Carrillo-Medina José Luis, Latorre Roberto
Departamento de Eléctrica y Electrónica, Universidad de las Fuerzas Armadas - ESPE Sangolquí, Ecuador.
Grupo de Neurocomputación Biológica, Dpto. de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid Madrid, Spain.
Front Comput Neurosci. 2016 Dec 20;10:132. doi: 10.3389/fncom.2016.00132. eCollection 2016.
constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed paradigm-i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data-to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence of inhibitory connections. These parameters also modulate the memory capabilities of the network. The dynamical modes observed in the different informational dimensions in a given moment are independent and they only depend on the parameters shaping the information processing in this dimension. In view of these results, we argue that plasticity mechanisms inside individual cells and multicoding strategies can provide additional computational properties to spiking neural networks, which could enhance their capacity and performance in a wide variety of real-world tasks.
构成了开发逼真的人工神经网络（ANNs）最具前景的方法。与传统的基于发放率的范式不同，脉冲发放模型中的信息编码基于单个脉冲的精确时间。已经证明，脉冲发放人工神经网络可以成功且高效地应用于多个能用传统策略解决的实际问题（例如，数据分类或模式识别）。近年来，神经科学研究的重大突破在不同的活体神经系统中发现了新的相关计算原理。人工神经网络能否从这些近期发现中受益，从而获得新的启发元素呢？这对研究界来说是一个有趣的问题，并且包括新型生物启发式信息编码和处理策略在内的脉冲发放人工神经网络的发展正受到关注。从这个角度来看，在这项工作中，我们采用了最近提出的范式的核心概念，即神经特征来识别网络中的每个单元、处理过程中的局部信息情境化以及关于数据来源和内容的信息传播的多编码策略，以便在脉冲发放神经网络中使用。据我们所知，这些机制尚未在脉冲发放神经元的人工神经网络背景中使用过。本文为它们在此类网络中的适用性提供了概念验证。计算机模拟表明，像这里讨论的简单网络模型具有复杂的自组织特性。多种同时编码方案的组合使网络能够在不同的时空空间中生成共存的时空活动模式来编码信息。作为塑造相应编码方式的网络和/或单元内参数的函数，即使在没有抑制性连接的情况下，诱发模式之间也会出现不同形式的竞争。这些参数还会调节网络的记忆能力。在给定时刻不同信息维度中观察到的动态模式是独立的，并且它们仅取决于塑造该维度信息处理的参数。鉴于这些结果，我们认为单个细胞内的可塑性机制和多编码策略可以为脉冲发放神经网络提供额外的计算特性，这可以增强它们在各种现实世界任务中的能力和性能。
