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SCIENCE CHINA Information Sciences, Volume 64 , Issue 3 : 132202(2021) https://doi.org/10.1007/s11432-019-2988-1

Hybrid neural state machine for neural network

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  • ReceivedNov 5, 2019
  • AcceptedJun 29, 2020
  • PublishedJan 22, 2021

Abstract


Acknowledgment

This work was partly supported by National Natural Science Foundation of China (Grant No. 61836004), Brain-Science Special Program of Beijing (Grant No. Z181100001518006), and CETC Haikang Group-Brain Inspired Computing Joint Research Center, the Suzhou-Tsinghua Innovation Leading Program (Grant No. 2016SZ0102).


References

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  • Figure 1

    (Color online) (a) Traditional neural network workflow; (b) H-NSM takes input from ANNs and/or SNNs, and controls the working flow according to diverse tasks, and then sends control signals to the inference networks or actuators; (c) H-NSM-C makes decision based on different conditions and activates the desired branches to accomplish different tasks; (d) H-NSM-S accomplishes sequential tasks and sends control signals according to the current step.

  • Figure 2

    (Color online) Demonstrate a three-state complete Moore state machine using SNN-based H-NSM.

  • Figure 3

    (Color online) Training procedure of an SNN-based H-NSM.

  • Figure 4

    (Color online) (a) State transfer matrix training with accurate supervised signals; (b) state transfer matrix training with 60% correct supervised signals after 32 training epochs; (c) state transfer matrix training with 50% correct supervised signals after 32 training epochs; (d) state transfer matrix training with 50% correct supervised signals after 100 training epochs.

  • Figure 5

    (Color online) (a) The state transfer rules for the Tower of Hanoi; (b) the process flow for function $f(s,t)$ using LIF neural network.

  • Figure 6

    (Color online) Multitask bicycle platform, which is able to take command from the environment to accomplish different tasks such as following a target person and avoiding the obstacles.

  • Figure 7

    (Color online) The 6-state state machine for autopilot bicycle demo. (a) The state transfer rules; (b) the H-NSM-C receives the camera video streams and microphone voice streams, and controls a steering motor for following a target person, executing voice command or avoiding the obstacles; (c) neuron state and event signal recorded during the testing.

  • Table 1  

    Table 1State configuration

    State Context Action
    $S_0$ First move Select the source and target peg for the first move
    $S_1$ Select Random select a source peg and a target peg
    $S_2$ Verify Perform function $f$
    $S_3$ Move Perform move
    $S_4$ Finish
  •   

    Algorithm 1 Inference and training procedure

    SubProc 1 is applied for deciding current state according to previous states and transfer signals: ${\rm~SIn}=[{\rm~SOut},{\rm~TOut}]$;

    $V_S={\rm~SIn}~\cdot~{\rm~CM}$; ${\rm~SOut}_j=1~{\rm~if}~(V_{S_j}>{\rm~ST})~{\rm~else}~~0,j\in[1,S]$;

    SubProc 2 is the STDP-like training of state transfer matrix: ${\rm~If}~({\rm~SOut}_j==0~\&~{\rm~SForce}_j~(t)==1~\&~{\rm~SIn}_{(S+r)}==1~\&~{\rm~CM}_{(r,j)}<P_{\rm~Ths})~{\rm~then}$

    ${\rm~CM}_{(r,j)}={\rm~CM}_{(r,j)}+\delta$; ${\rm~If}~({\rm~SOut}_j==1~\&~{\rm~SForce}_j~(t)==0~\&~{\rm~SIn}_{(S+r)}==1~\&~{\rm~CM}_{(r,j)}>N_{\rm~Ths})~{\rm~then}$ ${\rm~CM}_{(r,j)}={\rm~CM}_{(r,j)}-\delta$; $r\in[1,T],~j\in[1,S]$;

    SubProc 3: ${\rm~TIn}=[{\rm~Trigger},{\rm~SOut}]$;

    $V_T={\rm~TIn}~\cdot~{\rm~TM}$; ${\rm~TOut}_j=1~{\rm~if}~(V_{T_j}>{\rm~STT})~{\rm~else}~~0,j\in[1,T]$;

    SubProc 4: ${\rm~If}~({\rm~TOut}_j==0~\&~{\rm~TForce}_j~(t)==1~\&~{\rm~TIn}_r==1~\&~{\rm~TM}_{(r,j)}<P_{\rm~Tht})~{\rm~then}~$

    ${\rm~TM}_{(r,j)}={\rm~TM}_{(r,j)}+\delta$; ${\rm~If}~({\rm~TOut}_j==1~\&~{\rm~TForce}_j~(t)==0~\&~{\rm~TIn}_r==1~\&~{\rm~TM}_{(r,j)}~>N_{\rm~Tht}~)~~{\rm~then}$ ${\rm~TM}_{(r,j)}={\rm~TM}_{(r,j)}-\delta$; $r\in[1,S],j\in[1,T]$.

    for $t~=~1~\TO~{\rm~Tmax}$

    SubProc 1. Integrate and fire of state neurons;

    SubProc 2. If training, learning the state transfer matrix (SM);

    SubProc 3. Integrate and fire of transfer neurons;

    SubProc 4. If training, learning the trigger matrix (TM);

    end for

  • Table 2  

    Table 2Transfer condition

    Transfer Condition Action
    $T_0$ $S_0$–$S_3$ Always true
    $T_1$ $S_1$–$S_2$ Always true
    $T_2$ $S_2$–$S_3$ Function $f$ returns true
    $T_3$ $S_2$–$S_1$ Function $f$ returns false
    $T_4$ $S_3$–$S_1$ Not finish
    $T_5$ $S_3$–$S_4$ Finish (all the disks are on peg C)
  • Table 3  

    Table 3Time (ms)/number of steps cost using different methods

    Method Number of pegs
    4 5 6 7 8
    Optimum 0.6/16 1.3/32 3.4/64 6.9/128 11.8/256
    Random 25.1/315 681.5/8328 3068.8/39930 5927.0/78599 86107.6/1273359
    H-NSM 0.9/16 2.3/32 3.9/64 9.8/128 16.9/256