Significance of Input Correlations in Striatal Function

Yim, Man Yi; Aertsen, Ad; Kumar, Arvind

doi:10.1371/journal.pcbi.1002254

Cited by 36 publications

(51 citation statements)

References 71 publications

(102 reference statements)

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“…That synchronization of FSIs is critical for striatal balance was confirmed when increasing the level of correlation between cortical inputs to FSIs restored the balance. Our finding that a change to correlation between FSIs did not change the mean firing of MSNs in the network is consistent with another computational study that used simplified singleneuron models to study the effects of correlated inputs on striatal function (Yim et al 2011). This implies that, even though changes to correlation between FSIs might not affect overall firing frequency of MSNs, they can modulate D1 and D2 MSN firing rates differently.…”

Section: Discussionsupporting

confidence: 90%

Synchronized firing of fast-spiking interneurons is critical to maintain balanced firing between direct and indirect pathway neurons of the striatum

Damodaran

Evans

Blackwell

2014

Journal of Neurophysiology

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Damodaran S, Evans RC, Blackwell KT. Synchronized firing of fast-spiking interneurons is critical to maintain balanced firing between direct and indirect pathway neurons of the striatum. J Neurophysiol 111: 836 -848, 2014. First published December 4, 2013 doi:10.1152/jn.00382.2013.-The inhibitory circuits of the striatum are known to be critical for motor function, yet their contributions to Parkinsonian motor deficits are not clear. Altered firing in the globus pallidus suggests that striatal medium spiny neurons (MSN) of the direct (D1 MSN) and indirect pathway (D2 MSN) are imbalanced during dopamine depletion. Both MSN classes receive inhibitory input from each other and from inhibitory interneurons within the striatum, specifically the fast-spiking interneurons (FSI). To investigate the role of inhibition in maintaining striatal balance, we developed a biologically-realistic striatal network model consisting of multicompartmental neuron models: 500 D1 MSNs, 500 D2 MSNs and 49 FSIs. The D1 and D2 MSN models are differentiated based on published experiments of individual channel modulations by dopamine, with D2 MSNs being more excitable than D1 MSNs. Despite this difference in response to current injection, in the network D1 and D2 MSNs fire at similar frequencies in response to excitatory synaptic input. Simulations further reveal that inhibition from FSIs connected by gap junctions is critical to produce balanced firing. Although gap junctions produce only a small increase in synchronization between FSIs, removing these connections resulted in significant firing differences between D1 and D2 MSNs, and balanced firing was restored by providing synchronized cortical input to the FSIs. Together these findings suggest that desynchronization of FSI firing is sufficient to alter balanced firing between D1 and D2 MSNs.Parkinson's disease; striatum; medium-spiny neuron; fast-spiking interneuron; gap junctions DOPAMINE DEPLETION LEADS TO an imbalance in the activation of the direct and indirect pathways of the striatum (Albin et al. 1989;DeLong 1990;Hikosaka et al. 2000;Mallet et al. 2006;Obeso et al. 2004). The direct pathway is composed of striatal medium spiny neurons (MSNs) that express dopamine D1 receptors (D1 MSNs) and projects to both the substantia nigra pars reticulata and the internal segment of the globus pallidus. The indirect pathway is composed of MSNs that express dopamine D2 receptors (D2 MSNs) and projects to the external segment of the globus pallidus. The two populations of MSNs differ in their intrinsic excitability, with D2 MSNs more responsive to somatic current injection (Gertler et al. 2008). Inhibitory circuits of the striatal network, including feedback inhibition from other MSNs and feedforward inhibition from fast-spiking interneurons (FSIs), modulate this excitability (Koós and Tepper 1999;Plenz 2003). The contributions of these circuits to maintaining balanced firing between D1 and D2 MSNs in the network (Cui et al. 2013;Mallet et al. 2006) are not clear.A strong source of GABAergic in...

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Section: Discussionsupporting

confidence: 90%

Synchronized firing of fast-spiking interneurons is critical to maintain balanced firing between direct and indirect pathway neurons of the striatum

Damodaran

Evans

Blackwell

2014

Journal of Neurophysiology

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“…It is believed that the striatum integrates these convergent streams of information, with the resulting activity acting on basal ganglia output nuclei connected to association and motor control areas in the cortex (Alexander et al 1986). Computational and experimental studies suggest that the converging input to the striatum leads to the formation of functionally specialized subsets of MSNs with temporally correlated activity patterns (Adler et al 2012;Carrillo-Reid et al 2008;Humphries et al 2009;Ponzi and Wickens 2010;Yim et al 2011). These findings in the striatum, and a large body of work focusing on cortical circuits (Averbeck et al 2006;Bair et al 2001;Cohen and Maunsell 2009;Mitchell et al 2009;Shadlen and Newsome 1998;Zohary et al 1994), implicate correlated activity in neural computation and behavior.…”

mentioning

confidence: 99%

Temporal correlations among functionally specialized striatal neural ensembles in reward-conditioned mice

Bakhurin

Mac

Golshani

et al. 2016

Journal of Neurophysiology

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Bakhurin KI, Mac V, Golshani P, Masmanidis SC. Temporal correlations among functionally specialized striatal neural ensembles in reward-conditioned mice. J Neurophysiol 115: 1521-1532, 2016. First published January 13, 2016; doi:10.1152/jn.01037.2015.-As the major input to the basal ganglia, the striatum is innervated by a wide range of other areas. Overlapping input from these regions is speculated to influence temporal correlations among striatal ensembles. However, the network dynamics among behaviorally related neural populations in the striatum has not been extensively studied. We used large-scale neural recordings to monitor activity from striatal ensembles in mice undergoing Pavlovian reward conditioning. A subpopulation of putative medium spiny projection neurons (MSNs) was found to discriminate between cues that predicted the delivery of a reward and cues that predicted no specific outcome. These cells were preferentially located in lateral subregions of the striatum. Discriminating MSNs were more spontaneously active and more correlated than their nondiscriminating counterparts. Furthermore, discriminating fast spiking interneurons (FSIs) represented a highly prevalent group in the recordings, which formed a strongly correlated network with discriminating MSNs. Spike time cross-correlation analysis showed the existence of synchronized activity among FSIs and feedforward inhibitory modulation of MSN spiking by FSIs. These findings suggest that populations of functionally specialized (cue-discriminating) striatal neurons have distinct network dynamics that sets them apart from nondiscriminating cells, potentially to facilitate accurate behavioral responding during associative reward learning.

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“…The various neuron and synapse parameters are summarized in Table 1. Whenever possible, we used parameters corresponding to the striatal network (Yim et al, 2011). …”

Section: Methodsmentioning

confidence: 99%

Activity Dynamics and Signal Representation in a Striatal Network Model with Distance-Dependent Connectivity

Spreizer

Angelhuber

Bahuguna

et al. 2017

eNeuro

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Significance of Input Correlations in Striatal Function

Cited by 36 publications

References 71 publications

Synchronized firing of fast-spiking interneurons is critical to maintain balanced firing between direct and indirect pathway neurons of the striatum

Synchronized firing of fast-spiking interneurons is critical to maintain balanced firing between direct and indirect pathway neurons of the striatum

Temporal correlations among functionally specialized striatal neural ensembles in reward-conditioned mice

Activity Dynamics and Signal Representation in a Striatal Network Model with Distance-Dependent Connectivity

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