Transient information flow in a network of excitatory and inhibitory model neurons: Role of noise and signal autocorrelation

Mayor, Julien; Gerstner, Wulfram

doi:10.1016/j.jphysparis.2005.09.009

Cited by 5 publications

(2 citation statements)

References 66 publications

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“…Therefore, any potential phase information benefit is lost. Two broad approaches to represent the liquid states can be seen in the literature: sampling of analog signals ( [25], [10]), such as the postsynaptic potential or the neural membrane voltage [26] and spike train decoding ( [23], [27], [28], [29], [8], [24]). T the typical representation in the latter is a state matrix obtained by filtering the discrete spike trains generated within the liquid after exposure to a stimulus ([3], [13], [30], [27], [10]).…”

Section: The Liquid State Machine Model (Lsm)mentioning

confidence: 99%

Neural Synchrony-Based State Representation in Liquid State Machines, an Exploratory Study

Pajot,

Boukadoum

2023

J. Engg. Res. & Sci.

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Solving classification problems by Liquid State Machines (LSM) usually ignores the influence of the liquid state representation on performance, leaving the role to the reader circuit. In most studies, the decoding of the internally generated neural states is performed on spike rate-based vector representations. This approach occults the interspike timing, a central aspect of biological neural coding, with potentially detrimental consequences on the LSM performance. In this work, we propose a model of liquid state representation that builds the feature vectors from temporal information extracted from the spike trains, hence using spike synchrony instead of rate. Using pairs of Poissondistributed spike trains in noisy conditions, we show that such model outperforms a rate-only model in distinguishing two spike trains regardless of the sampling frequency of the liquid states or the noise level. In the same vein, we suggest a synchrony-based measure of the separation property (SP), a core feature of LSMs regarding classification performance, for a more robust and biologically plausible interpretation.

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Section: The Liquid State Machine Model (Lsm)mentioning

confidence: 99%

Neural Synchrony-Based State Representation in Liquid State Machines, an Exploratory Study

Pajot,

Boukadoum

2023

J. Engg. Res. & Sci.

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“…Activity patterns and spiking dynamics in E-I recurrent networks were systematically studied in these works. What's more, signal coding in recurrent E-I network also attracted much research [12,17,27,28,31,34,[34][35][36][37][38][39]. In most works, neurons are considered into excitatory and inhibitory neurons and whether the synaptic connections are excitatory or inhibitory is mainly determined by the type of presynaptic neurons.…”

Section: Introductionmentioning

confidence: 99%

Population rate coding in recurrent neuronal networks with undetermined-type neurons

Si¹,

Sun²

2019

Preprint

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Neural coding is a key problem in neuroscience, which can promote people's understanding of the mechanism that brain processes information. Among the classical theories of neural coding, the population rate coding has been studied widely in many works. Most computational studies considered the neurons and the corresponding presynaptic synapses as pre-determined excitatory or inhibitory types. According to physiological evidence, however, that the real effect of a synapse is inhibitory or excitatory is determined by the type of the activated receptors. The corelease of excitatory and inhibitory receptors in the same synapse exists widely in the brain. In this paper, we study the population rate coding in recurrent neuronal networks with undetermined neurons and synapses, different from the traditional works, in which one neuron can perform either excitatory or inhibitory effect to the corresponding postsynaptic neurons. We find such neuronal networks can encode the stimuli information in population firing rate well. We find that intermediate recurrent probability together with moderate Inhibitory-Excitatory strength ratio can enhance the encoding performance. Suitable combinations of the previous two parameters with the noise intensity, the excitatory synaptic strength and the synaptic time constant have promoting effects on the performance of population rate coding. Finally, we compare the performance of population rate coding between the traditional (determined) model and ours, and we find that it is rational to consider the co-release of inhibitory and excitatory receptors.

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Population rate coding in recurrent neuronal networks consisting of neurons with mixed excitatory–inhibitory synapses

Sun

2020

Nonlinear Dyn

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Transient information flow in a network of excitatory and inhibitory model neurons: Role of noise and signal autocorrelation

Cited by 5 publications

References 66 publications

Neural Synchrony-Based State Representation in Liquid State Machines, an Exploratory Study

Neural Synchrony-Based State Representation in Liquid State Machines, an Exploratory Study

Population rate coding in recurrent neuronal networks with undetermined-type neurons

Population rate coding in recurrent neuronal networks consisting of neurons with mixed excitatory–inhibitory synapses

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