Gamma Oscillations Facilitate Effective Learning in Excitatory-Inhibitory Balanced Neural Circuits

Li, Kwan Tung; Liang, Junhao; Zhou, Changsong

doi:10.1155/2021/6668175

Cited by 13 publications

(9 citation statements)

References 70 publications

(68 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Indeed, it has been shown that E-I balanced networks with critical features found here (e.g., gamma oscillation and weak pairwise correlation) can achieve most efficient coding [65]. Such dynamic features are also beneficial to encode memory in networks through spike-timing-dependent plasticity [83].…”

Section: Neural Oscillation and Criticalitymentioning

confidence: 65%

Criticality enhances the multilevel reliability of stimulus responses in cortical neural networks

Liang

Zhou

2022

PLoS Comput Biol

Self Cite

View full text Add to dashboard Cite

Cortical neural networks exhibit high internal variability in spontaneous dynamic activities and they can robustly and reliably respond to external stimuli with multilevel features–from microscopic irregular spiking of neurons to macroscopic oscillatory local field potential. A comprehensive study integrating these multilevel features in spontaneous and stimulus–evoked dynamics with seemingly distinct mechanisms is still lacking. Here, we study the stimulus–response dynamics of biologically plausible excitation–inhibition (E–I) balanced networks. We confirm that networks around critical synchronous transition states can maintain strong internal variability but are sensitive to external stimuli. In this dynamical region, applying a stimulus to the network can reduce the trial-to-trial variability and shift the network oscillatory frequency while preserving the dynamical criticality. These multilevel features widely observed in different experiments cannot simultaneously occur in non-critical dynamical states. Furthermore, the dynamical mechanisms underlying these multilevel features are revealed using a semi-analytical mean-field theory that derives the macroscopic network field equations from the microscopic neuronal networks, enabling the analysis by nonlinear dynamics theory and linear noise approximation. The generic dynamical principle revealed here contributes to a more integrative understanding of neural systems and brain functions and incorporates multimodal and multilevel experimental observations. The E–I balanced neural network in combination with the effective mean-field theory can serve as a mechanistic modeling framework to study the multilevel neural dynamics underlying neural information and cognitive processes.

show abstract

Section: Neural Oscillation and Criticalitymentioning

confidence: 65%

Criticality enhances the multilevel reliability of stimulus responses in cortical neural networks

Liang

Zhou

2022

PLoS Comput Biol

Self Cite

View full text Add to dashboard Cite

show abstract

“…Gamma oscillations orchestrate activity synchronization across brain regions [10, 11], aiding memory retrieval [10, 11]. They align with the time constant of spike-timing-dependent plasticity (STDP) [45, 46], facilitating more effective synaptic modification, especially in the occurrence of spike-gamma coherence [16, 47]. These properties instantiate their importance in memory formation as well as the formation of cognitive maps, indicating the significance of gamma oscillations in encoding spatial memory.…”

Section: Discussionmentioning

confidence: 99%

Synfire Chain Dynamics Unravelling Theta-nested Gamma Oscillations for Balancing Prediction and Dodge in Navigation

Li,

Wei,

Gong

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Theta-nested gamma oscillations, widely observed in experiments, play a crucial role in navigation, yet their functional roles and the origin of the positive correlation between theta frequency and motion velocity remain unclear. We propose that the object’s survival relies on both prediction and dodge – predicting future events and staying alert to unpredictable ones, the latter of which has seldom been considered in goal-navigation tasks. By building a biologically plausible spiking neuronal network model and reproducing experimental results, we leverage synfire chain properties – length and separation – to elucidate the functional roles of theta-nested gamma oscillations: theta oscillations for self-location awareness, gamma oscillations for predictive capabilities and their coupling for enhancing functionality. The positive correlation between theta frequency and motion velocity is demonstrated to optimally balance representing predictable events for planning and staying alert to unexpected events. Our study offers a new avenue for unravelling the neural mechanisms of navigation.

show abstract

“…For example, the variability (CV) of the model is slightly larger than that observed in LFP; the broad peak in ERP and firing rate from monkey LFP do not exactly match in timing; the frequencies of Alpha oscillation in human EEG and monkey LFP do not match. Future studies can use such E–I balanced network as a core model with additional biological details to study more specific information processing associated to the multiple neural response features to external stimulus, such as learning and memory (Li et al 2021b ; Li et al 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Multilevel and multifaceted brain response features in spiking, ERP and ERD: experimental observation and simultaneous generation in a neuronal network model with excitation–inhibition balance

et al. 2022

View full text Add to dashboard Cite

Brain as a dynamic system responds to stimulations with specific patterns affected by its inherent ongoing dynamics. The patterns are manifested across different levels of organization—from spiking activity of neurons to collective oscillations in local field potential (LFP) and electroencephalogram (EEG). The multilevel and multifaceted response activities show patterns seemingly distinct and non-comparable from each other, but they should be coherently related because they are generated from the same underlying neural dynamic system. A coherent understanding of the interrelationships between different levels/aspects of activity features is important for understanding the complex brain functions. Here, based on analysis of data from human EEG, monkey LFP and neuronal spiking, we demonstrated that the brain response activities from different levels of neural system are highly coherent: the external stimulus simultaneously generated event-related potentials, event-related desynchronization, and variation in neuronal spiking activities that precisely match with each other in the temporal unfolding. Based on a biologically plausible but generic network of conductance-based integrate-and-fire excitatory and inhibitory neurons with dense connections, we showed that the multiple key features can be simultaneously produced at critical dynamical regimes supported by excitation–inhibition (E–I) balance. The elucidation of the inherent coherency of various neural response activities and demonstration of a simple dynamical neural circuit system having the ability to simultaneously produce multiple features suggest the plausibility of understanding high-level brain function and cognition from elementary and generic neuronal dynamics.

show abstract

Gamma Oscillations Facilitate Effective Learning in Excitatory-Inhibitory Balanced Neural Circuits

Cited by 13 publications

References 70 publications

Criticality enhances the multilevel reliability of stimulus responses in cortical neural networks

Criticality enhances the multilevel reliability of stimulus responses in cortical neural networks

Synfire Chain Dynamics Unravelling Theta-nested Gamma Oscillations for Balancing Prediction and Dodge in Navigation

Multilevel and multifaceted brain response features in spiking, ERP and ERD: experimental observation and simultaneous generation in a neuronal network model with excitation–inhibition balance

Contact Info

Product

Resources

About