scholarly journals Nonlinear transient amplification in recurrent neural networks with short-term plasticity

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Yue Kris Wu ◽  
Friedemann Zenke
Keyword(s):  
Specific Activity ◽  
Feedback Inhibition ◽  
Activity Patterns ◽  
Network Models ◽  
Critical Threshold ◽  
Alternative Mechanism ◽  
Short Term ◽  
Short Term Plasticity ◽  
Nonlinear Transient ◽  
Rapid Amplification

To rapidly process information, neural circuits have to amplify specific activity patterns transiently. How the brain performs this nonlinear operation remains elusive. Hebbian assemblies are one possibility whereby strong recurrent excitatory connections boost neuronal activity. However, such Hebbian amplification is often associated with dynamical slowing of network dynamics, non-transient attractor states, and pathological run-away activity. Feedback inhibition can alleviate these effects but typically linearizes responses and reduces amplification gain. Here we study nonlinear transient amplification (NTA), a plausible alternative mechanism that reconciles strong recurrent excitation with rapid amplification while avoiding the above issues. NTA has two distinct temporal phases. Initially, positive feedback excitation selectively amplifies inputs that exceed a critical threshold. Subsequently, short-term plasticity quenches the run-away dynamics into an inhibition-stabilized network state. By characterizing NTA in supralinear network models, we establish that the resulting onset transients are stimulus selective and well-suited for speedy information processing. Further, we find that excitatory-inhibitory co-tuning widens the parameter regime in which NTA is possible in the absence of persistent activity. In summary, NTA provides a parsimonious explanation for how excitatory-inhibitory co-tuning and short-term plasticity collaborate in recurrent networks to achieve transient amplification.

scholarly journals Nonlinear transient amplification in recurrent neural networks with short-term plasticity

2021 ◽  
Author(s):  
Yue Kris Wu ◽  
Friedemann Zenke
Keyword(s):  
Specific Activity ◽  
Feedback Inhibition ◽  
Activity Patterns ◽  
Network Models ◽  
Critical Threshold ◽  
Short Term ◽  
Short Term Plasticity ◽  
Nonlinear Transient ◽  
Rapid Amplification ◽  
Nonlinear Operation

To rapidly process information, neural circuits have to amplify specific activity patterns transiently. How the brain performs this nonlinear operation remains elusive. Hebbian assemblies are one possibility whereby symmetric excitatory connections boost neuronal activity. However, such Hebbian amplification is often associated with dynamical slowing of network dynamics, non-transient attractor states, and pathological run-away activity. Feedback inhibition can alleviate these effects but typically linearizes responses and reduces amplification gain. At the same time, other alternative mechanisms rely on asymmetric connectivity, in conflict with the Hebbian doctrine. Here we propose nonlinear transient amplification (NTA), a plausible circuit mechanism that reconciles symmetric connectivity with rapid amplification while avoiding the above issues. NTA has two distinct temporal phases. Initially, positive feedback excitation selectively amplifies inputs that exceed a critical threshold. Subsequently, short-term plasticity quenches the run-away dynamics into an inhibition-stabilized network state. By characterizing NTA in supralinear network models, we establish that the resulting onset transients are stimulus selective and well-suited for speedy information processing. Further, we find that excitatory-inhibitory co-tuning widens the parameter regime in which NTA is possible. In summary, NTA provides a parsimonious explanation for how excitatory-inhibitory co-tuning and short-term plasticity collaborate in recurrent networks to achieve transient amplification.

scholarly journals Short-term plasticity in glomerular inhibitory circuits shapes olfactory bulb output

2020 ◽  
Vol 123 (3) ◽  
pp. 1120-1132 ◽  
Author(s):  
Fu-Wen Zhou ◽  
Zuo-Yi Shao ◽  
Michael T. Shipley ◽  
Adam C. Puche
Keyword(s):  
Fluorescent Protein ◽  
Feedback Inhibition ◽  
Olfactory Nerve ◽  
Inhibitory Synapses ◽  
Acid Decarboxylase ◽  
Short Term ◽  
Short Term Plasticity ◽  
Inhibitory Circuits ◽  
Frequency Independent ◽  
Green Fluorescent

