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A non-spiking neuron model with dynamic leak to avoid instability in recurrent networks
Frontiers in Computational Neuroscience ( IF 3.2 ) Pub Date : 2021-04-19 , DOI: 10.3389/fncom.2021.656401
Udaya B. Rongala , Jonas M. D. Enander , Matthias Kohler , Gerald E. Loeb , Henrik Jörntell

Recurrent circuitry components are distributed widely within the brain, including both excitatory and inhibitory synaptic connections. Recurrent neuronal networks have potential stability problems, perhaps a predisposition to epilepsy. More generally, instability risks making internal representations of information unreliable. To assess the inherent stability properties of such recurrent networks, we tested a linear summation, non-spiking neuron model with and without a ‘dynamic leak’, corresponding to the low-pass filtering of synaptic input current by the RC circuit of the biological membrane. We first show that the output of this neuron model, in either of its two forms, follows its input at a higher fidelity than a wide range of spiking neuron models across a range of input frequencies. Then we constructed fully connected recurrent networks with equal numbers of excitatory and inhibitory neurons and randomly distributed weights across all synapses. When the networks were driven by pseudorandom sensory inputs with varying frequency, the recurrent network activity tended to induce high frequency self-amplifying components, sometimes evident as distinct transients, which were not present in the input data. The addition of a dynamic leak based on known membrane properties consistently removed such spurious high frequency noise across all networks. Furthermore, we found that the neuron model with dynamic leak imparts a network stability that seamlessly scales with the size of the network, conduction delays, the input density of the sensory signal and a wide range of synaptic weight distributions. Our findings suggest that neuronal dynamic leak serves the beneficial function of protecting recurrent neuronal circuitry from the self-induction of spurious high frequency signals, thereby permitting the brain to utilize this architectural circuitry component regardless of network size or recurrency.

中文翻译:

具有动态泄漏的非尖峰神经元模型,可避免递归网络中的不稳定

循环电路组件广泛分布在大脑内,包括兴奋性和抑制性突触连接。复发性神经元网络具有潜在的稳定性问题,可能是癫痫病的易感性。更笼统地说,不稳定有使信息的内部表示不可靠的风险。为了评估此类递归网络的固有稳定性,我们测试了带有和不带有“动态泄漏”的线性求和,非尖峰神经元模型,该模型对应于生物膜的RC电路对突触输入电流的低通滤波。 。我们首先表明,在各种输入频率范围内,该神经元模型以两种形式之一的输出都以比高范围尖峰神经元模型更高的保真度跟随其输入。然后,我们构建了具有相等数量的兴奋性和抑制性神经元且权重在所有突触中随机分布的完全连接的递归网络。当网络由频率变化的伪随机感觉输入驱动时,网络的经常性活动往往会引起高频自放大成分,有时表现为明显的瞬态现象,这些瞬态现象在输入数据中不存在。基于已知膜特性的动态泄漏的添加始终消除了所有网络中的此类杂散高频噪声。此外,我们发现具有动态泄漏的神经元模型赋予网络稳定性,该稳定性与网络的大小,传导延迟,感觉信号的输入密度和广泛的突触权重分布无缝地缩放。
更新日期:2021-04-19
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