Sensory coding and contrast invariance emerge from the control of plastic inhibition over emergent selectivity

Larisch, René; Gönner, Lorenz; Teichmann, Michael; Hamker, Fred H.

doi:10.1101/2020.04.07.029157

Cited by 2 publications

(2 citation statements)

References 125 publications

(302 reference statements)

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“…First, it has been demonstrated that different inhibitory plasticity rules can match static excitatory connectivity [20][21][22][23]. More recently, it was also shown that various combinations of plasticity and diverse normalisation mechanisms allow for simultaneous development of matching excitatory and inhibitory connectivity in feedforward settings [6,24], as well as the simultaneous learning of excitatory and inhibitory connectivity in recurrent settings [25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Structural influences on synaptic plasticity: the role of presynaptic connectivity in the emergence of E/I co-tuning

Giannakakis

Vinogradov

Buendía

et al. 2023

Preprint

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Experimental studies have shown that in cortical neurons, excitatory and inhibitory incoming currents are strongly correlated, which is hypothesized to be essential for efficient computations. Additionally, cortical neurons exhibit strong preference to particular stimuli, which combined with the co-variability of excitatory and inhibitory inputs indicates a detailed co-tuning of the corresponding populations. Such co-tuning is hypothesized to emerge during development in a self-organized manner. Indeed, theoretical studies have demonstrated that a combination of plasticity rules could lead to the emergence of E/I co-tuning in neurons driven by low noise signals from feedforward connections. However, cortical signals are very noisy and originate in highly recurrent networks, which raises a question on the ability of known plasticity mechanisms to self-organize co-tuned connectivity. We demonstrate that high noise levels combined with random recurrence destroy co-tuning. However, we demonstrate that introducing structure in the connectivity patterns of the recurrent E/ I network, the recurrence does not hinder but enhances the formation of the co-tuned selectivity. We employ a combination of analytical methods and simulation-based inference to uncover constraints on the recurrent connectivity that allow E/I co-tuning to emerge. We find that stronger excitatory connectivity within similarly tuned neurons, combined with more homogeneous inhibitory connectivity enhances the ability of plasticity to produce co-tuning in an upstream population. Our results suggest that structured recurrent connectivity controls information propagation and can enhance the ability of synaptic plasticity to learn input-output relationships in higher brain areas.

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Section: Introductionmentioning

confidence: 99%

Structural influences on synaptic plasticity: the role of presynaptic connectivity in the emergence of E/I co-tuning

Giannakakis

Vinogradov

Buendía

et al. 2023

Preprint

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“…Network models in ANNarchy are defined through equations written in "natural language". ANNarchy has been used to implement models of the BG pathways (Baladron et al, 2019;Gönner et al, 2020;Maith et al, 2020;Villagrasa et al, 2018), spatial attention and vision (Bergelt & Hamker, 2019;Jamalian et al, 2017;Larisch et al, 2020) and learning and memory (Gönner et al, 2017;J. Müller et al, 2018;Schmid et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Virtual deep brain stimulation: Multiscale co-simulation of a spiking basal ganglia model and a whole-brain mean-field model with The Virtual Brain

Meier

Perdikis

Blickensdörfer

et al. 2021

Preprint

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Deep brain stimulation (DBS) has been successfully applied in various neurodegenerative diseases as an effective symptomatic treatment. However, its mechanisms of action within the brain network are still poorly understood. Many virtual DBS models analyze a subnetwork around the basal ganglia and its dynamics as a spiking network with their details validated by experimental data. However, connectomic evidence shows widespread effects of DBS affecting many different cortical and subcortical areas. From a clinical perspective, various effects of DBS besides the motoric impact have been demonstrated. The neuroinformatics platform The Virtual Brain (TVB) offers a modeling framework allowing us to virtually perform stimulation, including DBS, and forecast the outcome from a dynamic systems perspective prior to invasive surgery with DBS lead placement. For an accurate prediction of the effects of DBS, we implement a detailed spiking model of the basal ganglia, which we combine with TVB via our previously developed co-simulation environment. This multiscale co-simulation approach builds on the extensive previous literature of spiking models of the basal ganglia while simultaneously offering a whole-brain perspective on widespread effects of the stimulation going beyond the motor circuit. In the first demonstration of our model, we show that virtual DBS can move the firing rates of a Parkinson’s disease patient’s thalamus - basal ganglia network towards the healthy regime while, at the same time, altering the activity in distributed cortical regions with a pronounced effect in frontal regions. Thus, we provide proof of concept for virtual DBS in a co-simulation environment with TVB. The developed modeling approach has the potential to optimize DBS lead placement and configuration and forecast the success of DBS treatment for individual patients.Highlights-We implement and validate a co-simulation approach of a spiking network model for subcortical regions in and around the basal ganglia and interface it with mean-field network models for each cortical region.-Our simulations are based on a normative connectome including detailed tracts between the cortex and the basal ganglia regions combined with subject-specific optimized weights for a healthy control and a patient with Parkinson’s disease.-We provide proof of concept by demonstrating that the implemented model shows biologically plausible dynamics during resting state including decreased thalamic activity in the virtual patient and during virtual deep brain stimulation including normalized thalamic activity and distributed altered cortical activity predominantly in frontal regions.-The presented co-simulation model can be used to tailor deep brain stimulation for individual patients.

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Sensory coding and contrast invariance emerge from the control of plastic inhibition over emergent selectivity

Cited by 2 publications

References 125 publications

Structural influences on synaptic plasticity: the role of presynaptic connectivity in the emergence of E/I co-tuning

Structural influences on synaptic plasticity: the role of presynaptic connectivity in the emergence of E/I co-tuning

Virtual deep brain stimulation: Multiscale co-simulation of a spiking basal ganglia model and a whole-brain mean-field model with The Virtual Brain

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