Functional network stability and average minimal distance – A framework to rapidly assess dynamics of functional network representations

Wu, Jiaxing; Skilling, Quinton M.; Maruyama, Daniel; Li, Chenguang; Ognjanovski, Nicolette; Aton, Sara J.; Żochowski, Michał

doi:10.1016/j.jneumeth.2017.12.021

Cited by 10 publications

(32 citation statements)

References 52 publications

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“…To investigate how the emergent oscillations affects neurons' firing relationships, we calculated the stability of functional connectivity between neurons, using the functional network stability metric (FuNS; see Methods) [29]. First, in silico, we simulated networks with differential exposure to learning (i.e.…”

Section: Nrem Hippocampal Network Stabilization In Vivo Predicts Succmentioning

confidence: 99%

“…AMD calculates the mean value of the smallest temporal difference between all spikes in one neuron and all spikes in another neuron. Analytical calculations of the expected mean and standard deviation of minimal distance is then used to rapidly determine the significance of pairwise minimal distance [29]. Specifically, the first and second raw moments of minimal distance for each node are calculated:…”

Section: Analysis Of Functional Network Structures Through Amd and Funsmentioning

confidence: 99%

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Non-REM sleep facilitates consolidation of contextual fear memory through temporal coding among hippocampal neurons

Skilling

Clawson

Eniwaye

et al. 2020

Preprint

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Sleep plays a critical role in memory consolidation, although the exact mechanisms mediating this process are unknown. Combining computational and in vivo experimental approaches, we test the hypothesis that reduced cholinergic input to the hippocampus during non-rapid eye movement (NREM) sleep generates stable spike timing relationships between neurons. We find that the order of firing among neurons during a period of NREM sleep reflects their relative firing rates during prior wake, and changes as a function of prior learning. We show that learning-dependent pattern formation (e.g. "replay") in the hippocampus during NREM, together with spike timing dependent plasticity (STDP), restructures network activity in a manner similar to that observed in brain circuits across periods of sleep. This suggests that sleep actively promotes memory consolidation by switching the network from rate-based to firing phase-based information encoding.Oscillatory patterning of neuronal firing during sleep has been implicated in promoting synaptic plasticity and memory storage [12][13][14][15][16][17][18][19]. Network oscillations present in brain circuits during sleep have been implicated in promoting STDP by precisely timing the firing between pairs of neurons [14,15]. Hypothetically, the transition to oscillatory dynamics between wake and sleep could constitute a switch between rate coding (useful during learning) and phase coding (which may promote consolidation). For the reasons outlined above, this switch from rate to phase coding (i.e. when neurons' respective phase of firing on each cycle conveys the information about the network patterning) is essential for long-term information storage in brain networks.Here, we combine computational modeling of a highly reduced CA1 hippocampal network with analyses of in vivo recordings from CA1 to investigate how hippocampal network structure and dynamics are affected during contextual fear memory (CFM) consolidation following singletrial contextual fear conditioning (CFC) [20]. CFM consolidation relies on ad lib sleep in the hours immediately following CFC [17, 21] -a process associated with enhanced delta-(0.5-4 Hz), theta-(4-12 Hz), and ripple-frequency (150-200 Hz) hippocampal activity in the hours following learning

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Section: Nrem Hippocampal Network Stabilization In Vivo Predicts Succmentioning

confidence: 99%

Section: Analysis Of Functional Network Structures Through Amd and Funsmentioning

confidence: 99%

Non-REM sleep facilitates consolidation of contextual fear memory through temporal coding among hippocampal neurons

Skilling

Clawson

Eniwaye

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…AMD calculates the mean value of the smallest temporal difference between all spikes in one neuron and all spikes in another neuron. Analytical calculations of the expected mean and standard deviation of minimal distance is then used to rapidly determine the significance of pairwise minimal distance (24). Specifically, the first and second raw moments of minimal distance for each node are calculated: and , where L is the temporal…”

