Effect of inhibitory feedback on correlated firing of spiking neural network

Xie, Jinli; Wang, Zhijie

doi:10.1007/s11571-013-9241-5

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…Currently, the main coding technologies include perceptual coding, phase coding, frequency coding and population coding etc. (Johnson 2004;Nirenberg and Latham 2003;Victor 1999;Rokem et al 2006;Liu et al 2010;Xie and Wang 2013;Pakhomov and Sudin 2013). However, these neural coding theories have many difficulties in studying neural encoding and decoding in the brain (McLaughlin 2009;Gopathy Purushothanman & David 2005).The main reason is that these coding theories are local and regional, and do not cross influence of large-scale neurological activities.…”

Section: Introductionmentioning

confidence: 99%

Energy coding in neural network with inhibitory neurons

Wang

Fang

2014

Cogn Neurodyn

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This paper aimed at assessing and comparing the effects of the inhibitory neurons in the neural network on the neural energy distribution, and the network activities in the absence of the inhibitory neurons to understand the nature of neural energy distribution and neural energy coding. Stimulus, synchronous oscillation has significant difference between neural networks with and without inhibitory neurons, and this difference can be quantitatively evaluated by the characteristic energy distribution. In addition, the synchronous oscillation difference of the neural activity can be quantitatively described by change of the energy distribution if the network parameters are gradually adjusted. Compared with traditional method of correlation coefficient analysis, the quantitative indicators based on nervous energy distribution characteristics are more effective in reflecting the dynamic features of the neural network activities. Meanwhile, this neural coding method from a global perspective of neural activity effectively avoids the current defects of neural encoding and decoding theory and enormous difficulties encountered. Our studies have shown that neural energy coding is a new coding theory with high efficiency and great potential.

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Section: Introductionmentioning

confidence: 99%

Energy coding in neural network with inhibitory neurons

Wang

Fang

2014

Cogn Neurodyn

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“…with overlapping receptive fields) will create higher levels of correlations, which would be presumably information-limiting (because common inputs create similar signal and noise correlations). Inhibitory feedback implemented by inhibitory connectivity between neuromorphic neurons may instead implement a correlation-reducing mechanism (as it has been shown both in models and in empirical neuroscience data [140][141][142]). This mechanism may be effective even in the presence of a large proportion of overlapping local or external inputs [133].…”

Section: Correlationsmentioning

confidence: 97%

Constraints on the design of neuromorphic circuits set by the properties of neural population codes

Panzeri

Janotte

Pequeno-Zurro

et al. 2023

Neuromorph. Comput. Eng.

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In the brain, information is encoded, transmitted and used to inform behaviour at the level of timing of action potentials distributed over population of neurons. To implement neural-like systems in silico, to emulate neural function, and to interface successfully with the brain, neuromorphic circuits need to encode information in a way compatible to that used by populations of neuron in the brain. To facilitate the cross-talk between neuromorphic engineering and neuroscience, in this Review we first critically examine and summarize emerging recent findings about how population of neurons encode and transmit information. We examine the effects on encoding and readout of information for different features of neural population activity, namely the sparseness of neural representations, the heterogeneity of neural properties, the correlations among neurons, and the time scales (from short to long) at which neurons encode information and maintain it consistently over time. Finally, we critically elaborate on how these facts constrain the design of information coding in neuromorphic circuits. We focus primarily on the implications for designing neuromorphic circuits that communicate with the brain, as in this case it is essential that artificial and biological neurons use compatible neural codes. However, we also discuss implications for the design of neuromorphic systems for implementation or emulation of neural computation.

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“…There has been much work recently on the gain modulation in networks developed from sensory systems [10][11][12][13][14][15]. It is known theoretically and numerically that network correlations can be modulated in networks with global delayed feedback [16][17][18][19][20][21][22]. However, these studies focused on the neural dynamics with connections which are spatially uniform, and thus little is known about the effects of the topography on correlations [23,24].…”

Section: Introductionmentioning

confidence: 99%