A Lognormal Recurrent Network Model for Burst Generation during Hippocampal Sharp Waves

Omura, Yoshiyuki; Carvalho, Milena; Inokuchi, Kaoru; Fukai, Tomoki

doi:10.1523/jneurosci.4944-14.2015

Cited by 56 publications

(72 citation statements)

References 58 publications

(51 reference statements)

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“…On the one hand, this may have 569 important implications for information processing in these networks [12,13]. On the 570 other hand, this may also be important for network disorders such as epilepsy, where 571 such highly influential neurons are believed to play a key role in the initiation and 572 spreading of aberrant activity [67].…”

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confidence: 99%

Neural oligarchy: how synaptic plasticity breeds neurons with extreme influence

Kleberg

Triesch

2018

Preprint

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Synapses between cortical neurons are subject to constant modifications through synaptic plasticity mechanisms, which are believed to underlie learning and memory formation. The strengths of excitatory and inhibitory synapses in the cortex follow a right-skewed long-tailed distribution. Similarly, the firing rates of excitatory and inhibitory neurons also follow a right-skewed long-tailed distribution. How these distributions come about and how they maintain their shape over time is currently not well understood. Here we propose a spiking neural network model that explains the origin of these distributions as a consequence of the interaction of spike-timing dependent plasticity (STDP) of excitatory and inhibitory synapses and a multiplicative form of synaptic normalisation. Specifically, we show that the combination of additive STDP and multiplicative normalisation leads to lognormal-like distributions of excitatory and inhibitory synaptic efficacies as observed experimentally. The shape of these distributions remains stable even if spontaneous fluctuations of synaptic efficacies are added. In the same network, lognormal-like distributions of the firing rates of excitatory and inhibitory neurons result from small variability in the spiking thresholds of individual neurons. Interestingly, we find that variation in firing rates is strongly coupled to variation in synaptic efficacies: neurons with the highest firing rates develop very strong connections onto other neurons. Finally, we define an impact measure for individual neurons and demonstrate the existence of a small group of neurons with an exceptionally strong impact on the network, that arise as a result of synaptic plasticity. In summary, synaptic plasticity and small variability in neuronal parameters underlie a neural oligarchy in recurrent neural networks. Author summaryOur brain's neural networks are composed of billions of neurons that exchange signals via trillions of synapses. Are these neurons created equal, or do they contribute in similar ways to the network dynamics? Or do some neurons wield much more power than others? Recent experiments have shown that some neurons are much more active than the average neuron and that some synaptic connections are much stronger than the average synaptic connection. However, it is still unclear how these properties come about in the brain. Here we present a neural network model that explains these findings as a result of the interaction of synaptic plasticity mechanisms that modify synapses' efficacies. The model reproduces recent findings on the statistics of neuronal firing rates PLOS

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confidence: 99%

Neural oligarchy: how synaptic plasticity breeds neurons with extreme influence

Kleberg

Triesch

2018

Preprint

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“…A lognormal NB size distribution has been suggested as the origin of the experimentally observed lognormal SE size distribution in the modeling work of (Omura et al, ). In the sequel we point out that in our model, heavy‐tailed NB size distributions (of which lognormal is a special case) yield an unrealistic SE size distribution, which in particular predicts global CA1 activation with non‐negligible probability.…”

Section: Resultsmentioning

confidence: 96%

“…A model for spontaneous lognormal NB generation has been proposed in (Omura et al, ). There, it was shown that a recurrent network of bursting neurons interconnected by synapses with a lognormal weight distribution spontaneously emits NBs whose size distribution follows a lognormal distribution.…”

Section: Discussionmentioning

confidence: 99%

“…A model for spontaneous lognormal NB generation has been proposed in (Omura et al, 2015). There, it was shown that a recurrent network of bursting neurons interconnected by synapses with a lognormal weight distribution spontaneously emits NBs whose size distri- Fitting the mean μ X and the standard deviation σ X of normally distributed NB size X such that the mean squared error between the distribution of the resulting SE size Y and data depicted in (Buzsáki & Mizuseki, 2014, Figure 2B) is minimized Our model investigates synchronous spike transmission propagated over one synaptic layer.…”

Section: Related Computational Modelsmentioning

confidence: 99%

“…Further, fitting the parameters of a normal NB size distribution such that the resulting SE size distribution fits experimental data of (Buzsáki & Mizuseki, 2014) yields a prediction of NB size in particular. In addition, we study how a postulated lognormal NB size distribution (Omura, Carvalho, Inokuchi, & Fukai, 2015) affects the distribution of the SE size. Here, we find that the heavy tail of the NB size distribution yields an unrealistically heavy tail in the SE size distribution, in contradiction with experimental evidence (Buzsáki & Mizuseki, 2014).…”

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confidence: 99%

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On the origin of lognormal network synchrony in CA1

et al. 2018

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The sharp wave ripple complex in rodent hippocampus is associated with a network burst in CA3 (NB) that triggers a synchronous event in the CA1 population (SE). The number of CA1 pyramidal cells participating in a SE has been observed to follow a lognormal distribution. However, the origin of this skewed and heavy-tailed distribution of population synchrony in CA1 remains unknown. Because the size of SEs is likely to originate from the size of the NBs and the underlying neural circuitry, we model the CA3-CA1 circuit to study the underlying mechanisms and their functional implications. We show analytically that if the size of a NB in CA3 is distributed according to a normal distribution, then the size of the resulting SE in CA1 follows a lognormal distribution. Our model predicts the distribution of the NB size in CA3, which remains to be tested experimentally. Moreover, we show that a putative lognormal NB size distribution leads to an extremely heavy-tailed SE size distribution in CA1, contradicting experimental evidence. In conclusion, our model provides general insight on the origin of lognormally distributed network synchrony as a consequence of synchronous synaptic transmission of normally distributed input events.

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Hippocampal sharp wave‐ripple: A cognitive biomarker for episodic memory and planning

2015

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Sharp wave ripples (SPW-Rs) represent the most synchronous population pattern in the mammalian brain. Their excitatory output affects a wide area of the cortex and several subcortical nuclei. SPW-Rs occur during ‘off-line’ states of the brain, associated with consummatory behaviors and non-REM sleep, and are influenced by numerous neurotransmitters and neuromodulators. They arise from the excitatory recurrent system of the CA3 region and the SPW-induced excitation brings about a fast network oscillation (ripple) in CA1. The spike content of SPW-Rs is temporally and spatially coordinated by a consortium of interneurons to replay fragments of waking neuronal sequences in a compressed format. SPW-Rs assist in transferring this compressed hippocampal representation to distributed circuits to support memory consolidation; selective disruption of SPW-Rs interferes with memory. Recently acquired and pre-existing information are combined during SPW-R replay to influence decisions, plan actions and, potentially, allow for creative thoughts. In addition to the widely studied contribution to memory, SPW-Rs may also affect endocrine function via activation of hypothalamic circuits. Alteration of the physiological mechanisms supporting SPW-Rs leads to their pathological conversion, “p-ripples”, which are a marker of epileptogenic tissue and can be observed in rodent models of schizophrenia and Alzheimer’s Disease. Mechanisms for SPW-R genesis and function are discussed in this review.

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A Lognormal Recurrent Network Model for Burst Generation during Hippocampal Sharp Waves

Cited by 56 publications

References 58 publications

Neural oligarchy: how synaptic plasticity breeds neurons with extreme influence

Neural oligarchy: how synaptic plasticity breeds neurons with extreme influence

On the origin of lognormal network synchrony in CA1

Hippocampal sharp wave‐ripple: A cognitive biomarker for episodic memory and planning

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