Short-term plasticity is a fundamental synaptic property thought to underlie memory and neural processing. The glomerular microcircuit comprises complex excitatory and inhibitory interactions and transmits olfactory nerve signals to the excitatory output neurons, mitral/tufted cells (M/TCs). The major glomerular inhibitory interneurons, short axon cells (SACs) and periglomerular cells (PGCs), both provide feedforward and feedback inhibition to M/TCs and have reciprocal inhibitory synapses between each other. Olfactory input is episodically driven by sniffing. We hypothesized that frequency-dependent short-term plasticity within these inhibitory circuits could influence signals sent to higher-order olfactory networks. To assess short-term plasticity in glomerular circuits and MC outputs, we virally delivered channelrhodopsin-2 (ChR2) in glutamic acid decarboxylase-65 promotor (GAD2-cre) or tyrosine hydroxylase promoter (TH-cre) mice and selectively activated one of these two populations while recording from cells of the other population or from MCs. Selective activation of TH-ChR2-expressing SACs inhibited all recorded GAD2-green fluorescent protein(GFP)-expressing presumptive PGC cells, and activation of GAD2-ChR2 cells inhibited TH-GFP-expressing SACs, indicating reciprocal inhibitory connections. SAC synaptic inhibition of GAD2-expressing cells was significantly facilitated at 5–10 Hz activation frequencies. In contrast, GAD2-ChR2 cell inhibition of TH-expressing cells was activation-frequency independent. Both SAC and PGC inhibition of MCs also exhibited short-term plasticity, pronounced in the 5–20 Hz range corresponding to investigative sniffing frequency ranges. In paired SAC and olfactory nerve electrical stimulations, the SAC to MC synapse was able to markedly suppress MC spiking. These data suggest that short-term plasticity across investigative sniffing ranges may differentially regulate intra- and interglomerular inhibitory circuits to dynamically shape glomerular output signals to downstream targets. NEW & NOTEWORTHY Short-term plasticity is a fundamental synaptic property that modulates synaptic strength based on preceding activity of the synapse. In rodent olfaction, sensory input arrives episodically driven by sniffing rates ranging from quiescent respiration (1–2 Hz) through to investigative sniffing (5–10 Hz). Here we show that glomerular inhibitory networks are exquisitely sensitive to input frequencies and exhibit plasticity proportional to investigative sniffing frequencies. This indicates that olfactory glomerular circuits are dynamically modulated by episodic sniffing input.

scholarly journals Interneuron diversity is required for compartment-specific feedback inhibition

2020 ◽  
Author(s):  
Joram Keijser ◽  
Henning Sprekeler
Keyword(s):  
Functional Role ◽  
Feedback Inhibition ◽  
Inhibitory Interneurons ◽  
Optimization Approach ◽  
Short Term ◽  
Short Term Plasticity ◽  
Specific Feedback

AbstractCortical inhibitory interneurons consist of many subtypes that have been associated with different functions. Here we use an optimization approach to show that two classes of interneurons are necessary to implement compartment-specific feedback inhibition to pyramidal cells.The two classes resemble PV-expressing and SST-expressing interneurons in their connectivity and short-term plasticity, suggesting a functional role for their diverse characteristics.

scholarly journals Towards biologically constrained attractor models of schizophrenia

2021 ◽  
Author(s):  
Heike Stein ◽  
Joao Barbosa ◽  
Albert Compte
Keyword(s):  
Working Memory ◽  
Synaptic Transmission ◽  
Firing Rate ◽  
Experimental Evidence ◽  
Cognitive Deficits ◽  
Critical Points ◽  
Network Models ◽  
Short Term ◽  
Short Term Plasticity ◽  
Network Simulations

Alterations in neuromodulation or synaptic transmission in biophysical attractor network models, as proposed by the dominant dopaminergic and glutamatergic theories of schizophrenia, successfully mimic working memory (WM) deficits in people with schizophrenia (PSZ). Yet, multiple, often opposing circuit mechanisms can lead to the same behavioral patterns in these network models. Here, we critically revise the computational and experimental literature that links NMDAR hypofunction to WM precision loss in PSZ. We show in network simulations that currently available experimental evidence cannot set apart competing mechanistic accounts, and critical points to resolve are firing rate tuning and shared noise modulations by E/I ratio alterations through NMDAR blockade, and possible concomitant deficits in short-term plasticity. We argue that these concerted experimental and computational efforts will lead to a better understanding of the neurobiology underlying cognitive deficits in PSZ.

scholarly journals Inhibitory control of correlated intrinsic variability in cortical networks

