15 results found
Dmitruk E, Metzner C, Steuber V, et al., 2021, Larger inter-individual variability of large-scale brain organization in schizophrenia revealed by topological data analysis, Publisher: SPRINGER, Pages: S23-S24, ISSN: 0929-5313
, 2021, 30th Annual Computational Neuroscience Meeting: CNS*2021-Meeting Abstracts., J Comput Neurosci, Vol: 49, Pages: 3-208
Katakura O, Maex R, Kadir S, et al., 2021, Determinants of pattern recognition in a network model of cerebellar cortex, Publisher: SPRINGER, Pages: S176-S176, ISSN: 0929-5313
, 2020, 29th Annual Computational Neuroscience Meeting: CNS*2020., BMC Neurosci, Vol: 21
Keshavarzi M, Kegler M, Kadir S, et al., 2020, Transcranial alternating current stimulation in the theta band but not in the delta band modulates the comprehension of naturalistic speech in noise, NeuroImage, Vol: 210, ISSN: 1053-8119
Auditory cortical activity entrains to speech rhythms and has been proposed as a mechanism for online speech processing. In particular, neural activity in the theta frequency band (4–8 Hz) tracks the onset of syllables which may aid the parsing of a speech stream. Similarly, cortical activity in the delta band (1–4 Hz) entrains to the onset of words in natural speech and has been found to encode both syntactic as well as semantic information. Such neural entrainment to speech rhythms is not merely an epiphenomenon of other neural processes, but plays a functional role in speech processing: modulating the neural entrainment through transcranial alternating current stimulation influences the speech-related neural activity and modulates the comprehension of degraded speech. However, the distinct functional contributions of the delta- and of the theta-band entrainment to the modulation of speech comprehension have not yet been investigated. Here we use transcranial alternating current stimulation with waveforms derived from the speech envelope and filtered in the delta and theta frequency bands to alter cortical entrainment in both bands separately. We find that transcranial alternating current stimulation in the theta band but not in the delta band impacts speech comprehension. Moreover, we find that transcranial alternating current stimulation with the theta-band portion of the speech envelope can improve speech-in-noise comprehension beyond sham stimulation. Our results show a distinct contribution of the theta- but not of the delta-band stimulation to the modulation of speech comprehension. In addition, our findings open up a potential avenue of enhancing the comprehension of speech in noise.
Reichenbach J, Kadir S, Kaza C, et al., 2020, Modulation of speech-in-noise comprehension through transcranial current stimulation with the phase-shifted speech envelope, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol: 28, Pages: 23-31, ISSN: 1534-4320
Neural activity tracks the envelope of a speech signalat latencies from 50 ms to 300 ms. Modulating this neural trackingthrough transcranial alternating current stimulation influencesspeech comprehension. Two important variables that can affectthis modulation are the latency and the phase of the stimulationwith respect to the sound. While previous studies have found aninfluence of both variables on speech comprehension, theinteraction between both has not yet been measured. We presented17 subjects with speech in noise coupled with simultaneoustranscranial alternating current stimulation. The currents werebased on the envelope of the target speech but shifted by differentphases, as well as by two temporal delays of 100 ms and 250 ms.We also employed various control stimulations, and assessed thesignal-to-noise ratio at which the subject understood half of thespeech. We found that, at both latencies, speech comprehension ismodulated by the phase of the current stimulation. However, theform of the modulation differed between the two latencies. Phaseand latency of neurostimulation have accordingly distinctinfluences on speech comprehension. The different effects at thelatencies of 100 ms and 250 ms hint at distinct neural processes forspeech processing.