Section: Analysis Of Functional Network Structures Through Amd and Funsmentioning

confidence: 99%

“…length of the interspike interval and we have assumed that (looking both forward and backward in time) the maximum temporal distance between spikes is . Over the entire recording interval (24). The procedure is as follows: first, a recording interval is split into n partitions of equal temporal length.…”

Section: Analysis Of Functional Network Structures Through Amd and Funsmentioning

confidence: 99%

“…characteristic oscillations in the theta-band regime) might be sufficient to generate reliable sequences of spiking patterns; such oscillation-driven sequences are thought to play an important role in memory consolidation. To investigate this fully, we calculated the relative stability of network functional connectivity, using the functional network stability metric (FuNS; see Methods ) (24).…”

Section: Introduction Of Memory Traces Augments Network Oscillatory Dmentioning

confidence: 99%

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Network resonance during slow-wave sleep facilitates memory consolidation through phase-coding

Skilling

Clawson

Eniwaye

et al. 2019

Preprint

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Sleep plays a critical role in memory consolidation, however, the exact role that sleep and its effects on neural network dynamics play in this process is still unclear. Here, we combine computational and experimental approaches to study the dynamical, network-wide underpinnings of hippocampal memory consolidation during sleep. We provide data to support a novel hypothesis on the role of cellular resonance with sleep-associated theta band (4-12 Hz) hippocampal oscillations in this process. We show that increases in the stability of hippocampal memory representations after learning (which predicts successful memory consolidation) are mediated through emergent network-wide resonance and locking of neuronal activity to network oscillations. These changes arise in the network as a function of changes to network structure during learning, and mirror experimental findings in the hippocampus. Finally, we show that input-dependent pattern formation (e.g. "replay") in the hippocampus during sleep states, together with spike timing dependent plasticity (STDP)-based memory consolidation, leads to universal network activity reorganization. This reorganization generates heterogeneous changes in neuronal spiking frequency, similar to what has been observed in a variety of brain circuits across periods of sleep. Our results support the hypothesis that sleep plays an active role in memory consolidation by switching the hippocampal network from rate-based to phase-based information representation. The mechanisms through which this occurs supports the integration of heterogeneous cell populations into memory traces. 2Significance Statement : In this study, we provide a mechanistic explanation of how sleep selectively facilitates memory consolidation, through recruitment of heterogeneous neuronal populations and structural reorganization of the network into an engram. Specifically, we show that emergent theta band oscillations during sleep facilitate stabilization of memory representations via spike timing dependent reinforcement. This stabilization, together with STDP, allows for systematic reorganization of synaptic connections within these populations, universally redistributing firing rates of participating neurons. Simultaneously, network oscillations facilitate a switch from rate-to phase-coding of information among neuronal populations with highly heterogenous firing frequencies, incorporating more neurons into the engram. Our results reconcile discrepant findings on network reorganization during sleep, and demonstrate a clear mechanism for both strengthening and weakening of synaptic efficacy during sleep.

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Resonance with subthreshold oscillatory drive organizes activity and optimizes learning in neural networks

Roach

Pidde

Katz

et al. 2018

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

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SignificanceNetworks of neurons need to reliably encode and replay patterns and sequences of activity. In the brain, sequences of spatially coding neurons are replayed in both the forward and reverse direction in time with respect to their order in recent experience. As of yet there is no network-level or biophysical mechanism known that can produce both modes of replay within the same network. Here we propose that resonance, a property of neurons, paired with subthreshold oscillations in neural input facilitate network-level learning of fixed and sequential activity patterns and lead to both forward and reverse replay.

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Functional network stability and average minimal distance – A framework to rapidly assess dynamics of functional network representations

Abstract: The AMD-FuNS framework should be universally useful in linking long time-scale, global network dynamics and cognitive behavior.

Cited by 10 publications

References 52 publications

Non-REM sleep facilitates consolidation of contextual fear memory through temporal coding among hippocampal neurons

Non-REM sleep facilitates consolidation of contextual fear memory through temporal coding among hippocampal neurons

Network resonance during slow-wave sleep facilitates memory consolidation through phase-coding

Resonance with subthreshold oscillatory drive organizes activity and optimizes learning in neural networks

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