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Enhancement of Information Transmission with Stochastic Resonance in Hippocampal CA1 Neuron Models: Effects of Noise Input Location and Its Power Spectrum

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  • 確率共振現象による海馬CA1ニューロンモデルでの情報伝送の強化: 雑音の付加位置および周波数スペクトルに関する検討
  • カクリツ キョウシン ゲンショウ ニ ヨル カイバ CA1 ニューロン モデル デ ノ ジョウホウ デンソウ ノ キョウカ ザツオン ノ フカ イチ オヨビ シュウハスウ スペクトル ニ カンスル ケントウ

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Abstract

Stochastic resonance (SR) is a phenomenon whereby detection of sub-threshold signal is improved by additive background noise in nonlinear systems. It has been unclear how this phenomenon is affected by the characteristics of background noise. In this paper, we investigate the effects of background noise characteristics on information transmission in a realistic hippocampal CA1 neuron model, i. e., the effect of input locations of the background noise and the influence of power spectra of the background noise. Using the computer simulation, the random sub-threshold input signal generated by a filtered homogeneous Poisson process was applied to a distal portion of the apical dendrite, while the background noise having a 1/fβ(β=0, 1, 2) power spectrum generated by a fractional integration was further applied to a variable location to investigate the effects of background noise input location on information transmission. Our results showed that SR was observed as the information rate reached a maximum value for optimal noise amplitude. The results also showed that the background noise input location and the coefficient β did not alter the maximum information rate generated by SR. The noise amplitude required to the maximum information rate increased when the noise input location was set at distal or the coefficient β was approached to zero from two. It is concluded that the variance of the noise having a frequency component of less than 200 Hz can play a key role in the information processing of sub-threshold signal transmission in the hippocampus.

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