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Title:          Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT
Subject:        Nature Communications 6,  (2015). doi:10.1038/ncomms7177
Author:         Klaus Wimmer
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        Title: Results
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          Title: Mechanisms underlying correlations and CP
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        Title: Figure™1Model architecture and single-trial response.(a) Network model composed of a sensory circuit (MT) with two opposite stimulus-selective excitatory populations E1 and E2 that are coupled to two choice-associated populations D1 and D2 in an integrati
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        Title: Figure™2Impact of bottom-up and top-down correlations on choice probability.(a-c) Network without top-down connections receiving non-replicate zero-coherence stimuli that cause bottom-up correlations (see Methods and Supplementary Fig.™1; bFB=0, sgr=1). A
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          Title: CP in MT during a motion discrimination task
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        Title: Figure™3Bottom-up correlations together with top-down signals can lead to sustained CP and a decaying psychophysical kernel.(a) CP obtained in the network with top-down connections receiving non-replicate stimuli (sgr=1, bFB=1; black) is sustained through
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          Title: Stability of individual CPs increases through the trial
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        Title: Figure™4Stimulus fluctuations impact neuronal variability and reveal a bottom-up component of choice probability in MT.Left panels show the time-course of the spike count Fano factor (a), spike count correlation (b) and CP (c) obtained from responses to n
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        Title: Figure™5Heterogeneity of individual choice probabilities reveals increased stability towards the end of the stimulus interval.(a,b) Individual CP traces of four example cells (a) and CP correlation matrix C(ti, tj) (b) obtained from the homogeneous networ
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          Title: Lagged correlations predict late CP
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          Title: Top-down feedback enhances the stability of the categorization
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        Title: Figure™6MT neuron pairs with rising lagged correlations exhibit enhanced late CP.(a-c) Spike count correlations obtained from neuron pairs in the homogeneous network with bottom-up correlations but no top-down inputs (a), with top-down inputs and no botto
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        Title: Figure™7Top-down inputs strengthen the categorization dynamics and increase their stability.(a,b) Spike rastergram and population rates of the decision populations D1 and D2 (240+240 neurons) during an example trial for the network without (a) and with (b
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        Title: Discussion
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        Title: Methods
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          Title: Physiological data
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          Title: Spike count statistics and choice probabilities
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          Title: Psychophysical data
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          Title: Psychophysical reverse correlation
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        Title: GoldJ. I.ShadlenM. N.The neural basis of decision makingAnnu. Rev. Neurosci.305355742007BrittenK. H.NewsomeW. T.ShadlenM. N.CelebriniS.MovshonJ. A.A relationship between behavioral choice and the visual responses of neurons in macaque MTVis. Neurosci.1387
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       Item: 
        Title: We thank K.H. Britten, J.A. Movshon, W.T. Newsome, M.N. Shadlen and E. Zohary for making their data available in the ’Neural Signal ArchiveCloseCurlyQuote (http://www.neuralsignal.org/), and W. Bair for maintaining this database. We thank B. Cumming, H. S
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   Type: MD5
  Checksum: 35b53dd7d07d9c8579c104a57aab481c5441390e
   Type: SHA-1