spike timing dependent plasticity

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Spike Timing Dependent Plasticity

Author : Henry Markram
ISBN : 9782889190430
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Hebb's postulate provided a crucial framework to understand synaptic alterations underlying learning and memory. Hebb's theory proposed that neurons that fire together, also wire together, which provided the logical framework for the strengthening of synapses. Weakening of synapses was however addressed by "not being strengthened", and it was only later that the active decrease of synaptic strength was introduced through the discovery of long-term depression caused by low frequency stimulation of the presynaptic neuron. In 1994, it was found that the precise relative timing of pre and postynaptic spikes determined not only the magnitude, but also the direction of synaptic alterations when two neurons are active together. Neurons that fire together may therefore not necessarily wire together if the precise timing of the spikes involved are not tighly correlated. In the subsequent 15 years, Spike Timing Dependent Plasticity (STDP) has been found in multiple brain brain regions and in many different species. The size and shape of the time windows in which positive and negative changes can be made vary for different brain regions, but the core principle of spike timing dependent changes remain. A large number of theoretical studies have also been conducted during this period that explore the computational function of this driving principle and STDP algorithms have become the main learning algorithm when modeling neural networks. This Research Topic will bring together all the key experimental and theoretical research on STDP.

Supervised Spike Timing Dependent Plasticity Bei Der Beuteortung Des Krallenfrosches

Author : Audrey Houillon
ISBN : OCLC:633374455
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Origin Of The Spike Timing Dependent Plasticity Rule

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ISBN : OCLC:1051931477
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Abstract: A biological synapse changes its efficacy depending on the difference between pre- and post-synaptic spike timings. Formulating spike-timing–dependent interactions in terms of the path integral, we establish a neural-network model, which makes it possible to predict relevant quantities rigorously by means of standard methods in statistical mechanics and field theory. In particular, the biological synaptic plasticity rule is shown to emerge as the optimal form for minimizing the free energy. It is further revealed that maximization of the entropy of neural activities gives rise to the competitive behavior of biological learning. This demonstrates that statistical mechanics helps to understand rigorously key characteristic behaviors of a neural network, thus providing the possibility of physics serving as a useful and relevant framework for probing life.

Network Structures Arising From Spike Timing Dependent Plasticity

Author : Baktash Babadi
ISBN : OCLC:1004785813
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By analyzing pairwise interactions of neurons through STDP and also numerical simulations of a large network, I show that conventional pair-based STDP functions as a loop-eliminating mechanism in a network of spiking neurons and organizes neurons into in- and out-hubs. Loop-elimination increases when depression dominates and decreases when potentiation dominates. STDP with dominant depression implements a buffering mechanism for network firing rates, and shifted STDP can generate recurrent connections in a network, and also functions as a homeostatic mechanism that maintains a roughly constant average value of the synaptic strengths. In conclusion, studying pairwise interactions of neurons through STDP provides a number of important insights about the structures that arise from this plasticity rule in large networks. This approach can be extended to networks with more complex STDP models and more structured external input.

Spike Timing Dependent Plasticity And Backpropagating Action Potentials Microform A Modeling Study

Author : Acker, Corey Derek
ISBN : OCLC:297107787
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Abstract: Advances in experimental techniques have led to a recent explosion in knowledge of the properties and function of neuronal dendrites. Much new research has focused on dendritic action potentials, which are thought to play an important role in many cellular functions, including synaptic plasticity. In particular, dendritic action potentials are thought to play an important role in the recently discovered form of long-term synaptic plasticity called spike timing-dependent plasticity (STDP). Action potentials that propagate back into the dendrites are thought to influence plasticity machinery located at synapses. In this dissertation, I use computational and theoretical techniques to better understand propagation of dendritic action potentials and to uncover the underlying mechanisms responsible for the STDP phenomenon.

Exact Ensemble Dynamics For Spike Timing Dependent Plasticity

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ISBN : OCLC:648295398
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M.S.

