nach oben

Journal of Computational Neuroscience

Erschienen in:

08.10.2018

Linear-nonlinear-time-warp-poisson models of neural activity

verfasst von: Patrick N. Lawlor, Matthew G. Perich, Lee E. Miller, Konrad P. Kording

Erschienen in: Journal of Computational Neuroscience | Ausgabe 3/2018

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Prominent models of spike trains assume only one source of variability – stochastic (Poisson) spiking – when stimuli and behavior are fixed. However, spike trains may also reflect variability due to internal processes such as planning. For example, we can plan a movement at one point in time and execute it at some arbitrary later time. Neurons involved in planning may thus share an underlying time course that is not precisely locked to the actual movement. Here we combine the standard Linear-Nonlinear-Poisson (LNP) model with Dynamic Time Warping (DTW) to account for shared temporal variability. When applied to recordings from macaque premotor cortex, we find that time warping considerably improves predictions of neural activity. We suggest that such temporal variability is a widespread phenomenon in the brain which should be modeled.

Vorheriger Artikel Replicability or reproducibility? On the replication crisis in computational neuroscience and sharing only relevant detail

Nächster Artikel Modeling the interactions between stimulation and physiologically induced APs in a mammalian nerve fiber: dependence on frequency and fiber diameter

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

Aldworth, Z.N., Miller, J.P., Gedeon, T., Cummins, G.I., Dimitrov, A.G. (2005). Dejittered spike-conditioned stimulus waveforms yield improved estimates of neuronal feature selectivity and spike-timing precision of sensory interneurons. The Journal of Neuroscience, 25(22), 5323–5332. https://doi.org/10.1523/JNEUROSCI.0359-05.2005.PubMedCrossRef

Aldworth, Z.N., Dimitrov, A.G., Cummins, G.I., Gedeon, T., Miller, J.P. (2011). Temporal encoding in a nervous system. PLoS Computational Biology, 7(5), e1002041–e1002041. https://doi.org/10.1371/journal.pcbi.1002041.PubMedPubMedCentralCrossRef

Berndt, D., & Clifford, J. (1994). Using dynamic time warping to find patterns in time series. Workshop on Knowledge Knowledge Discovery in Databases, 398, 359–370.

Buesing, L., Macke, J.H., Sahani, M. (2012). Learning stable, regularised latent models of neural population dynamics. Network: Computation in Neural Systems, 23(1-2), 24–47. https://doi.org/10.3109/0954898X.2012.677095.CrossRef

Carandini, M. (2004). Amplification of trial-to-trial response variability by neurons in visual cortex. PLoS Biology, 2(9), e264–e264. https://doi.org/10.1371/journal.pbio.0020264.PubMedPubMedCentralCrossRef

Chase, S.M., Schwartz, A.B., Kass, R.E. (2010). Latent inputs improve estimates of neural encoding in motor cortex. The Journal of Neuoscience, 30(41), 13,873–13,882. https://doi.org/10.1523/JNEUROSCI.2325-10.2010.

Churchland, M.M., & Shenoy, K.V. (2007). Temporal complexity and heterogeneity of single-neuron activity in premotor and motor cortex. Journal of Neurophysiology, 97(6), 4235–4257. https://doi.org/10.1152/jn.00095.2007.PubMedCrossRef

Cisek, P., & Kalaska, J.F. (2004). Neural correlates of mental rehearsal in dorsal premotor cortex. Nature, 431(7011), 993–996. https://doi.org/10.1038/nature03005.PubMedCrossRef

Cisek, P., & Kalaska, J.F. (2005). Neural correlates of reaching decisions in dorsal premotor cortex: specification of multiple direction choices and final selection of action. Neuron, 45(5), 801–814. https://doi.org/10.1016/j.neuron.2005.01.027.PubMedCrossRef