2016 ◽  
Author(s):  
Carsen Stringer ◽  
Marius Pachitariu ◽  
Michael Okun ◽  
Peter Bartho ◽  
Kenneth Harris ◽  
...  
Keyword(s):  
Large Scale ◽  
Feedback Inhibition ◽  
Activity Patterns ◽  
Network Models ◽  
External Noise ◽  
Inhibitory Neurons ◽  
Computational Techniques ◽  
Model Parameters ◽  
Intrinsic Variability ◽  
Cortical Networks

AbstractCortical networks exhibit intrinsic dynamics that drive coordinated, large-scale fluctuations across neuronal populations and create noise correlations that impact sensory coding. To investigate the network-level mechanisms that underlie these dynamics, we developed novel computational techniques to fit a deterministic spiking network model directly to multi-neuron recordings from different species, sensory modalities, and behavioral states. The model generated correlated variability without external noise and accurately reproduced the wide variety of activity patterns in our recordings. Analysis of the model parameters suggested that differences in noise correlations across recordings were due primarily to differences in the strength of feedback inhibition. Further analysis of our recordings confirmed that putative inhibitory neurons were indeed more active during desynchronized cortical states with weak noise correlations. Our results demonstrate that network models with intrinsically-generated variability can accurately reproduce the activity patterns observed in multi-neuron recordings and suggest that inhibition modulates the interactions between intrinsic dynamics and sensory inputs to control the strength of noise correlations.

scholarly journals Inhibitory control of correlated intrinsic variability in cortical networks

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Carsen Stringer ◽  
Marius Pachitariu ◽  
Nicholas A Steinmetz ◽  
Michael Okun ◽  
Peter Bartho ◽  
...  
Keyword(s):  
Large Scale ◽  
Feedback Inhibition ◽  
Activity Patterns ◽  
Network Models ◽  
External Noise ◽  
Inhibitory Neurons ◽  
Computational Techniques ◽  
Model Parameters ◽  
Intrinsic Variability ◽  
Cortical Networks

Cortical networks exhibit intrinsic dynamics that drive coordinated, large-scale fluctuations across neuronal populations and create noise correlations that impact sensory coding. To investigate the network-level mechanisms that underlie these dynamics, we developed novel computational techniques to fit a deterministic spiking network model directly to multi-neuron recordings from different rodent species, sensory modalities, and behavioral states. The model generated correlated variability without external noise and accurately reproduced the diverse activity patterns in our recordings. Analysis of the model parameters suggested that differences in noise correlations across recordings were due primarily to differences in the strength of feedback inhibition. Further analysis of our recordings confirmed that putative inhibitory neurons were indeed more active during desynchronized cortical states with weak noise correlations. Our results demonstrate that network models with intrinsically-generated variability can accurately reproduce the activity patterns observed in multi-neuron recordings and suggest that inhibition modulates the interactions between intrinsic dynamics and sensory inputs to control the strength of noise correlations.

scholarly journals Disparate energy consumption despite similar network activity

2021 ◽  
Author(s):  
Michael Deistler ◽  
Jakob H Macke ◽  
Pedro J Goncalves
Keyword(s):  
Energy Consumption ◽  
Neural Circuits ◽  
Specific Activity ◽  
Activity Patterns ◽  
Network Models ◽  
Metabolic Cost ◽  
Network Activity ◽  
Metabolic Efficiency ◽  
Cancer Borealis ◽  
Similar Activity

Neural circuits can produce similar activity patterns from vastly different combinations of channel and synaptic conductances. These conductances are tuned for specific activity patterns but might also reflect additional constraints, such as metabolic cost or robustness to perturbations. How do such constraints influence the range of permissible conductances? Here, we investigate how metabolic cost affects the parameters of neural circuits with similar activity in a model of the pyloric network of the crab Cancer borealis. We use a novel machine learning method to identify a range of network models that can generate activity patterns matching experimental data. We find that neural circuits can consume largely different amounts of energy despite similar circuit activity. We then study how circuit parameters get constrained by minimizing energy consumption and identify circuit parameters that might be subject to metabolic tuning. Finally, we investigate the interaction between metabolic cost and temperature robustness. We show that metabolic cost can vary across temperatures, but that robustness to temperature changes does not necessarily incur an increased metabolic cost. Our analyses show that, despite metabolic efficiency and temperature robustness constraining circuit parameters, neural systems can generate functional, efficient, and robust network activity with widely disparate sets of conductances.

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