Consistent recovery of sensory stimuli encoded with MIMO neural circuits.
Consistent recovery of sensory stimuli encoded with MIMO neural circuits.
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DOI：
10.1155/2010/469658
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发表时间：
2010
影响因子：
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通讯作者：
Pnevmatikakis EA
中科院分区：
文献类型：
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作者：
Lazar AA;Pnevmatikakis EA
我们考虑了用一群脉冲泄漏整合-火神经元编码的有限能量刺激的重构问题。重构后的信号满足一致性条件：当它通过同一个神经元时，它会触发与原始刺激相同的脉冲序列。恢复的刺激还必须最小化一个二次光滑最优准则。对于标量和矢量值的刺激，我们将重建表述为样条插值问题，并证明恢复具有唯一解。我们提供了明确的重建算法的刺激编码与单一以及一个群体的整合和火神经元。我们演示了我们的重建算法如何应用于带有反馈的开关神经回路编码的刺激。最后，我们将这种形式推广到多输入多输出的神经电路中，并证明了当神经种群的大小超过阈值时，向量值有限能量信号可以被有效地编码。举例说明了我们的方法在系统神经科学和神经形态工程中的潜在应用。
We consider the problem of reconstructing finite energy stimuli encoded with a population of spiking leaky integrate-and-fire neurons. The reconstructed signal satisfies a consistency condition: when passed through the same neuron, it triggers the same spike train as the original stimulus. The recovered stimulus has to also minimize a quadratic smoothness optimality criterion. We formulate the reconstruction as a spline interpolation problem for scalar as well as vector valued stimuli and show that the recovery has a unique solution. We provide explicit reconstruction algorithms for stimuli encoded with single as well as a population of integrate-and-fire neurons. We demonstrate how our reconstruction algorithms can be applied to stimuli encoded with ON-OFF neural circuits with feedback. Finally, we extend the formalism to multi-input multi-output neural circuits and demonstrate that vector-valued finite energy signals can be efficiently encoded by a neural population provided that its size is beyond a threshold value. Examples are given that demonstrate the potential applications of our methodology to systems neuroscience and neuromorphic engineering.