Giulioni Massimiliano, Camilleri Patrick, Mattia Maurizio, Dante Vittorio, Braun Jochen, Del Giudice Paolo
Department of Technologies and Health, Istituto Superiore di Sanitã Rome, Italy.
Front Neurosci. 2012 Feb 2;5:149. doi: 10.3389/fnins.2011.00149. eCollection 2011.
We demonstrate bistable attractor dynamics in a spiking neural network implemented with neuromorphic VLSI hardware. The on-chip network consists of three interacting populations (two excitatory, one inhibitory) of leaky integrate-and-fire (LIF) neurons. One excitatory population is distinguished by strong synaptic self-excitation, which sustains meta-stable states of "high" and "low"-firing activity. Depending on the overall excitability, transitions to the "high" state may be evoked by external stimulation, or may occur spontaneously due to random activity fluctuations. In the former case, the "high" state retains a "working memory" of a stimulus until well after its release. In the latter case, "high" states remain stable for seconds, three orders of magnitude longer than the largest time-scale implemented in the circuitry. Evoked and spontaneous transitions form a continuum and may exhibit a wide range of latencies, depending on the strength of external stimulation and of recurrent synaptic excitation. In addition, we investigated "corrupted" "high" states comprising neurons of both excitatory populations. Within a "basin of attraction," the network dynamics "corrects" such states and re-establishes the prototypical "high" state. We conclude that, with effective theoretical guidance, full-fledged attractor dynamics can be realized with comparatively small populations of neuromorphic hardware neurons.
我们在由神经形态超大规模集成电路硬件实现的脉冲神经网络中展示了双稳态吸引子动力学。片上网络由三个相互作用的群体（两个兴奋性群体、一个抑制性群体）的泄漏积分发放（LIF）神经元组成。一个兴奋性群体的特点是具有强烈的突触自我兴奋，这维持了“高”和“低”发放活动的亚稳态。根据整体兴奋性，向“高”状态的转变可能由外部刺激诱发，或者可能由于随机活动波动而自发发生。在前一种情况下，“高”状态保留刺激的“工作记忆”，直到刺激释放后很久。在后一种情况下，“高”状态保持稳定数秒，比电路中实现的最大时间尺度长三个数量级。诱发的和自发的转变形成一个连续体，并且可能表现出广泛的延迟，这取决于外部刺激的强度和递归突触兴奋的强度。此外，我们研究了由两个兴奋性群体的神经元组成的“受损”“高”状态。在“吸引域”内，网络动力学“纠正”这些状态并重新建立典型的“高”状态。我们得出结论，在有效的理论指导下，用相对较少数量的神经形态硬件神经元就可以实现成熟的吸引子动力学。