Cohen, M.R., & Maunsell, J.H.R. (2009). Attention improves performance primarily by reducing interneuronal correlations. Nature Neuroscience, 12(12), 1594–1600. https://doi.org/10.1038/nn.2439.PubMedPubMedCentralCrossRef

Crammond, D.J., & Kalaska, J.F. (2000). Prior information in motor and premotor cortex: activity during the delay period and effect on pre-movement activity. Journal of Neurophysiology, 84(2), 986–1005.PubMedCrossRef

de Ruyter van Steveninck, RR, Lewen, G.D., Strong, S.P., Köberle, R., Bialek, W. (1997). Reproducibility and variability in neural spike trains. Science, 275(5307), 1805–1808. https://doi.org/10.1126/science.275.5307.1805.

Dempster, A.P., Laird, N.M., Rubin, D.B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 38, 1–38.CrossRef

Fernandes, H.L., Stevenson, I.H., Phillips, A.N., Segraves, M.A., Kording, K.P. (2013). Saliency and saccade encoding in the frontal eye field during natural scene search. Cerebral cortex (New York NY), 1991, 1–14. https://doi.org/10.1093/cercor/bht179.

Gold, J.I., & Shadlen, M.N. (2007). The neural basis of decision making. Annual Review of Neuroscience, 30, 535–574. https://doi.org/10.1146/annurev.neuro.29.051605.113038.PubMedCrossRef

Gollisch, T. (2006). Estimating receptive fields in the presence of spike-time jitter. Network (Bristol England), 17(2), 103–129. https://doi.org/10.1080/09548980600569670.CrossRef

Goris, R.L.T, Movshon, J.A., Simoncelli, E.P. (2014). Partitioning neuronal variability. Nature Neuroscience (April). https://doi.org/10.1038/nn.3711.

Guo, Z.V., Inagaki, H.K., Daie, K., Druckmann, S., Gerfen, C.R., Svoboda, K. (2017). Maintenance of persistent activity in a frontal thalamocortical loop. Nature, 545(7653), 181–186. https://doi.org/10.1038/nature22324. http://www.nature.com/doifinder/10.1038/nature22324.PubMedCrossRef

Haith, A.M., Pakpoor, J., Krakauer, J.W. (2016). Independence of movement preparation and movement initiation. Journal of Neuroscience, 36(10), 3007–3015. https://doi.org/10.1523/JNEUROSCI.3245-15.2016.PubMedCrossRef

Hubel, D.H., & Wiesel, T.N. (1962). Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of Physiology, 160, 106–154.PubMedPubMedCentralCrossRef

Kisley, M.A., & Gerstein, G.L. (1999). Trial-to-trial variability and state-dependent modulation of auditory-evoked responses in cortex. Journal of Neuroscience, 19(23), 10,451–10,460.CrossRef

Kollmorgen, S., & Hahnloser, R.H.R. (2014). Dynamic alignment models for neural coding. PLoS Computational Biology, 10(3), e1003508–e1003508. https://doi.org/10.1371/journal.pcbi.1003508.PubMedPubMedCentralCrossRef

Lakshmanan, K.C., Sadtler, P.T., Tyler-Kabara, E.C., Batista, A.P., Yu, B.M. (2015). Extracting low-dimensional latent structure from time series in the presence of delays. Neural Computation, 27(9), 1825–1856. https://doi.org/10.1162/NECO_a_00759.PubMedPubMedCentralCrossRef

Latimer, K.W., Yates, J.L., Meister, M.L.R., Huk, A.C., Pillow, J.W. (2015). Single-trial spike trains in parietal cortex reveal discrete steps during decision-making. Science, 349(6244), 184–187. https://doi.org/10.1126/science.aaa4056.PubMedPubMedCentralCrossRef

Lawhern, V., Wu, W., Hatsopoulos, N., Paninski, L. (2010). Population decoding of motor cortical activity using a generalized linear model with hidden states. Journal of Neuroscience Methods, 189(2), 267–280. https://doi.org/10.1016/j.jneumeth.2010.03.024.PubMedPubMedCentralCrossRef

