classification based on these features is developed. Compared to conventional methods for spike sorting, the hardware implementation of the proposed method does not require any synchronisation clock for logic circuits, and thus its power consumption depend solely on the spike activity. This has been implemented using a variable quantisation step CT analogue to digital converter (ADC) with custom digital logic that is driven by level crossing events. Simulation results using synthetic neural data shows a comparable accuracy compared to template matching (TM) and Principle Components Analysis (PCA) based discrete sampled classification.

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High-Dimensional Cluster Analysis with the Masked EM Algorithm

Cited by 286 publications

References 26 publications

A functional difference in information processing between orbitofrontal cortex and ventral striatum during decision-making behaviour

A functional difference in information processing between orbitofrontal cortex and ventral striatum during decision-making behaviour

Evidence and Counterevidence in Motion Perception

Clockless continuous-time neural spike sorting: Method, implementation and evaluation

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