2012
DOI: 10.3389/fnsys.2012.00006
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Input Dependent Cell Assembly Dynamics in a Model of the Striatal Medium Spiny Neuron Network

Abstract: The striatal medium spiny neuron (MSN) network is sparsely connected with fairly weak GABAergic collaterals receiving an excitatory glutamatergic cortical projection. Peri-stimulus time histograms (PSTH) of MSN population response investigated in various experimental studies display strong firing rate modulations distributed throughout behavioral task epochs. In previous work we have shown by numerical simulation that sparse random networks of inhibitory spiking neurons with characteristics appropriate for UP … Show more

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Cited by 37 publications
(74 citation statements)
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References 109 publications
(148 reference statements)
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“…Previous studies employing neural clustering approach identified cell assemblies that represented functional states of the striatal network (Adler et al, 2012; Bakhurin et al, 2016; Carrillo-Reid et al, 2008; Jaidar et al, 2010; Ponzi and Wickens, 2012). We therefore applied similar neural clustering approach (Humphries, 2011; Ozden et al, 2008) to our data and identified neural clusters of D1- or D2- MSN based solely on their calcium activity correlation (Figure S4).…”
Section: Resultsmentioning
confidence: 99%
“…Previous studies employing neural clustering approach identified cell assemblies that represented functional states of the striatal network (Adler et al, 2012; Bakhurin et al, 2016; Carrillo-Reid et al, 2008; Jaidar et al, 2010; Ponzi and Wickens, 2012). We therefore applied similar neural clustering approach (Humphries, 2011; Ozden et al, 2008) to our data and identified neural clusters of D1- or D2- MSN based solely on their calcium activity correlation (Figure S4).…”
Section: Resultsmentioning
confidence: 99%
“…Notable neuroimaging machine learning studies using ICA in dimensionality reduction include (Castro et al 2011a; Chai et al 2010; De Martino et al 2007; Douglas et al 2011; Duff et al 2011; Ince et al 2008; Sato et al 2012; Tagliazucchi et al 2012; Toussaint et al 2012; Yang et al 2010). A review of ICA applications in multi-modal feature reduction tasks is given elsewhere (Sui et al 2012).…”
Section: 0 Unsupervised Feature Reduction Techniquesmentioning
confidence: 99%
“…The resulting classifier is used to make predictions on subjects’ data not present during the training stage. Previous classification studies include; Alzheimer’s disease (AD) (Kloppel et al 2008; Magnin et al 2009; Zhang et al 2011b), Major Depressive Disorder (MDD) (Mwangi et al 2012a; Zeng et al 2012), Autism Spectrum Disorder (ASD) (Ecker et al 2010; Ingalhalikar et al 2011),Schizophrenia (Koutsouleris et al 2009), Mild Cognitive Impairment(MCI) (Haller et al 2010b) and Attention Deficient Hyperactivity Disorder (ADHD) (Lim et al 2013; Sato et al 2012; Zhu et al 2005). Conversely, in regression neuroimaging data with corresponding continuous targets (e.g.…”
Section: 0 Introductionmentioning
confidence: 99%
“…However, with notable exceptions (Brown et al, 2012; Cuingnet et al, 2011), there has been little effort to publish benchmark results that researchers can replicate, reference, and objectively compare against. Today, the increasing availability of several widely used, thoroughly validated, and freely distributed…”
Section: Introductionmentioning
confidence: 99%