scholarly journals Scaling of spike-timing based neuron model for mammalian olfaction with network size

2014 ◽  
Vol 15 (Suppl 1) ◽  
pp. P90
Author(s):  
Bolun Chen ◽  
Jan R Engelbrecht ◽  
Renato Mirollo
Keyword(s):  
Neuron Model ◽  
Network Size ◽  
Spike Timing

scholarly journals Redundancy in synaptic connections enables neurons to learn optimally

2017 ◽  
Author(s):  
Naoki Hiratani ◽  
Tomoki Fukai
Keyword(s):  
Synaptic Plasticity ◽  
Particle Filtering ◽  
Neuron Model ◽  
Experimental Studies ◽  
Dendritic Tree ◽  
Spike Timing ◽  
Mammalian Brain ◽  
Synaptic Connections ◽  
Synaptic Organization ◽  
Functional Benefit

AbstractRecent experimental studies suggest that, in cortical microcircuits of the mammalian brain, the majority of neuron-to-neuron connections are realized by multiple synapses. However, it is not known whether such redundant synaptic connections provide any functional benefit. Here, we show that redundant synaptic connections enable near-optimal learning in cooperation with synaptic rewiring. By constructing a simple dendritic neuron model, we demonstrate that with multisynaptic connections, synaptic plasticity approximates a sample-based Bayesian filtering algorithm known as particle filtering, and wiring plasticity implements its resampling process. Applying the proposed framework to a detailed single neuron model, we show that the model accounts for many experimental observations, including the dendritic position dependence of spike-timing-dependent plasticity, and the functional synaptic organization on the dendritic tree based on the stimulus selectivity of presynaptic neurons. Our study provides a novel conceptual framework for synaptic plasticity and rewiring.

scholarly journals Modified STDP Triplet Rule Significantly Increases Neuron Training Stability in the Learning of Spatial Patterns

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Dalius Krunglevicius
Keyword(s):  
Spatial Patterns ◽  
Neuron Model ◽  
Nearest Neighbor ◽  
Hebbian Learning ◽  
Spike Timing ◽  
Point Of View ◽  
Neighbor Interaction ◽  
Training Performance ◽  
Learning Rules ◽  
Single Synapse

Spike-timing-dependent plasticity (STDP) is a set of Hebbian learning rules which are based firmly on biological evidence. STDP learning is capable of detecting spatiotemporal patterns highly obscured by noise. This feature appears attractive from the point of view of machine learning. In this paper three different additive STDP models of spike interactions were compared in respect to training performance when the neuron is exposed to a recurrent spatial pattern injected into Poisson noise. The STDP models compared were all-to-all interaction, nearest-neighbor interaction, and the nearest-neighbor triplet interaction. The parameters of the neuron model and STDP training rules were optimized for a range of spatial patterns of different sizes by the means of heuristic algorithm. The size of the pattern, that is, the number of synapses containing the pattern, was gradually decreased from what amounted to a relatively easy task down to a single synapse. Optimization was performed for each size of the pattern. The parameters were allowed to evolve freely. The triplet rule, in most cases, performed better by far than the other two rules, while the evolutionary algorithm immediately switched the polarity of the triplet update. The all-to-all rule achieved moderate results.

Effect of Spike-Timing-Dependent Plasticity on Intrinsic Coherence Resonance in Newman–Watts Stochastic Hodgkin–Huxley Neuronal Networks

2016 ◽  
Vol 15 (04) ◽  
pp. 1650027 ◽  
Author(s):  
Huijuan Xie ◽  
Yubing Gong ◽  
Qi Wang
Keyword(s):  
Neuronal Networks ◽  
Electrical Coupling ◽  
Network Size ◽  
Spike Timing ◽  
Channel Noise ◽  
Coherence Resonance ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Optimal Network ◽  
Chemical Coupling

In this paper, we numerically study the effect of spike-timing-dependent plasticity (STDP) on coherence resonance (CR) induced by channel noise in adaptive Newman–Watts stochastic Hodgkin–Huxley neuron networks. It is found that STDP can either enhance or suppress the intrinsic CR when the adjusting rate of STDP decreases or increases. STDP can alter the effects of network randomness and network size on the intrinsic CR. Under STDP, for electrical coupling there are optimal network randomness and network size by which the intrinsic CR becomes strongest, however, for chemical coupling the intrinsic CR is always enhanced as network randomness or network size increases, which are different from the results for fixed coupling. These results show that the intrinsic CR of the neuronal networks can be either enhanced or suppressed by STDP, and there are optimal network randomness and network size by which the intrinsic CR becomes strongest. These findings could provide a new insight into the role of STDP for the information processing and transmission in neural systems.

scholarly journals Redundancy in synaptic connections enables neurons to learn optimally

2018 ◽  
Vol 115 (29) ◽  
pp. E6871-E6879 ◽  
Author(s):  
Naoki Hiratani ◽  
Tomoki Fukai
Keyword(s):  
Synaptic Plasticity ◽  
Particle Filtering ◽  
Neuron Model ◽  
Experimental Studies ◽  
Dendritic Tree ◽  
Spike Timing ◽  
Mammalian Brain ◽  
Synaptic Connections ◽  
Synaptic Organization ◽  
Functional Benefit

Recent experimental studies suggest that, in cortical microcircuits of the mammalian brain, the majority of neuron-to-neuron connections are realized by multiple synapses. However, it is not known whether such redundant synaptic connections provide any functional benefit. Here, we show that redundant synaptic connections enable near-optimal learning in cooperation with synaptic rewiring. By constructing a simple dendritic neuron model, we demonstrate that with multisynaptic connections synaptic plasticity approximates a sample-based Bayesian filtering algorithm known as particle filtering, and wiring plasticity implements its resampling process. Extending the proposed framework to a detailed single-neuron model of perceptual learning in the primary visual cortex, we show that the model accounts for many experimental observations. In particular, the proposed model reproduces the dendritic position dependence of spike-timing-dependent plasticity and the functional synaptic organization on the dendritic tree based on the stimulus selectivity of presynaptic neurons. Our study provides a conceptual framework for synaptic plasticity and rewiring.

scholarly journals Does the Zebra Finch Mating Song Circuit Use Spike Times Efficiently?

