Formation and Regulation of Dynamic Patterns in Two-Dimensional Spiking Neural Circuits with Spike-Timing-Dependent Plasticity

2013 ◽  
Vol 25 (11) ◽  
pp. 2833-2857 ◽  
Author(s):  
John H. C. Palmer ◽  
Pulin Gong
Keyword(s):  
Neural Circuits ◽  
Neural Circuit ◽  
Spike Timing ◽  
Fine Tuning ◽  
Regulation Mechanism ◽  
Two Dimensional ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Wave Patterns ◽  
Dynamic Patterns

Spike-timing-dependent plasticity (STDP) is an important synaptic dynamics that is capable of shaping the complex spatiotemporal activity of neural circuits. In this study, we examine the effects of STDP on the spatiotemporal patterns of a spatially extended, two-dimensional spiking neural circuit. We show that STDP can promote the formation of multiple, localized spiking wave patterns or multiple spike timing sequences in a broad parameter space of the neural circuit. Furthermore, we illustrate that the formation of these dynamic patterns is due to the interaction between the dynamics of ongoing patterns in the neural circuit and STDP. This interaction is analyzed by developing a simple model able to capture its essential dynamics, which give rise to symmetry breaking. This occurs in a fundamentally self-organizing manner, without fine-tuning of the system parameters. Moreover, we find that STDP provides a synaptic mechanism to learn the paths taken by spiking waves and modulate the dynamics of their interactions, enabling them to be regulated. This regulation mechanism has error-correcting properties. Our results therefore highlight the important roles played by STDP in facilitating the formation and regulation of spiking wave patterns that may have crucial functional roles in brain information processing.

scholarly journals Robust Rhythmogenesis in the Gamma Band via Spike Timing Dependent Plasticity

2020 ◽  
Author(s):  
Gabi Socolovsky ◽  
Maoz Shamir
Keyword(s):  
Mean Field ◽  
Rhythmic Activity ◽  
Gamma Oscillations ◽  
Underlying Mechanism ◽  
Spike Timing ◽  
Gamma Band ◽  
Fine Tuning ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Wide Range

Rhythmic activity in the gamma band (30-100Hz) has been observed in numerous animal species ranging from insects to humans, and in relation to a wide range of cognitive tasks. Various experimental and theoretical studies have investigated this rhythmic activity. The theoretical efforts have mainly been focused on the neuronal dynamics, under the assumption that network connectivity satisfies certain fine-tuning conditions required to generate gamma oscillations. However, it remains unclear how this fine tuning is achieved.Here we investigated the hypothesis that spike timing dependent plasticity (STDP) can provide the underlying mechanism for tuning synaptic connectivity to generate rhythmic activity in the gamma band. We addressed this question in a modeling study. We examined STDP dynamics in the framework of a network of excitatory and inhibitory neuronal populations that has been suggested to underlie the generation of gamma. Mean field Fokker Planck equations for the synaptic weights dynamics are derived in the limit of slow learning. We drew on this approximation to determine which types of STDP rules drive the system to exhibit gamma oscillations, and demonstrate how the parameters that characterize the plasticity rule govern the rhythmic activity. Finally, we propose a novel mechanism that can ensure the robustness of self-developing processes, in general and for rhythmogenesis in particular.

scholarly journals Enhancement of Synchronization in a Hybrid Neural Circuit by Spike-Timing Dependent Plasticity

2003 ◽  
Vol 23 (30) ◽  
pp. 9776-9785 ◽  
Author(s):  
Thomas Nowotny ◽  
Valentin P. Zhigulin ◽  
Allan I. Selverston ◽  
Henry D. I. Abarbanel ◽  
Mikhail I. Rabinovich
Keyword(s):  
Neural Circuit ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity

scholarly journals Detailed dendritic excitatory/inhibitory balance through heterosynaptic spike-timing-dependent plasticity

2016 ◽  
Author(s):  
Naoki Hiratani ◽  
Tomoki Fukai
Keyword(s):  
Computational Models ◽  
Neural Circuit ◽  
Detailed Balance ◽  
Experimental Studies ◽  
Spike Timing ◽  
Experimental Results ◽  
Dendritic Branch ◽  
Synaptic Organization ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity

AbstractBalance between excitatory and inhibitory inputs is a key feature of cortical dynamics. Such balance is arguably preserved in dendritic branches, yet its underlying mechanism and functional roles remain unknown. Here, by considering computational models of heterosynaptic spike-timing-dependent plasticity (STDP), we show that the detailed excitatory/inhibitory balance on dendritic branch is robustly achieved through heterosynaptic interaction between excitatory and inhibitory synapses. The model well reproduces experimental results on heterosynaptic STDP, and provides analytical insights. Furthermore, heterosynaptic STDP explains how maturation of inhibitory neurons modulates selectivity of excitatory neurons in critical period plasticity of binocular matching. Our results propose heterosynaptic STDP as a critical factor in synaptic organization and resultant dendritic computation.Significance statementRecent experimental studies have revealed that relative spike timings among neighboring Glutamatergic and GABAergic synapses on a dendritic branch significantly influences changes in synaptic efficiency of these synapses. This heterosynaptic form of spike-timing-dependent plasticity (STDP) is potentially important for shaping the synaptic organization and computation of neurons, but its functional role remains elusive. Here, through computational modeling, we show that heterosynaptic plasticity causes the detailed balance between excitatory and inhibitory inputs on the dendrite, at the parameter regime where previous experimental results are well reproduced. Our result reveals a potential principle of GABA-driven neural circuit formation.

scholarly journals Predictive visual motion extrapolation emerges spontaneously and without supervision from a layered neural network with spike-timing-dependent plasticity

2020 ◽  
Author(s):  
Anthony N. Burkitt ◽  
Hinze Hogendoorn
Keyword(s):  
Neural Network ◽  
Neural Circuits ◽  
Spike Timing ◽  
Moving Object ◽  
Present Moment ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Neural Populations ◽  
Neural Transmission ◽  
The Brain

AbstractThe fact that the transmission and processing of visual information in the brain takes time presents a problem for the accurate real-time localisation of a moving object. One way this problem might be solved is extrapolation: using an object’s past trajectory to predict its location in the present moment. Here, we investigate how a simulated in silico layered neural network might implement such extrapolation mechanisms, and how the necessary neural circuits might develop. We allowed an unsupervised hierarchical network of velocity-tuned neurons to learn its connectivity through spike-timing dependent plasticity. We show that the temporal contingencies between the different neural populations that are activated by an object as it moves causes the receptive fields of higher-level neurons to shift in the direction opposite to their preferred direction of motion. The result is that neural populations spontaneously start to represent moving objects as being further along their trajectory than where they were physically detected. Due to the inherent delays of neural transmission, this effectively compensates for (part of) those delays by bringing the represented position of a moving object closer to its instantaneous position in the world. Finally, we show that this model accurately predicts the pattern of perceptual mislocalisation that arises when human observers are required to localise a moving object relative to a flashed static object (the flash-lag effect).Significance StatementOur ability to track and respond to rapidly changing visual stimuli, such as a fast moving tennis ball, indicates that the brain is capable of extrapolating the trajectory of a moving object in order to predict its current position, despite the delays that result from neural transmission. Here we show how the neural circuits underlying this ability can be learned through spike-timing dependent synaptic plasticity, and that these circuits emerge spontaneously and without supervision. This demonstrates how the neural transmission delays can, in part, be compensated to implement the extrapolation mechanisms required to predict where a moving object is at the present moment.

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