Fusi S, Annunziato M, Badoni D, Salamon A, Amit D J
INFN Sezione RM1, University of Rome La Sapienza, Roma, Italy.
Neural Comput. 2000 Oct;12(10):2227-58. doi: 10.1162/089976600300014917.
We present a model for spike-driven dynamics of a plastic synapse, suited for aVLSI implementation. The synaptic device behaves as a capacitor on short timescales and preserves the memory of two stable states (efficacies) on long timescales. The transitions (LTP/LTD) are stochastic because both the number and the distribution of neural spikes in any finite (stimulation) interval fluctuate, even at fixed pre- and postsynaptic spike rates. The dynamics of the single synapse is studied analytically by extending the solution to a classic problem in queuing theory (Takacs process). The model of the synapse is implemented in aVLSI and consists of only 18 transistors. It is also directly simulated. The simulations indicate that LTP/LTD probabilities versus rates are robust to fluctuations of the electronic parameters in a wide range of rates. The solutions for these probabilities are in very good agreement with both the simulations and measurements. Moreover, the probabilities are readily manipulable by variations of the chip's parameters, even in ranges where they are very small. The tests of the electronic device cover the range from spontaneous activity (3-4 Hz) to stimulus-driven rates (50 Hz). Low transition probabilities can be maintained in all ranges, even though the intrinsic time constants of the device are short (approximately 100 ms). Synaptic transitions are triggered by elevated presynaptic rates: for low presynaptic rates, there are essentially no transitions. The synaptic device can preserve its memory for years in the absence of stimulation. Stochasticity of learning is a result of the variability of interspike intervals; noise is a feature of the distributed dynamics of the network. The fact that the synapse is binary on long timescales solves the stability problem of synaptic efficacies in the absence of stimulation. Yet stochastic learning theory ensures that it does not affect the collective behavior of the network, if the transition probabilities are low and LTP is balanced against LTD.
我们提出了一种用于塑性突触的尖峰驱动动力学模型，适用于超大规模集成电路（VLSI）实现。该突触器件在短时间尺度上表现为一个电容器，并在长时间尺度上保留两种稳定状态（效能）的记忆。由于在任何有限（刺激）间隔内神经尖峰的数量和分布都会波动，即使在固定的突触前和突触后尖峰率下，转变（长时程增强/长时程抑制，LTP/LTD）也是随机的。通过将解决方案扩展到排队论中的一个经典问题（塔卡克斯过程），对单个突触的动力学进行了分析研究。突触模型在超大规模集成电路中实现，仅由18个晶体管组成。它也可以直接进行模拟。模拟表明，在很宽的速率范围内，LTP/LTD概率与速率的关系对电子参数的波动具有鲁棒性。这些概率的解决方案与模拟和测量结果都非常吻合。此外，即使在概率非常小的范围内，通过改变芯片参数也可以很容易地操纵这些概率。电子器件的测试涵盖了从自发活动（3 - 4赫兹）到刺激驱动速率（50赫兹）的范围。即使器件的固有时间常数很短（约100毫秒），在所有范围内都可以保持低转变概率。突触转变由突触前速率升高触发：对于低突触前速率，基本上没有转变。在没有刺激的情况下，突触器件可以将其记忆保留数年。学习的随机性是尖峰间隔变异性的结果；噪声是网络分布式动力学的一个特征。突触在长时间尺度上是二元的这一事实解决了在没有刺激时突触效能的稳定性问题。然而，随机学习理论确保如果转变概率很低且LTP与LTD平衡，它不会影响网络的集体行为。
