Versatile networks of simulated spiking neurons displaying winner-take-all behavior.

Versatile networks of simulated spiking neurons displaying winner-take-all behavior.
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DOI:
10.3389/fncom.2013.00016
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发表时间:
2013
影响因子:
3.2
通讯作者:
Edelman GM
Edelman GM
中科院分区:
医学4区
文献类型:
--
作者:
Chen Y;McKinstry JL;Edelman GM

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我们描述了大规模网络的兴奋性和抑制性尖峰神经元,可以产生动态稳定的赢家通吃(WTA)的行为模拟。网络连接是中心-环绕架构的一种变体,我们称之为中心-环形-环绕(CAS)。在这种结构中,每个神经元被附近的邻居兴奋,并被环形环绕区域中更远的邻居抑制。这些网络的神经单元模拟基于电导的尖峰神经元,这些神经元通过对短期突触可塑性和STDP敏感的机制进行相互作用。我们表明,这样的CAS网络显示强大的WTA行为不同的中心环绕网络和其他控制架构,我们已经研究过。我们发现,一个大规模的网络尖峰神经元与兴奋性和抑制性神经元的单独群体可以产生平滑的感觉输入的地图。此外,我们还证明了在尖峰WTA神经网络控制下的类人型基于大脑的设备(BBD)可以学习到达其视野中的目标位置,从而证明了获得感觉运动协调。
We describe simulations of large-scale networks of excitatory and inhibitory spiking neurons that can generate dynamically stable winner-take-all (WTA) behavior. The network connectivity is a variant of center-surround architecture that we call center-annular-surround (CAS). In this architecture each neuron is excited by nearby neighbors and inhibited by more distant neighbors in an annular-surround region. The neural units of these networks simulate conductance-based spiking neurons that interact via mechanisms susceptible to both short-term synaptic plasticity and STDP. We show that such CAS networks display robust WTA behavior unlike the center-surround networks and other control architectures that we have studied. We find that a large-scale network of spiking neurons with separate populations of excitatory and inhibitory neurons can give rise to smooth maps of sensory input. In addition, we show that a humanoid brain-based-device (BBD) under the control of a spiking WTA neural network can learn to reach to target positions in its visual field, thus demonstrating the acquisition of sensorimotor coordination.
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