Autonomous emergence of connectivity assemblies via spike triplet interactions

Montangie, Lisandro; Gjorgjieva, Julijana

doi:10.1101/716001

Cited by 9 publications

(22 citation statements)

References 122 publications

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“…The triplet STDP rule describes synaptic plasticity based on triplets of spikes and captures experiments where the rate of pre- and postsynaptic neurons varies ( 58 ). The triplet STDP rule enables the formation of bidirectional connections, a necessity for the formation of clustered architectures ( 41 , 59 ). According to this rule, the synaptic strength from excitatory neuron

to excitatory neuron

follows:

Here,

and

are the amplitude of the weight change induced by a post–pre pair or a post–pre–post triplet of spikes.…”

Section: Methodsmentioning

confidence: 99%

Homeostatic mechanisms regulate distinct aspects of cortical circuit dynamics

Hengen

Turrigiano

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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Homeostasis is indispensable to counteract the destabilizing effects of Hebbian plasticity. Although it is commonly assumed that homeostasis modulates synaptic strength, membrane excitability, and firing rates, its role at the neural circuit and network level is unknown. Here, we identify changes in higher-order network properties of freely behaving rodents during prolonged visual deprivation. Strikingly, our data reveal that functional pairwise correlations and their structure are subject to homeostatic regulation. Using a computational model, we demonstrate that the interplay of different plasticity and homeostatic mechanisms can capture the initial drop and delayed recovery of firing rates and correlations observed experimentally. Moreover, our model indicates that synaptic scaling is crucial for the recovery of correlations and network structure, while intrinsic plasticity is essential for the rebound of firing rates, suggesting that synaptic scaling and intrinsic plasticity can serve distinct functions in homeostatically regulating network dynamics.

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to excitatory neuron

follows:

Here,

and

are the amplitude of the weight change induced by a post–pre pair or a post–pre–post triplet of spikes.…”

Section: Methodsmentioning

confidence: 99%

Homeostatic mechanisms regulate distinct aspects of cortical circuit dynamics

Hengen

Turrigiano

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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“…Since the memories are in our model stored in the synapses between excitatory neurons, only these are plastic. Previous theoretical work has shown that learning [15,20] or spontaneous emergence [16,19] of static assemblies as well as their maintenance can be enabled by a combination of STDP and homeostatic plasticity. A recent study investigated STDP in the recurrent excitatory synapses of the hippocampal region CA3 [26], i.e.…”

Section: Models and Approachesmentioning

confidence: 99%

“…For faithful memory storage the ensemble of neurons forming an assembly is assumed to remain the same [12]. Previous theoretical analysis has carefully studied the formation and maintenance of such static neuronal assemblies [13,14,15,16,17,18,19]. In particular, it has been suggested that in presence of noisy, irregular spiking activity [14,20,15,16] and spontaneous synaptic changes [18] assemblies are preserved with the help of activity dependent synaptic plasticity.…”

mentioning

confidence: 99%

Drifting Assemblies for Persistent Memory

Kossio

Goedeke

Klos

et al. 2020

Preprint

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Change is ubiquitous in living beings. In particular, the connectome and neural representations can change. Nevertheless behaviors and memories often persist over long times. In a standard model, memories are represented by assemblies of strongly interconnected neurons. For faithful storage these assemblies are assumed to consist of the same neurons over time. Here we propose a contrasting memory model with complete temporal remodeling of assemblies, based on experimentally observed changes of connections and neural representations. The assemblies drift freely as spontaneous synaptic turnover or random activity induce neuron exchange. The gradual exchange allows activity dependent and homeostatic plasticity to conserve the representational structure and keep inputs, outputs and assemblies consistent. This leads to persistent memory. Our findings explain recent experimental results on the temporal evolution of fear memory representations and suggest that memory systems need to be understood in their completeness as individual parts may constantly change.

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“…In particular, Ravid Tannenbaum et al showed that in networks of Poisson neurons synfire chains and self connected assemblies can emerge autonomously in recurrent networks [ 46 ]. Montangie et al showed that a more realistic form of STDP based on spike triplets also leads to autonomous emergence of assemblies [ 47 ].…”

Section: Introductionmentioning

confidence: 99%

Balanced networks under spike-time dependent plasticity

2021

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The dynamics of local cortical networks are irregular, but correlated. Dynamic excitatory–inhibitory balance is a plausible mechanism that generates such irregular activity, but it remains unclear how balance is achieved and maintained in plastic neural networks. In particular, it is not fully understood how plasticity induced changes in the network affect balance, and in turn, how correlated, balanced activity impacts learning. How do the dynamics of balanced networks change under different plasticity rules? How does correlated spiking activity in recurrent networks change the evolution of weights, their eventual magnitude, and structure across the network? To address these questions, we develop a theory of spike–timing dependent plasticity in balanced networks. We show that balance can be attained and maintained under plasticity–induced weight changes. We find that correlations in the input mildly affect the evolution of synaptic weights. Under certain plasticity rules, we find an emergence of correlations between firing rates and synaptic weights. Under these rules, synaptic weights converge to a stable manifold in weight space with their final configuration dependent on the initial state of the network. Lastly, we show that our framework can also describe the dynamics of plastic balanced networks when subsets of neurons receive targeted optogenetic input.

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Autonomous emergence of connectivity assemblies via spike triplet interactions

Cited by 9 publications

References 122 publications

Homeostatic mechanisms regulate distinct aspects of cortical circuit dynamics

Homeostatic mechanisms regulate distinct aspects of cortical circuit dynamics

Drifting Assemblies for Persistent Memory

Balanced networks under spike-time dependent plasticity

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