When do microcircuits produce beyond-pairwise correlations?

Barreiro, Andrea K.; Gjorgjieva, Julijana; Rieke, Fred; Shea‐Brown, Eric

doi:10.3389/fncom.2014.00010

Cited by 26 publications

(32 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…HOCs 42 have been experimentally measured in several brain areas [55], and shown to account for a substantial amount 43 of information transfer in sensory cortex [55][56][57][58]. HOCs are also important for characterizing the firing of a 44 postsynaptic neuron [59,60], circuit function and coding [61,62], and the synchronous firing and the distribution 45 of activity in a neuronal pool [63][64][65][66]. Here we investigated the functional significance of such HOCs for shaping 46 recurrent network structure through synaptic plasticity.…”

mentioning

confidence: 99%

Autonomous emergence of connectivity assemblies via spike triplet interactions

Montangie

Gjorgjieva

2019

Preprint

Self Cite

View full text Add to dashboard Cite

AbstractNon-random connectivity can emerge without structured external input driven by activity-dependent mechanisms of synaptic plasticity based on precise spiking patterns. Here we analyze the emergence of global structures in recurrent networks based on a triplet model of spike timing dependent plasticity (STDP) which depends on the interactions of three precisely-timed spikes and can describe plasticity experiments with varying spike frequency better than the classical pair-based STDP rule. We describe synaptic changes arising from emergent higher-order correlations, and investigate their influence on different connectivity motifs in the network. Our motif expansion framework reveals novel motif structures under the triplet STDP rule, which support the formation of bidirectional connections and loops in contrast to the classical pair-based STDP rule. Therefore, triplet STDP drives the spontaneous emergence of self-connected groups of neurons, or assemblies, proposed to represent functional units in neural circuits. Assembly formation has often been associated with plasticity driven by firing rates or external stimuli. We propose that assembly structure can emerge without the need for externally patterned inputs or assuming a symmetric pair-based STDP rule commonly assumed in previous studies. The emergence of non-random network structure under triplet STDP occurs through internally-generated higher-order correlations, which are ubiquitous in natural stimuli and neuronal spiking activity, and important for coding. We further demonstrate how neuromodulatory mechanisms that modulate the shape of triplet STDP or the synaptic transmission function differentially promote connectivity motifs underlying the emergence of assemblies, and quantify the differences using graph theoretic measures.

show abstract

mentioning

confidence: 99%

Autonomous emergence of connectivity assemblies via spike triplet interactions

Montangie

Gjorgjieva

2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, it is speculated that common inhibitory inputs can also generate the same neural interactions (Ohiorhenuan and Victor, 2011;Shimazaki et al, 2015). The DG model based on the Gaussian approximation for common synaptic inputs (Amari et al, 2003;Leen and Shea-Brown, 2015;Macke et al, 2011;Montangie and Montani, 2015) as well as extended DG model for non-Gaussian inputs (Barreiro et al, 2014;Montani, 2017, 2018) do not incorporate the dynamics of synapses and membrane potentials, hence are not suitable for investigating types of synaptic inputs in different architectures. The aforementioned input-output relation for near threshold neurons (Shomali et al, 2018), on the other hand, does address dynamics of membrane potential using LIF neuron model; so it yields quantitative results with temporal accuracy, which let us quantitatively infer neural architecture and type of common input.…”

Section: Discussionmentioning

confidence: 99%

Uncovering Network Architecture Using an Exact Statistical Input-Output Relation of a Neuron Model

Shomali

Ahmadabadi

Rasuli

et al. 2018

Preprint

View full text Add to dashboard Cite

An appealing challenge in Neuroscience is to identify network architecture from neural activity. A key requirement is the knowledge of statistical input-output relation of single neurons in vivo. Using a recent exact solution of spike-timing for leaky integrate-and-fire neurons under noisy inputs balanced near threshold, we construct a unified framework that links synaptic inputs, spiking nonlinearity, and network architecture, with statistics of population activity. The framework predicts structured higher-order interactions of neurons receiving common inputs under different architectures: It unveils two network motifs behind sparse activity reported in visual neurons. Comparing model's prediction with monkey's V1 neurons, we found excitatory inputs to pairs explain the sparse activity characterized by negative triple-wise interactions, ruling out shared inhibition. While the model predicts variation in the structured activity according to local circuitries, we show strong negative interactions are in general a signature of excitatory inputs to neuron pairs, whenever background activity is sparse.1 Keywords network architecture, sparse activity, common inputs, higher-order interactions, leaky integrateand-fire neuron model, threshold regime, input-output relation, triple-wise interactions, spontaneous activity