Lin, I.C., Okun, M., Carandini, M., Harris, K. D. (2015). The nature of shared cortical variability. Neuron, 87(3), 1–13. https://doi.org/10.1016/j.neuron.2015.06.035.CrossRef

Mitchell, J.F., Sundberg, K.A., Reynolds, J.H. (2009). Spatial attention decorrelates intrinsic activity fluctuations in macaque area V4. Neuron, 63(6), 879–888. https://doi.org/10.1016/j.neuron.2009.09.013.PubMedPubMedCentralCrossRef

Nelder, J.A., & Baker, R.J. (1972). Generalized linear models. Encyclopedia of Statistical Sciences.

Nordstrom, M., Fuglevand, A., Enoka, R. (1992). Estimating the strength of common input to human motoneurons from the cross-correlogram. The Journal of Physiology, 453, 547–574.PubMedPubMedCentralCrossRef

Okun, M., Steinmetz, N.A., Cossell, L., Iacaruso, M.F., Ko, H., Barthó, P., Moore, T., Hofer, S.B., Mrsic-Flogel, T.D., Carandini, M., Harris, K.D. (2015). Diverse coupling of neurons to populations in sensory cortex. Nature. https://doi.org/10.1038/nature14273.

Perez, O., Kass, R.E., Merchant, H. (2013). Trial time warping to discriminate stimulus-related from movement-related neural activity. Journal of Neuroscience Methods, 212(2), 203–210. https://doi.org/10.1016/j.jneumeth.2012.10.019.PubMedCrossRef

Pfau, D., Pnevmatikakis, E.A., Paninski, L. (2013). Robust learning of low-dimensional dynamics from large neural ensembles. In Burges, C.J.C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K.Q. (Eds.) Advances in neural information processing systems (Vol. 26, pp. 2391–2399). Red Hook: Curran Associates, Inc.

Pillow, J.W., Shlens, J., Paninski, L., Sher, A., Litke, A.M., Chichilnisky, E.J., Simoncelli, E.P. (2008). Spatio-temporal correlations and visual signalling in a complete neuronal population. Nature, 454(7207), 995–999. https://doi.org/10.1038/nature07140.PubMedPubMedCentralCrossRef

Rabinowitz, N.C., Goris, R.L., Cohen, M., Simoncelli, E. (2015). Attention stabilizes the shared gain of V4 populations. eLife, 4, e08998–e08998. https://doi.org/10.7554/eLife.08998.PubMedPubMedCentralCrossRef

Ramkumar, P., Lawlor, P.N., Glaser, J.I., Wood, D.K., Phillips, A.N., Segraves, M.A., Kording, K.P. (2016). Feature-based attention and spatial selection in frontal eye fields during natural scene search. Journal of Neurophysiology, 116 (3), 1328–1343. https://doi.org/10.1152/jn.01044.2015. http://jn.physiology.org/lookup/doi/10.1152/jn.01044.2015.PubMedPubMedCentralCrossRef

Reich, D.S., Victor, J.D., Knight, B.W., Ozaki, T., Kaplan, E. (1997). Response variability and timing precision of neuronal spike trains in vivo. Journal of Neurophysiology, 77(5), 2836–2841.PubMedCrossRef

Sakoe, H., & Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. ASSP-26, 26(1), 43–49. https://doi.org/10.1109/TASSP.1978.1163055.CrossRef

Shenoy, K.V., Sahani, M., Churchland, M.M. (2013). Cortical control of arm movements: a dynamical systems perspective. Annual Review of Neuroscience, 36(1), 337–359. https://doi.org/10.1146/annurev-neuro-062111-150509.PubMedCrossRef

Siegel, M., Buschman, T.J., Miller, E.K. (2015). Cortical information flow during flexible sensorimotor decisions. Science, 348(6241), 1352–1355. https://doi.org/10.1126/science.aab0551.PubMedPubMedCentralCrossRef