Pachitariu M, Steinmetz N, Kadir S, et al., 2016, Kilosort: realtime spike-sorting for extracellular electrophysiology with hundreds of channels
<jats:title>Abstract</jats:title><jats:p>Advances in silicon probe technology mean that in vivo electrophysiological recordings from hundreds of channels will soon become commonplace. To interpret these recordings we need fast, scalable and accurate methods for spike sorting, whose output requires minimal time for manual curation. Here we introduce Kilosort, a spike sorting framework that meets these criteria, and show that it allows rapid and accurate sorting of large-scale in vivo data. Kilosort models the recorded voltage as a sum of template waveforms triggered on the spike times, allowing overlapping spikes to be identified and resolved. Rapid processing is achieved thanks to a novel low-dimensional approximation for the spatiotemporal distribution of each template, and to batch-based optimization on GPUs. A novel post-clustering merging step based on the continuity of the templates substantially reduces the requirement for subsequent manual curation operations. We compare Kilosort to an established algorithm on data obtained from 384-channel electrodes, and show superior performance, at much reduced processing times. Data from 384-channel electrode arrays can be processed in approximately realtime. Kilosort is an important step towards fully automated spike sorting of multichannel electrode recordings, and is freely available (<jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="http://github.com/cortex-lab/Kilosort">github.com/cortex-lab/Kilosort</jats:ext-link>).</jats:p>
Developments in microfabrication technology have enabled the production of neural electrode arrays with hundreds of closely spaced recording sites, and electrodes with thousands of sites are under development. These probes in principle allow the simultaneous recording of very large numbers of neurons. However, use of this technology requires the development of techniques for decoding the spike times of the recorded neurons from the raw data captured from the probes. Here we present a set of tools to solve this problem, implemented in a suite of practical, user-friendly, open-source software. We validate these methods on data from the cortex, hippocampus and thalamus of rat, mouse, macaque and marmoset, demonstrating error rates as low as 5%.
Pachitariu M, Steinmetz N, Kadir S, et al., 2016, Fast and accurate spike sorting of high-channel count probes with KiloSort, Pages: 4455-4463, ISSN: 1049-5258
New silicon technology is enabling large-scale electrophysiological recordings in vivo from hundreds to thousands of channels. Interpreting these recordings requires scalable and accurate automated methods for spike sorting, which should minimize the time required for manual curation of the results. Here we introduce KiloSort, a new integrated spike sorting framework that uses template matching both during spike detection and during spike clustering. KiloSort models the electrical voltage as a sum of template waveforms triggered on the spike times, which allows overlapping spikes to be identified and resolved. Unlike previous algorithms that compress the data with PCA, KiloSort operates on the raw data which allows it to construct a more accurate model of the waveforms. Processing times are faster than in previous algorithms thanks to batch-based optimization on GPUs. We compare KiloSort to an established algorithm and show favorable performance, at much reduced processing times. A novel post-clustering merging step based on the continuity of the templates further reduced substantially the number of manual operations required on this data, for the neurons with near-zero error rates, paving the way for fully automated spike sorting of multichannel electrode recordings.
Kadir SN, Goodman DFM, Harris KD, 2014, High-dimensional cluster analysis with the masked EM algorithm, Neural Computation, Vol: 26, Pages: 2379-2394, ISSN: 0899-7667
Cluster analysis faces two problems in high dimensions: the "curse of dimensionality" that can lead to overfitting and poor generalization performance and the sheer time taken for conventional algorithms to process large amounts of high-dimensional data. We describe a solution to these problems, designed for the application of spike sorting for nextgeneration, high-channel-count neural probes. In this problem, only a small subset of features provides information about the cluster membership of any one data vector, but this informative feature subset is not the same for all data points, rendering classical feature selection ineffective.We introduce a "masked EM" algorithm that allows accurate and time-efficient clustering of up to millions of points in thousands of dimensions. We demonstrate its applicability to synthetic data and to real-world high-channel-count spike sorting data.
Fruehbis-Krueger A, Kadir S, 2012, Zeta functions for families of Calabi Yau <i>n</i>-folds with singularities, ZETA FUNCTIONS IN ALGEBRA AND GEOMETRY, Vol: 566, Pages: 21-41, ISSN: 0271-4132
, 2012, Zeta Functions in Algebra and Geometry, Publisher: American Mathematical Society, ISBN: 9780821869000
Kadir S, Lynker M, Schimmrigk R, 2011, String modular phases in Calabi-Yau families, JOURNAL OF GEOMETRY AND PHYSICS, Vol: 61, Pages: 2453-2469, ISSN: 0393-0440
Kadir S, Yui N, 2008, Motives and Mirror Symmetry for Calabi-Yau Orbifolds, Workshop on Modular Forms and String Duality, Publisher: AMER MATHEMATICAL SOC, Pages: 3-+, ISSN: 1069-5265
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