show abstract

“…The key questions that these studies addressing are: (1) How are pairwise and higher order correlations generated in networks and which of them are important for a given network? and (2) How should we uncover and interpret spike train correlations in a given dataset?Four studies Zhou et al In a complementary study, Barreiro et al (2014) have focused on the emergence of pairwise and higher order correlations in retina models. The authors find that maximum entropy pairwise models capture surprisingly well the network spiking dynamics.…”

mentioning

confidence: 99%

“…Four studies Zhou et al In a complementary study, Barreiro et al (2014) have focused on the emergence of pairwise and higher order correlations in retina models. The authors find that maximum entropy pairwise models capture surprisingly well the network spiking dynamics.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Correlated neuronal activity and its relationship to coding, dynamics and network architecture

2014

Frontiers Research Topics

View full text Add to dashboard Cite

Correlated and synchronous activity in populations of neurons has been observed in many brain regions and has been shown to play a crucial role in cortical coding, attention, and network dynamics (Singer and Gray, 1995;Salinas and Sejnowski, 2001). However, we still lack a detailed knowledge of the origin and function, if any, of neuronal correlations. In this Research Topic, new ideas about these long standing questions are put forward. One group of studies in this Research Topic investigates the interaction of neuronal correlations with cellular and circuit mechanisms at the level of single neurons and cell pairs. Bolhasani et al. (2013) study the interaction between direct synaptic coupling between two neurons with correlated stochastic input to the neurons. They find that excitatory synaptic coupling can alter the transfer of pairwise correlations from current input to spike output. Interestingly, there is an optimal value of synaptic coupling strength for which the sensitivity of output correlations to input correlations is maximized.Bird and Richardson (2014) study the interaction between long term plasticity, synaptic vesicle depletion at multiple release sites and presynaptic spiking correlations. They find that there is an optimal number of release sites for driving postsynaptic spiking when synchrony is present in the presynaptic spike trains. Schwalger and Lindner (2013) investigated correlations between the interspike intervals of oscillator model neurons with adaptation. They reveal a fundamental connection between interval correlations and the phase response curve of the neuron model. They also show that when firing rates are high, negative interval correlations cause long-timescale variability of a model neuron's activity to be small.A second group of studies in this Research Topic investigates neuronal correlations on the level of networks. The key questions that these studies addressing are: (1) How are pairwise and higher order correlations generated in networks and which of them are important for a given network? and (2) How should we uncover and interpret spike train correlations in a given dataset?Four studies Zhou et al. In a complementary study, Barreiro et al. (2014) have focused on the emergence of pairwise and higher order correlations in retina models. The authors find that maximum entropy pairwise models capture surprisingly well the network spiking dynamics. What is surprising about these results is that higher-order correlations in this type of models can be constrained to be far lower than the statistically possible limits and that their strength depends more on the structure of the common input than on the synaptic connectivity profile. Jahnke et al. (2013) focused on spike patterns rather than correlations and proposed a mechanism for precise spike time pattern generation and replay in neural networks that lack strong densely connected feed-forward structures. The authors put forward the hypothesis that a non-linearity in synaptic summation rules may explain the lack of observed ...

show abstract

When do microcircuits produce beyond-pairwise correlations?

Cited by 26 publications

References 58 publications

Autonomous emergence of connectivity assemblies via spike triplet interactions

Autonomous emergence of connectivity assemblies via spike triplet interactions

Uncovering Network Architecture Using an Exact Statistical Input-Output Relation of a Neuron Model

Correlated neuronal activity and its relationship to coding, dynamics and network architecture

Contact Info

Product

Resources

About