Network control through coordinated inhibition

Herstel, Lotte J; Wierenga, Corette J.

doi:10.1016/j.conb.2020.08.001

Cited by 32 publications

(25 citation statements)

References 82 publications

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“…It has been hypothesized that a key component of this flexibility is the recurrent nature of neural connections [10, 11, 17]. Quite often, the focus of recurrent structures is on mutual excitation [5, 18, 19, 20, 21, 22] or feedback inhibition [5, 19, 23, 24, 25, 26, 27], and the role of mutual inhibition has not been well studied (the term “recurrent” has been used interchangeably across literature – see Fig. 1a for how it is defined in this study).…”

Section: Introductionmentioning

confidence: 99%

“…Secondly, evident shows that there are functional differences between feedback and mutual inhibition. For example, feedback inhibition is composed of a interconnected pair of excitatory and inhibitory neuron, which balances the network [26, 27, 36], allowing it to approximate arbitrary functions [17] and are useful in gain control. On the other hand, mutual inhibition increases the number of basin of attractions [5], allowing the network to perform functions such as winner-take-all decision [5, 37, 38, 39], bistable perception [28, 29], oscillations [40, 11, 10], associative memory [41, 16] and grid formation [42].…”

Section: Introductionmentioning

confidence: 99%

“…Secondly, evidence shows that there are functional differences between feedback and mutual inhibition. For example, feedback inhibition is composed of an interconnected pair of excitatory and inhibitory neurons, which balances the network [30,31,40], allowing it to approximate arbitrary functions [21] and are useful in gain control. On the other hand, many studies have examined inhibition in a more general way.…”

Section: Introductionmentioning

confidence: 99%

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Augmenting Flexibility: Mutual Inhibition Between Inhibitory Neurons Expands Functional Diversity

Liu

White

2020

Preprint

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One of the most intriguing observations of recurrent neural circuits is their flexibility. Seemingly, this flexibility extends far beyond the ability to learn, but includes the ability to use learned procedures to respond to novel situations. Here, we report that this flexibility arises from the synergistic interplay between recurrent mutual excitation and recurrent mutual inhibition. Specifically, we show that mutual inhibition is critical in expanding the functionality of the circuit, far beyond what feedback inhibition alone can accomplish. By taking advantage of dynamical systems theory and bifurcation analysis, we show mutual inhibition doubles the number of cusp bifurcations in the system in small neural circuits. As a concrete example, we build a simulation model of a class of functional motifs we call Coupled Recurrent inhibitory and Recurrent excitatory Loops (CRIRELs). These CRIRELs have the advantage of being multi-functional, performing a plethora of functions, including decisions, switches, toggles, central pattern generators, depending solely on the input type. We then use bifurcation theory to show how mutual inhibition gives rise to this broad repertoire of possible functions. Finally, we demonstrate how this trend also holds for larger networks, and how mutual inhibition greatly expands the amount of information a recurrent network can hold.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Augmenting Flexibility: Mutual Inhibition Between Inhibitory Neurons Expands Functional Diversity

Liu

White

2020

Preprint

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“…Inhibitory plasticity has more diverse forms than excitatory plasticity, so understanding its functional roles is challenging (Hennequin et al, 2017). An emerging principle is that inhibitory plasticity restores the balance of excitation and inhibition up to dendrite-specific level, in response to the imbalance caused by excitatory plasticity or environmental change (Herstel and Wierenga, 2021;Hennequin et al, 2017). For example, the activation of NMDA receptors potentiates the dendrite-targeting synapses from SST interneurons (Chiu et al, 2018), which may help SST interneurons better gate off the dendrites frequently or strongly activated by excitatory inputs.…”

Section: Discussionmentioning

confidence: 99%

Evolutionary learning in the brain by heterosynaptic plasticity

Chen

Yang

et al. 2021

Preprint

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The way in which the brain modifies synapses to improve the performance of complicated networks remains one of the biggest mysteries in neuroscience. Existing proposals lack sufficient experimental support, and neglect inter-cellular signaling pathways ubiquitous in the brain. Here we show that the heterosynaptic plasticity between hippocampal or cortical pyramidal cells mediated by diffusive nitric oxide and astrocyte calcium wave, together with flexible dendritic gating of somatostatin interneurons, implies an evolutionary algorithm (EA). In simulation, this EA is able to train deep networks with biologically plausible binary weights in MNIST classification and Atari-game playing tasks up to performance comparable with continuous-weight networks trained by gradient-based methods. Our work leads paradigmatically fresh understanding of the brain learning mechanism.

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“…Changes in the number of synaptic connections have been shown to be critical during learning in vivo (Bailey and Chen, 1989;Caroni et al, 2012;Hofer et al, 2009;Kozorovitskiy et al, 2012;Ruediger et al, 2011) and strongly determine postsynaptic function (Scholl et al, 2020). Plasticity of GABAergic synapses is particularly important for shaping and controlling brain activity throughout life (Chiu et al, 2019;Flores and Méndez, 2014;Herstel and Wierenga, 2021;Maffei et al, 2017) and GABAergic dysfunction is associated with multiple brain disorders, including schizophrenia and autism (Lewis et al, 2005;Mullins et al, 2016;Tang et al, 2021). For example, the number of inhibitory synapses is rapidly adjusted during learning (Bourne and Harris, 2011;Chen et al, 2015;Donato et al, 2015Donato et al, , 2013 or when sensory input is lost (Keck et al, 2011) to facilitate plasticity at nearby excitatory synapses.…”

Section: Introductionmentioning

confidence: 99%

Axonal CB1 receptors mediate inhibitory bouton formation via cAMP increase

Liang

Kruijssen

Verschuuren

et al. 2021

Preprint

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Experience-dependent formation and removal of synapses are essential throughout life. For instance, GABAergic synapses are removed to facilitate learning, and strong excitatory activity is accompanied by formation of inhibitory synapses. We recently discovered that active dendrites trigger the growth of inhibitory synapses via CB1 receptor-mediated endocannabinoid signaling, but the underlying mechanism remained unclear. Using two-photon microscopy to monitor the formation of individual inhibitory boutons, we found that CB1 receptor activation mediated formation of inhibitory boutons and promoted their subsequent stabilization. Inhibitory bouton formation did not require neuronal activity and was independent of Gi/o protein signaling, but was directly induced by elevating cAMP levels using forskolin and by activating Gs proteins using DREADDs. Our findings reveal that axonal CB1 receptors signal via unconventional downstream pathways and that inhibitory bouton formation is triggered by an increase in axonal cAMP levels. Our results demonstrate a novel role for axonal CB1 receptors in axon-specific, and context-dependent, inhibitory synapse formation.

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Network control through coordinated inhibition

Cited by 32 publications

References 82 publications

Augmenting Flexibility: Mutual Inhibition Between Inhibitory Neurons Expands Functional Diversity

Augmenting Flexibility: Mutual Inhibition Between Inhibitory Neurons Expands Functional Diversity

Evolutionary learning in the brain by heterosynaptic plasticity

Axonal CB1 receptors mediate inhibitory bouton formation via cAMP increase

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