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A new approach to higher-level information fusion using associative learning in semantic networks of spiking neurons

机译：尖峰神经元语义网络中使用联想学习进行高级信息融合的新方法

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This paper presents a new approach to higher-level information fusion in which knowledge and data are represented using semantic networks composed of coupled spiking neuron nodes. Networks of simulated spiking neurons have been shown to exhibit synchronization, in which sub-assemblies of nodes become phase locked to one another. This phase locking reflects the tendency of biological neural systems to produce synchronized neural assemblies, which have been hypothesized to be involved in binding of low-level features in the perception of objects. The approach presented in this paper embeds spiking neurons in a semantic network, in which a synchronized sub-assembly of nodes represents a hypothesis about a situation. Likewise, multiple synchronized assemblies that are out-of-phase with one another represent multiple hypotheses. The initial network is hand-coded, but additional semantic relationships can be established by associative learning mechanisms. This approach is demonstrated by simulation of proof-of-concept scenarios involving the tracking of suspected criminal vehicles between meeting places in an urban environment. Our results indicate that synchronized sub-assemblies of spiking nodes can be used to represent multiple simultaneous events occurring in the environment and to effectively learn new relationships between semantic items in response to these events. In contrast to models of synchronized spiking networks that use physiologically realistic parameters in order to explain limits in human short-term memory (STM) capacity, our networks are not subject to the same limitations in representational capacity for multiple simultaneous events. Simulations demonstrate that the representational capacity of our networks can be very large, but as more simultaneous events are represented by synchronized sub-assemblies, the effective learning rate for establishing new relationships decreases. We propose that this effect could be countered by speeding up the spiking dynamics of the networks (a tactic of limited availability to biological systems). Such a speedup would allow the number of simultaneous events to increase without compromising the learning rate.
机译：本文提出了一种新的高级信息融合方法，其中知识和数据使用由耦合的尖峰神经元节点组成的语义网络表示。模拟尖刺神经元的网络已显示出同步性，其中节点的子组件彼此锁相。这种锁相反映了生物神经系统产生同步神经组件的趋势，据推测这种神经组件参与了对象感知中的低级特征的绑定。本文提出的方法将尖峰神经元嵌入语义网络中，其中节点的同步子组件表示关于情况的假设。同样，彼此异相的多个同步装配代表多个假设。初始网络是手工编码的，但是可以通过关联学习机制建立其他语义关系。通过模拟概念验证场景来证明这种方法，该场景涉及在城市环境中的聚会场所之间跟踪可疑犯罪车辆。我们的结果表明，尖峰节点的同步子组件可用于表示环境中发生的多个同时发生的事件，并有效地响应这些事件来学习语义项之间的新关系。与使用生理上现实的参数来解释人类短期记忆（STM）能力限制的同步尖峰网络模型相反，我们的网络在多个同时发生的事件的表示能力上不受相同的限制。仿真表明，我们网络的表示能力可能非常大，但是随着更多同步事件由同步子程序集表示，建立新关系的有效学习率会下降。我们建议可以通过加快网络的尖峰动态（一种对生物系统的可用性有限的策略）来抵消这种影响。这样的加速将允许同时发生的事件数量增加而不会损害学习速度。

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