2021 ◽  
Author(s):  
Wilten Nicola ◽  
Claudia Clopath ◽  
Thomas Robert Newton
Keyword(s):  
Zebra Finch ◽  
Neural Circuits ◽  
Computational Modelling ◽  
Network Size ◽  
Spike Timing ◽  
Rate Code ◽  
Synfire Chain ◽  
Coding Schemes ◽  
Scaling Relationship ◽  
Efficient Code

Precise and reliable spike times are thought to subserve multiple possible functions, including improving the accuracy of encoding stimuli or behaviours relative to other coding schemes. Indeed, repeating sequences of spikes with sub-millisecond precision exist in nature, such as the synfire chain of spikes in area HVC of the zebra-finch mating-song circuit. Here, we analyzed what impact precise and reliable spikes have on the encoding accuracy for both the zebra-finch and more generic neural circuits using computational modelling. Our results show that neural circuits can use precisely timed spikes to encode signals with a higher-order accuracy than a conventional rate code. Circuits with precisely timed and reliably emitted spikes increase their encoding accuracy linearly with network size, which is the hallmark signature of an efficient code. This qualitatively differs from circuits that employ a rate code which increase their encoding accuracy with the square-root of network size. However, this improved scaling is dependent on the spikes becoming more accurate and more reliable with larger networks. Finally, we discuss how to test this scaling relationship in the zebra mating song circuit using both neural data and song-spectrogram-based recordings while taking advantage of the natural fluctuation in HVC network size due to neurogenesis. The zebra-finch mating-song circuit may represent the most likely candidate system for the use of spike-timing-based, efficient coding strategies in nature.

scholarly journals Spike-timing prediction in a neuron model with active dendrites

2009 ◽  
Vol 10 (S1) ◽  
Author(s):  
Richard Naud ◽  
Brice Bathellier ◽  
Wulfram Gerstner
Keyword(s):  
Neuron Model ◽  
Spike Timing ◽  
Active Dendrites

scholarly journals To the role of the choice of the neuron model in spiking network learning on base of Spike-Timing-Dependent Plasticity

2018 ◽  
Vol 123 ◽  
pp. 432-439 ◽  
Author(s):  
Alexander Sboev ◽  
Roman Rybka ◽  
Alexey Serenko ◽  
Danila Vlasov ◽  
Nikolay Kudryashov ◽  
...  
Keyword(s):  
Neuron Model ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Network Learning ◽  
Spiking Network

Mirror Neurons Modeled Through Spike-Timing-Dependent Plasticity are Affected by Channelopathies Associated with Autism Spectrum Disorder

2018 ◽  
Vol 28 (05) ◽  
pp. 1750058 ◽  
Author(s):  
Gabriela Antunes ◽  
Samuel F. Faria da Silva ◽  
Fabio M. Simoes de Souza
Keyword(s):  
Autism Spectrum Disorder ◽  
Mirror Neurons ◽  
Neuron Model ◽  
Mirror Neuron ◽  
Autism Spectrum ◽  
Spike Timing ◽  
Spectrum Disorder ◽  
Neocortical Neurons ◽  
Mirror Function ◽  
Simulation Results

Mirror neurons fire action potentials both when the agent performs a certain behavior and watches someone performing a similar action. Here, we present an original mirror neuron model based on the spike-timing-dependent plasticity (STDP) between two morpho-electrical models of neocortical pyramidal neurons. Both neurons fired spontaneously with basal firing rate that follows a Poisson distribution, and the STDP between them was modeled by the triplet algorithm. Our simulation results demonstrated that STDP is sufficient for the rise of mirror neuron function between the pairs of neocortical neurons. This is a proof of concept that pairs of neocortical neurons associating sensory inputs to motor outputs could operate like mirror neurons. In addition, we used the mirror neuron model to investigate whether channelopathies associated with autism spectrum disorder could impair the modeled mirror function. Our simulation results showed that impaired hyperpolarization-activated cationic currents (Ih) affected the mirror function between the pairs of neocortical neurons coupled by STDP.

Extraverts Have Larger Social Network Layers

2011 ◽  
Vol 32 (3) ◽  
pp. 161-169 ◽  
Author(s):  
Thomas V. Pollet ◽  
Sam G. B. Roberts ◽  
Robin I. M. Dunbar
Keyword(s):  
Social Network ◽  
Social Relationships ◽  
Outer Layer ◽  
Network Size ◽  
Emotional Intensity ◽  
Emotional Closeness ◽  
Network Layer ◽  
Network Layers ◽  
Social Network Size

Previous studies showed that extraversion influences social network size. However, it is unclear how extraversion affects the size of different layers of the network, and how extraversion relates to the emotional intensity of social relationships. We examined the relationships between extraversion, network size, and emotional closeness for 117 individuals. The results demonstrated that extraverts had larger networks at every layer (support clique, sympathy group, outer layer). The results were robust and were not attributable to potential confounds such as sex, though they were modest in size (raw correlations between extraversion and size of network layer, .20 < r < .23). However, extraverts were not emotionally closer to individuals in their network, even after controlling for network size. These results highlight the importance of considering not just social network size in relation to personality, but also the quality of relationships with network members.

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