Stevenson, I.H., Rebesco, J.M., Miller, L.E., Körding, K.P. (2008). Inferring functional connections between neurons. Current Opinion in Neurobiology, 18(6), 582–588. https://doi.org/10.1016/j.conb.2008.11.005.PubMedPubMedCentralCrossRef

Stevenson, I.H., London, B.M., Oby, E.R., Sachs, N.A., Reimer, J., Englitz, B., David, S.V., Shamma, S.A., Blanche, T.J., Mizuseki, K., Zandvakili, A., Hatsopoulos, N.G., Miller, L.E., Kording, K.P. (2012). Functional connectivity and tuning curves in populations of simultaneously recorded neurons . PLoS Computational Biology, 8(11), e1002775–e1002775. https://doi.org/10.1371/journal.pcbi.1002775.PubMedPubMedCentralCrossRef

Ventura, V., Cai, C., Kass, R.E. (2005). Trial-to-trial variability and its effect on time-varying dependency between two neurons. Journal of Neurophysiology, 94(4), 2928–2939.PubMedCrossRef

Victor, J.D. (2005). Spike train metrics. Current Opinion in Neurobiology, 15(5), 585–592. https://doi.org/10.1016/j.conb.2005.08.002.PubMedPubMedCentralCrossRef

Victor, J.D., & Purpura, K.P. (1996). Nature and precision of temporal coding in visual cortex: a metric-space analysis. Journal of Neurophysiology, 76(2), 1310–1326.PubMedCrossRef

Vidne, M., Ahmadian, Y., Shlens, J., Pillow, J.W., Kulkarni, J., Litke, A.M., Chichilnisky, E.J., Simoncelli, E., Paninski, L. (2012). Modeling the impact of common noise inputs on the network activity of retinal ganglion cells. Journal of Computational Neuroscience, 33(1), 97–121. https://doi.org/10.1007/s10827-011-0376-2.PubMedCrossRef

Weinrich, M., Wise, S.P., Mauritz, K.H. (1984). A neurophysiological study of the premotor cortex in the rhesus monkey. Brain: A Journal of Neurology, 2, 385–414. https://doi.org/10.1093/brain/107.2.385.CrossRef

Wurtz, R.H. (1969a). Comparison of effects of eye movements and stimulus movements on striate cortex neurons of the monkey. Journal of Neurophysiology, 32(98), 994–994.

Wurtz, R.H. (1969b). Visual receptive fields of striate cortex neurons in awake monkeys. Journal of Neurophysiology, 32(5), 727– 742.

Yu, B.M., Cunningham, J.P., Santhanam, G., Ryu, S.I., Shenoy, K.V., Sahani, M. (2009). Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. Journal of Neurophysiology, 102(1), 614–635. https://doi.org/10.1152/jn.90941.2008.PubMedPubMedCentralCrossRef

Titel: Linear-nonlinear-time-warp-poisson models of neural activity
verfasst von: Patrick N. Lawlor
Matthew G. Perich
Lee E. Miller
Konrad P. Kording
Publikationsdatum: 08.10.2018
Verlag: Springer US
Erschienen in: Journal of Computational Neuroscience / Ausgabe 3/2018
Print ISSN: 0929-5313
Elektronische ISSN: 1573-6873
DOI: https://doi.org/10.1007/s10827-018-0696-6

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 3/2018

A detailed anatomical and mathematical model of the hippocampal formation for the generation of sharp-wave ripples and theta-nested gamma oscillations

Replicability or reproducibility? On the replication crisis in computational neuroscience and sharing only relevant detail

Dendritic sodium spikes endow neurons with inverse firing rate response to correlated synaptic activity

Modeling the interactions between stimulation and physiologically induced APs in a mammalian nerve fiber: dependence on frequency and fiber diameter