Spike Timing Dependent Plasticity Optimized Coherence Resonance And Synchronization Transitions By Autaptic Delay In Adaptive Scale Free Neuronal Networks

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ISBN : OCLC:1032207351
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Highlights: STDP can optimize MCR and ST by autaptic delay in the adaptive scale-free neuronal networks. Effect of network average degree on ST by autaptic delay changes as Ap is increased. MCR and ST by autaptic delay are robust to network size in the presence of STDP. Abstract: In this paper, we numerically study the effect of spike-timing-dependent plasticity on multiple coherence resonance and synchronization transitions induced by autaptic time delay in adaptive scale-free Hodgkin–Huxley neuron networks. As the adjusting rate Ap of spike-timing-dependent plasticity increases, multiple coherence resonance and synchronization transitions enhance and become strongest at an intermediate Ap value, indicating that there is optimal spike-timing-dependent plasticity that can most strongly enhance the multiple coherence resonance and synchronization transitions. As Ap increases, increasing network average degree has a small effect on multiple coherence resonance, but its effect on synchronization transitions changes from suppressing to enhancing it. As network size is varied, multiple coherence resonance and synchronization transitions nearly do not change. These results show that spike-timing-dependent plasticity can simultaneously optimize multiple coherence resonance and synchronization transitions by autaptic delay in the adaptive scale-free neuronal networks. These findings provide a new insight into spike-timing-dependent plasticity and autaptic delay for the information processing and transmission in neural systems.

Developmental Control Of Spike Timing Dependent Plasticity By Tonic Gabaergic Signaling In Striatum

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ISBN : OCLC:1052032829
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Abstract: Activity-dependent long-term potentiation (LTP) and depression (LTD) of synaptic strength underlie multiple forms of learning and memory. Spike-timing-dependent plasticity (STDP) has been described as a Hebbian synaptic learning rule that could account for experience-dependent changes in neural networks, but little is known about whether and how STDP evolves during development. We previously showed that GABAergic signaling governs STDP polarity and thus operates as a Hebbian/anti-Hebbian switch in the striatum. Although GABAergic networks are subject to important developmental maturation, it remains unclear whether STDP is developmentally shaped by GABAergic signaling. Here, we investigated whether STDP rules are developmentally regulated at corticostriatal synapses in the dorsolateral striatum. We found that striatal STDP displays unidirectional plasticity (Hebbian tLTD) in young rats (P7-10 ) whereas STDP is bidirectional and anti-Hebbian in juvenile (P20-25 ) and adult (P60-90 ) rats. We also provide evidence that the appearance of tonic (extrasynaptic) GABAergic signaling from the juvenile stage is a crucial factor in shaping STDP rules during development, establishing bidirectional anti-Hebbian STDP in the adult striatum. Thus, developmental maturation of GABAergic signaling tightly drives the polarity of striatal plasticity. Graphical abstract: Highlights: Striatal STDP is developmentally regulated. Blockade of GABAA R signaling promotes Hebbian STDP, regardless of the developmental stage. Tonic GABAergic signaling is crucial in shaping STDP during development.

Theory Of Synaptic Spike Timing Dependent Plasticity

Author : Yotam Luz
ISBN : OCLC:951799932
Genre : Developmental neurophysiology
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The Many Faces Of Corticostriatal Spike Timing Dependent Plasticity

Author : Yihui Cui
ISBN : OCLC:875439355
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The corticostriatal plasticity is thought to be the neuronal substrate of procedural learning. We first investigated non-hebbian plasticity and found that both depolarization-induced suppression of excitation (DSE) and low-frequency stimulation (LFS) protocols induced LTD and are both mediated by endocannabinoid (eCB)-signaling. We then focused on corticostriatal spike timing-dependent plasticity (STDP) characterization and robustness. We found that with 100 STDP pairings, corticostriatal LTP was NMDA-dependent while LTD involved eCB-signaling. We then tested the robustness of corticostriatal STDP. We uncovered that LTP was even inducible with 5 pairings. Thanks to a model-driven experiment strategy, we demonstrated that this LTP relies on eCB-signaling. This eCB-LTP is homosynaptic, depends on cannabinoid-type-1 receptor (CB1R) and transient receptor potential vanilloid-type-1 (TRPV1) activation and is supported by presynaptic PKA and calcineurin. Our results considerably enlarge the spectrum of action of eCBs since they show that eCBs promote not only depression but also potentiation. To investigate the limits of corticostriatal STDP, we varied the STDP rate. We observed a transition from timing- to rate-dependent plasticity. This rate-dependency exists with both 100 and 10 pairings, in which LTP is respectively NMDA-dependent and CB1 and NMDA receptors. We then applied a randomized jitter within STDP protocol. We showed that NMDA-LTP is highly sensitive to jitter while eCB-LTP is not. These results showed novel forms of corticostriatal plasticity and demonstrated that the multiple learning rules at play for governing the corticostriatal information processing.

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