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

Spreizer, Sebastian; Angelhuber, Martin; Bahuguna, Jyotika; Aertsen, Ad; Kumar, Arvind

doi:10.1523/eneuro.0348-16.2017

Cited by 17 publications

(17 citation statements)

References 43 publications

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“…Rubchinsky and colleagues argued that healthy states should also operate on the boundary, as it offers many advantages such as easy creation and dissolution of transient neuronal assemblies [84,85] as required by functioning of network shown in other parts of basal ganglia (especially striatum, [86][87][88][89][90]). We also propose that the STN-GPe network should operate close to the border between oscillatory and non-oscillatory states because it makes it easy to generate short epochs of β band oscillations which are often observed in behaving animals.…”

Section: Tandem Of Gpe-stn Spike Bursting Generates Beta Oscillationsmentioning

confidence: 99%

Uncoupling the roles of firing rates and spike bursts in shaping the STN-GPe beta band oscillations

Bahuguna

Sahasranamam²,

Kumar

2020

PLoS Comput Biol

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The excess of 15-30 Hz (β-band) oscillations in the basal ganglia is one of the key signatures of Parkinson's disease (PD). The STN-GPe network is integral to generation and modulation of β band oscillations in basal ganglia. However, the role of changes in the firing rates and spike bursting of STN and GPe neurons in shaping these oscillations has remained unclear. In order to uncouple their effects, we studied the dynamics of STN-GPe network using numerical simulations. In particular, we used a neuron model, in which firing rates and spike bursting can be independently controlled. Using this model, we found that while STN firing rate is predictive of oscillations, GPe firing rate is not. The effect of spike bursting in STN and GPe neurons was state-dependent. That is, only when the network was operating in a state close to the border of oscillatory and non-oscillatory regimes, spike bursting had a qualitative effect on the β band oscillations. In these network states, an increase in GPe bursting enhanced the oscillations whereas an equivalent proportion of spike bursting in STN suppressed the oscillations. These results provide new insights into the mechanisms underlying the transient β bursts and how duration and power of β band oscillations may be controlled by an interplay of GPe and STN firing rates and spike bursts.

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Section: Tandem Of Gpe-stn Spike Bursting Generates Beta Oscillationsmentioning

confidence: 99%

Uncoupling the roles of firing rates and spike bursts in shaping the STN-GPe beta band oscillations

Bahuguna

Sahasranamam²,

Kumar

2020

PLoS Comput Biol

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“…However, in pathological conditions, the network very likely shifts deeper into the oscillation regime (due to the change in firing rates or excessive bursting), where no amount of STN bursting can push the network back to asynchronized regime. Operating on the boundary could have many advantages such as easy creation and dissolution of transient neuronal assemblies [61] as required by functioning of network shown in other parts of basal ganglia (especially striatum, [62][63][64][65][66]). Rubchinsky et.…”

Section: Tandem Of Gpe-stn Bursting Generates Bursts Of Beta Oscillatmentioning

confidence: 99%

Uncoupling the roles of firing rates and spike bursts in shaping the STN-GPe beta band oscillations

