Cyclic transitions between higher order motifs underlie sustained asynchronous spiking in sparse recurrent networks

Bojanek, Kyle; Zhu, Yuqing; MacLean, Jason N.

doi:10.1371/journal.pcbi.1007409

Cited by 22 publications

(23 citation statements)

References 54 publications

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“…SNNs were composed of both excitatory and inhibitory adapting exponential leaky integrate-and-fire neurons (AdEx) [27] connected with conductance-based synapses (Fig 1A; see methods). In previous work, we have shown that conductance-based synapses are crucial to accurately simulate neuronal integration of synaptic inputs—a critical consideration when evaluating structure-function hypotheses [8,28]. We conducted a grid search over a range of synaptic connectivity parameters defined by both excitatory and inhibitory connectivity and then quantified the outcome in SNN model behavior as connectivity parameter values changed (Fig 1B).…”

Section: Resultsmentioning

confidence: 99%

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Large-scale algorithmic search identifies stiff and sloppy dimensions in synaptic architectures consistent with murine neocortical wiring

Jabri

MacLean

2021

Preprint

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Complex systems can be defined by “sloppy” dimensions, meaning that their behavior is unmodified by large changes to specific parameter combinations, and “stiff” dimensions whose changes result in considerable modifications. In the case of the neocortex, sloppiness in synaptic architectures would be crucial to allow for the maintenance of spiking dynamics in the normal range despite a diversity of inputs and both short- and long-term changes to connectivity. Using simulations on neural networks with spiking matched to murine visual cortex, we determined the stiff and sloppy parameters of synaptic architectures across three classes of input (brief, continuous, and cyclical). Large-scale algorithmically-generated connectivity parameter values revealed that specific combinations of excitatory and inhibitory connectivity are stiff and that all other architectural details are sloppy. Stiff dimensions are consistent across a range of different input classes with self-sustaining synaptic architectures occupying a smaller subspace as compared to the other input classes. We also find that experimentally estimated connectivity probabilities from mouse visual cortex are similarly stiff and sloppy when compared to the architectures that we identified algorithmically. This suggests that simple statistical descriptions of spiking dynamics are a sufficient and parsimonious description of neocortical activity when examining structure-function relationships at the mesoscopic scale. Moreover, this study provides further evidence of the importance of the interrelationship of excitatory and inhibitory connectivity to establish and maintain stable spiking dynamical regimes in neocortex.Significance StatementConnections between neurons are continuously changing to allow learning and adaptation to new stimuli. However, the ability of neural networks to vary these connections while avoiding excessively high- or low-activity states is still not well understood. We tackled this question by studying how changes in the parameters of connectivity within and between different neuronal populations impacted network activity in computational models. We identified specific combinations of parameters, deemed “stiff”, that must be maintained to observe activity consistent with recordings from murine visual cortex, while the rest of the parameters can be varied freely with minimal effects on activity. Our results agree with experimentally measured connectivity statistics demonstrating the importance of balancing opposing forces to maintain activity in a natural regime.

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Section: Resultsmentioning

confidence: 99%

“…2A; see methods). In previous work, we have shown that conductance based synapses are crucial to accurately simulate neuronal integration of synaptic inputs-a critical consideration when evaluating structure-function hypotheses (Chambers and MacLean, 2016;Bojanek et al, 2020).…”

Section: Grid Search For Synaptic Architectures Producing Naturalistic Spikingmentioning

confidence: 99%

Large-scale algorithmic search identifies stiff and sloppy dimensions in synaptic architectures consistent with murine neocortical wiring

Jabri

MacLean

2021

Preprint

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“…While some directly utilize structural data to derive pairwise connections, others infer functional networks from spiking activity across channels using pairwise relations such as cross-correlation or Granger causality (e.g. Bojanek et al, 2020;Dechery & Maclean, 2018;Schneidman et al, 2006;Jiang et al, 2017). In contrast, our motifclasses are determined from the triple correlation of the spike raster itself using combinations of temporal and spatial lags (Equation ( 1)).…”

Section: Discussionmentioning

confidence: 99%

Third-order motifs are sufficient to fully and uniquely characterize spatiotemporal neural network activity

Deshpande

Smith

Drongelen

2021

Preprint

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We apply the Triple Correlation Uniqueness (TCU) theorem to the most basic representation of network spiking activity, the raster, to prove that third-order (triple) correlation uniquely characterizes spiking activity in the brain. As a consequence, the three-node (triple) motifs comprising the triple correlation are fundamental building blocks of neuronal activity. By analysing the putative flow of information in these motifs, we group all possible motifs into fourteen motif-classes. These motif-classes embody well-known and well-studied properties in neuroscience, e.g. spike rate, local dynamics, synchrony, feedback, feedforward, convergence, and divergence. We show that motifs with some of these properties---divergence, convergence, and feedforward---are far more prevalent in the triple correlation; and thus, these properties contribute more to characterizing neural activity than do the other properties. In the frequency domain, the triple correlation is the bispectrum, which characterizes the phase-relationship between spike trains and the relationships between frequency bands. We apply these analyses to example rasters to illustrate the dependence of our characterization on the firing patterns underlying neural activity.

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“…Dickinson (1998); Kusters et al (2007); Hashemi et al (2012) to the network level (e.g. Kriener et al (2014); Tomov et al (2016); Bojanek et al (2020); Borges et al (2020); B. Santos et al (2021)).…”

Section: Introductionmentioning

confidence: 99%

Active Role of Self-Sustained Neural Activity on Sensory Input Processing: A Minimal Theoretical Model

Santos¹,

Gomes²,

Barandiaran³

et al. 2022

Neural Computation

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A growing body of work has demonstrated the importance of ongoing oscillatory neural activity in sensory processing and the generation of sensorimotor behaviors. It has been shown, for several different brain areas, that sensory-evoked neural oscillations are generated from the modulation by sensory inputs of inherent self-sustained neural activity (SSA). This letter contributes to that strand of research by introducing a methodology to investigate how much of the sensory-evoked oscillatory activity is generated by SSA and how much is generated by sensory inputs within the context of sensorimotor behavior in a computational model. We develop an abstract model consisting of a network of three Kuramoto oscillators controlling the behavior of a simulated agent performing a categorical perception task. The effects of sensory inputs and SSAs on sensory-evoked oscillations are quantified by the cross product of velocity vectors in the phase space of the network under different conditions (disconnected without input, connected without input, and connected with input). We found that while the agent is carrying out the task, sensory-evoked activity is predominantly generated by SSA (93.10%) with much less influence from sensory inputs (6.90%). Furthermore, the influence of sensory inputs can be reduced by 10.4% (from 6.90% to 6.18%) with a decay in the agent's performance of only 2%. A dynamical analysis shows how sensory-evoked oscillations are generated from a dynamic coupling between the level of sensitivity of the network and the intensity of the input signals. This work may suggest interesting directions for neurophysiological experiments investigating how self-sustained neural activity influences sensory input processing, and ultimately affects behavior.

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Cyclic transitions between higher order motifs underlie sustained asynchronous spiking in sparse recurrent networks

Cited by 22 publications

References 54 publications

Large-scale algorithmic search identifies stiff and sloppy dimensions in synaptic architectures consistent with murine neocortical wiring

Large-scale algorithmic search identifies stiff and sloppy dimensions in synaptic architectures consistent with murine neocortical wiring

Third-order motifs are sufficient to fully and uniquely characterize spatiotemporal neural network activity

Active Role of Self-Sustained Neural Activity on Sensory Input Processing: A Minimal Theoretical Model

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