Multichannel parallel processing of neural signals in memristor arrays

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Science Advances  09 Oct 2020:
Vol. 6, no. 41, eabc4797
DOI: 10.1126/sciadv.abc4797
  • Fig. 1 Multichannel neural signal processing systems.

    (A) Illustration of conventional neural signal processing systems, which often use multiplexers (MUXs) to transform multichannel neural signals to serial signals and then process them for biomarker computation. (B) The proposed memristor-based system, which uses a memristor array for parallel processing of multichannel neural signals where the biomarker extraction is achieved by memristor conductance modulations. BL, bit line; WL, word line; SL, source line.

  • Fig. 2 Distinguish signal waveforms through memristor conductance modulation.

    (A) Memristor conductance modulation process under multiple identical RESET pulses starting from the same initial conductance value. Each data point is averaged from 128 devices. VWL = 5.0 V and VBL = 0 V. Pulse width, 50 ns. (B) Waveforms of several designed input signal pulse trains. They have the same average amplitude of 1.5 V, but different variations (σ), which increase from top to bottom. σ unit: volts. (C) Memristor conductance evolution with the applied pulse trains in (B). Each data point is averaged from 128 devices. (D) The average change in conductance (ΔG, left y axis) after applying different pulse trains, showing that ΔG increases with the input signal variations (right y axis).

  • Fig. 3 Parallel multichannel processing of neural signals.

    (A) Illustration of the signal segment scheme in memristor array.(B) Waveforms of typical 16-channel interictal and preictal signal clips. Blue, interictal. Red, preictal. The sampling rate is 400 Hz. (C and D) Histograms of the input voltage after the linear transformation of raw interictal and preictal neural signals, respectively. Gain = 104 and Voffset = 1.5 V. (E and F) Conductance change map after processing the interictal and preictal signal clips in the memristor array, respectively. (G and H) Histograms of the conductance change (ΔG) in (E) and (F), respectively.

  • Fig. 4 Robustness analysis of the memristor array–based system for multichannel neural signal processing.

    (A) The correlation matrix of the output ΔG in different trials. Interictal and preictal signals are used in first 100 and last 100 trials, respectively. (B) Shaded error bar plot of ΔG waveforms for the corresponding signals. (C) Distributions of ΔG variation in both interictal and preictal trials. (D) ΔG maps for different trials where the same sets of interictal and preictal neural signals are applied to two different memristor arrays. Trails from up to bottom and from left to right are labeled as T1 to T4 accordingly. (E) Correlation matrix between different trials. T1 and T2: The trials where the same interictal signal clip is applied to arrays #1 and #2, respectively. T3 and T4: The trials where the same preictal signal clip is applied to arrays #1 and #2, respectively.

  • Fig. 5 Analysis of memristor-processed results for seizure prediction.

    (A) Average ΔG versus signal energy of eight consecutive segments. (B) Average ΔG versus signal variation of eight consecutive segments. (C) Training results of the LDA classifier for 54 clips of 16-channel signals plotted along with testing results of the trained LDA classifier for 6 clips of 16-channel signals. F1, F2, and F3 represent the numbers of devices with ΔG in the specific ranges of (−35 μS, −27 μS), (−16 μS, −9 μS), and (−7 μS, 0 μS), respectively. (D) The training and testing accuracies from the 10-fold cross-validation.

Supplementary Materials

  • Supplementary Materials

    Multichannel parallel processing of neural signals in memristor arrays

    Zhengwu Liu, Jianshi Tang, Bin Gao, Xinyi Li, Peng Yao, Yudeng Lin, Dingkun Liu, Bo Hong, He Qian, Huaqiang Wu

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