Bahuguna

Sahasranamam²,

Kumar

2019

Preprint

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12The excess of 15-30 Hz (β-band) oscillations in the basal ganglia is one of the key 13 signatures of Parkinson's disease (PD). The STN-GPe network is integral to generation 14 and modulation of β band oscillations in basal ganglia. However, the role of changes in 15 the firing rates and spike bursting of STN and GPe neurons in shaping these oscillations 16 has remained unclear. In order to uncouple their effects, we studied the dynamics of 17 STN-GPe network using numerical simulations. In particular, we used a neuron model, 18 in which firing rates and spike bursting can be independently controlled. Using this 19 model, we found that while STN firing rate is predictive of oscillations but GPe firing 20 rate is not. The effect of spike bursting in STN and GPe neurons was state-dependent . 21 That is, only when the network was operating in a state close to the border of 22 oscillatory and non-oscillatory regimes, spike bursting had a qualitative effect on the β 23 band oscillations. In these network states, an increase in GPe bursting enhanced the 24 oscillations whereas an equivalent proportion of spike bursting in STN suppressed the 25 oscillations. These results provide new insights into the mechanisms underlying the 26 transient β bursts and how duration and power of β band oscillations may be controlled 27 by an interplay of GPe and STN firing rates and spike bursts. 28Author summary 29The STN-GPe network undergoes a change in firing rates as well as increased bursting 30 during excessive β band oscillations during Parkinson's disease. In this work we 31 uncouple their effects by using a novel neuron model and show that presence of 32 oscillations is contingent on the increase in STN firing rates, however the effect of spike 33 bursting on oscillations depends on the network state. In a network state on the border 34 of oscillatory and non-oscillatory regime, GPe spike bursting strengthens oscillations. 35The effect of spike bursting in the STN depends on the proportion of GPe neurons 36 bursting. These results suggest a mechanism underlying a transient β band oscillation 37 bursts often seen in experimental data. 38 Introduction 39Parkinson's disease (PD) is a progressive neurodegenerative brain disease caused by the 40 depletion of dopamine neurons in the substantia nigra pars compacta (SNc) [1]. Loss of 41 March 4, 2020 1/36 dopamine causes a host of cognitive and motor impairments. The dopaminergic cells 42 death can be attributed many causes e.g. genetic mutations [1], pathogen that affects 43 the gut microbome and travels to the central nervous systems [2, 3], excitotoxicity [4], 44 and mitichondrial dysfunction [5] etc. [6]. While the etiology of PD is still debated, the 45 behavioral symptoms of PD are accompanied by various changes in the neuronal 46 activity in Basal Ganglia (BG): e.g, increased firing rate of D2 type dopamine receptors 47 expressing striatal neurons [7-9]; increase in spike bursts in striatum, globus pallidus 48 externa (GPe), globus pallidus interna (GPi) and subthalamic nucle...

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“…The similarity between ramp-and-fork patterns of response across neurons in the LIP [3], FEF [4], and striatum [6] during the dot-motion task, is consistent with this (Fig 7a-d). That said, computational models have shown how the striatum's intricate microcircuit [72] can give rise to several types of complex responses to simple cortical input, often taking the form of spontaneously appearing neural ensembles [73][74][75]. Thus a promising avenue for future research is determining if, and how, the dynamics of the striatal micro-circuit can act as a relay-like function during decision formation.…”

Section: Anatomical Mapping Assumptions and Future Directionsmentioning

confidence: 99%

A Probabilistic, Distributed, Recursive Mechanism for Decision-making in the Brain

Caballero

Humphries

Gurney

2016

Preprint

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Decision formation recruits many brain regions, but the procedure they jointly execute is unknown. Here we characterize its essential composition, using as a framework a novel recursive Bayesian algorithm that makes decisions based on spike-trains with the statistics of those in sensory cortex (MT). Using it to simulate the random-dot-motion task, we demonstrate it quantitatively replicates the choice behaviour of monkeys, whilst predicting losses of otherwise usable information from MT. Its architecture maps to the recurrent cortico-basalganglia-thalamo-cortical loops, whose components are all implicated in decision-making. We show that the dynamics of its mapped computations match those of neural activity in the sensorimotor cortex and striatum during decisions, and forecast those of basal ganglia output and thalamus. This also predicts which aspects of neural dynamics are and are not part of inference. Our single-equation algorithm is probabilistic, distributed, recursive, and parallel. Its success at capturing anatomy, behaviour, and electrophysiology suggests that the mechanism implemented by the brain has these same characteristics. Author SummaryDecision-making is central to cognition. Abnormally-formed decisions characterize disorders like over-eating, Parkinson's and Huntington's diseases, OCD, addiction, and compulsive gambling. Yet, a unified account of decisionmaking has, hitherto, remained elusive. Here we show the essential composition of the brain's decision mechanism by matching experimental data from monkeys making decisions, to the knowable function of a novel statistical inference algorithm. Our algorithm maps onto the large-scale architecture of decision circuits in the primate brain, replicating the monkeys' choice behaviour and the dynamics of the neural activity that accompany it. Validated in this way, our algorithm establishes a basic framework for understanding the mechanistic ingredients of decisionmaking in the brain, and thereby, a basic platform for understanding how pathologies arise from abnormal function.

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

Abstract: Visual Abstract

Cited by 17 publications

References 43 publications

Uncoupling the roles of firing rates and spike bursts in shaping the STN-GPe beta band oscillations

Uncoupling the roles of firing rates and spike bursts in shaping the STN-GPe beta band oscillations

Uncoupling the roles of firing rates and spike bursts in shaping the STN-GPe beta band oscillations

A Probabilistic, Distributed, Recursive Mechanism for Decision-making in the Brain

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