Robust development of synfire chains from multiple plasticity mechanisms

Zheng, Pengsheng; Triesch, Jochen

doi:10.3389/fncom.2014.00066

Cited by 45 publications

(42 citation statements)

References 40 publications

(50 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the synaptic plasticity rules included an effective form of self-depression. Thus, the spontaneous formation of synfire chains in [44] is consistent with the predictions of our work.…”

Section: Discussionsupporting

confidence: 92%

Shaping Neural Circuits by High Order Synaptic Interactions

2016

View full text Add to dashboard Cite

Spike timing dependent plasticity (STDP) is believed to play an important role in shaping the structure of neural circuits. Here we show that STDP generates effective interactions between synapses of different neurons, which were neglected in previous theoretical treatments, and can be described as a sum over contributions from structural motifs. These interactions can have a pivotal influence on the connectivity patterns that emerge under the influence of STDP. In particular, we consider two highly ordered forms of structure: wide synfire chains, in which groups of neurons project to each other sequentially, and self connected assemblies. We show that high order synaptic interactions can enable the formation of both structures, depending on the form of the STDP function and the time course of synaptic currents. Furthermore, within a certain regime of biophysical parameters, emergence of the ordered connectivity occurs robustly and autonomously in a stochastic network of spiking neurons, without a need to expose the neural network to structured inputs during learning.

show abstract

Section: Discussionsupporting

confidence: 92%

Shaping Neural Circuits by High Order Synaptic Interactions

2016

View full text Add to dashboard Cite

show abstract

“…Apart from the skewed weight distribution, it eventually also leads to a sequential activation of neurons as observed in neocortex [35], in modelling work [36] and in the current study (Fig. 8b).…”

Section: Resultssupporting

confidence: 63%

Where’s the noise? Key features of neuronal variability and inference emerge from self-organized learning

Hartmann

Lazar

Triesch

2014

Preprint

Self Cite

View full text Add to dashboard Cite

Trial-to-trial variability and spontaneous activity of cortical recordings have been suggested to reflect intrinsic noise. This view is currently challenged by mounting evidence for structure in these phenomena: Trial-to-trial variability decreases following stimulus onset and can be predicted by previous spontaneous activity. This spontaneous activity is similar in magnitude and structure to evoked activity and can predict decisions. Allof the observed neuronal properties described above can be accounted for, at an abstract computational level, by the sampling-hypothesis, according to which response variability reflects stimulus uncertainty. However, a mechanistic explanation at the level of neural circuit dynamics is still missing. In this study, we demonstrate that all of these phenomena can be accounted for by a noise-free self-organizing recurrent neural network model (SORN). It combines spike-timing dependent plasticity (STDP) and homeostatic mechanisms in a deterministic network of excitatory and inhibitory McCulloch-Pitts neurons. The network self-organizes to spatio-temporally varying input sequences. We find that the key properties of neural variability mentioned above develop in this model as the network learns to perform sampling-like inference. Importantly, the model shows high trial-to-trial variability although it is fully deterministic. This suggests that the trial-to-trial variability in neural recordings may not reflect intrinsic noise. Rather, it may reflect a deterministic approximation of sampling-like learning and inference. The simplicity of the model suggests that these correlates of the sampling theory are canonical properties of recurrent networks that learn with a combination of STDP and homeostatic plasticity mechanisms. Author Summary Neural recordings seem very noisy. If the exact same stimulus is shown to an animal multiple times, the neural response will vary. In fact, the activity of a single neuron shows many features of a stochastic process. Furthermore, in the absence of a sensory stimulus, cortical spontaneous activity has a magnitude comparable to the activity observed during stimulus presentation. These findings have led to a widespread belief that neural activity is indeed very noisy. However, recent evidence indicates that individual neurons can operate very reliably and that the spontaneous activity in the brain is highly structured, suggesting that much of the noise may in fact be signal. One hypothesis regarding this putative signal is that it reflects a form of probabilistic inference through sampling. Here we show that the key features of neural variability can be accounted for in a completely deterministic network model through self-organization. As the network learns a model of its sensory inputs, the deterministic dynamics give rise to sampling-like inference. Our findings show that the notorious variability in neural recordings does not need to be seen as evidence for a noisy brain. Instead it may reflect sampling-like inference emerging from a self-or...

show abstract

“…The exact rules governing activity-dependent structural changes, however, are still not understood. Computational models have tried to shed light on this issue by simulations, showing for example that many observed features of cortical connectivity could be achieved through the interaction of multiple plasticity mechanisms [66,40], and that homeostatic regulation of neuronal activity on multiple time scales is necessary in order to stabilize Hebbian changes [65,64]. Homeostatic plasticity, in this context, usually refers to a regulation of neuronal connectivity that result in stabilization of neuronal activity at a set point.…”

Section: Introductionmentioning

confidence: 99%

Associative properties of structural plasticity based on firing rate homeostasis in recurrent neuronal networks

Gallinaro¹,

Rotter²

2018

Sci Rep

View full text Add to dashboard Cite

Correlation-based Hebbian plasticity is thought to shape neuronal connectivity during development and learning, whereas homeostatic plasticity would stabilize network activity. Here we investigate another, new aspect of this dichotomy: Can Hebbian associative properties also emerge as a network effect from a plasticity rule based on homeostatic principles on the neuronal level? To address this question, we simulated a recurrent network of leaky integrate-and-fire neurons, in which excitatory connections are subject to a structural plasticity rule based on firing rate homeostasis. We show that a subgroup of neurons develop stronger within-group connectivity as a consequence of receiving stronger external stimulation. In an experimentally well-documented scenario we show that feature specific connectivity, similar to what has been observed in rodent visual cortex, can emerge from such a plasticity rule. The experience-dependent structural changes triggered by stimulation are long-lasting and decay only slowly when the neurons are exposed again to unspecific external inputs.

show abstract

Robust development of synfire chains from multiple plasticity mechanisms

Cited by 45 publications

References 40 publications

Shaping Neural Circuits by High Order Synaptic Interactions

Shaping Neural Circuits by High Order Synaptic Interactions

Where’s the noise? Key features of neuronal variability and inference emerge from self-organized learning

Associative properties of structural plasticity based on firing rate homeostasis in recurrent neuronal networks

Contact Info

Product

Resources

About