Hoppensteadt FC, Izhikevich EM
Center for Systems Science and Engineering, Arizona State University, Tempe, Arizona 85287-7606, USA.
Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics. 2000 Sep;62(3 Pt B):4010-3. doi: 10.1103/physreve.62.4010.
We investigate here possible neurocomputational features of networks of laser oscillators. Our approach is similar to classical optical neurocomputing where artificial neurons are lasers and connection matrices are holographic media. However, we consider oscillatory neurons communicating via phases rather than amplitudes. Memorized patterns correspond to synchronized states where the neurons oscillate with equal frequencies and with prescribed phase relations. The mechanism of recognition is related to phase locking.
我们在此研究激光振荡器网络可能具有的神经计算特征。我们的方法类似于经典光学神经计算,其中人工神经元是激光器,连接矩阵是全息介质。然而,我们考虑的是通过相位而非幅度进行通信的振荡神经元。记忆模式对应于同步状态,其中神经元以相等频率并按照规定的相位关系振荡。识别机制与锁相有关。