Department of Physics and Astronomy, University of California Los Angeles Los Angeles, CA, USA.
Front Comput Neurosci. 2010 Oct 15;4:142. doi: 10.3389/fncom.2010.00142. eCollection 2010.
Since the discovery of place cells - single pyramidal neurons that encode spatial location - it has been hypothesized that the hippocampus may act as a cognitive map of known environments. This putative function has been extensively modeled using auto-associative networks, which utilize rate-coded synaptic plasticity rules in order to generate strong bi-directional connections between concurrently active place cells that encode for neighboring place fields. However, empirical studies using hippocampal cultures have demonstrated that the magnitude and direction of changes in synaptic strength can also be dictated by the relative timing of pre- and post-synaptic firing according to a spike-timing dependent plasticity (STDP) rule. Furthermore, electrophysiology studies have identified persistent "theta-coded" temporal correlations in place cell activity in vivo, characterized by phase precession of firing as the corresponding place field is traversed. It is not yet clear if STDP and theta-coded neural dynamics are compatible with cognitive map theory and previous rate-coded models of spatial learning in the hippocampus. Here, we demonstrate that an STDP rule based on empirical data obtained from the hippocampus can mediate rate-coded Hebbian learning when pre- and post-synaptic activity is stochastic and has no persistent sequence bias. We subsequently demonstrate that a spiking recurrent neural network that utilizes this STDP rule, alongside theta-coded neural activity, allows the rapid development of a cognitive map during directed or random exploration of an environment of overlapping place fields. Hence, we establish that STDP and phase precession are compatible with rate-coded models of cognitive map development.
自发现位置细胞(一种编码空间位置的单个金字塔形神经元)以来,人们一直假设海马体可能充当已知环境的认知图。这种假设功能已通过自联想网络进行了广泛的建模,该网络利用率编码突触可塑性规则,以在编码相邻位置场的同时活跃的位置细胞之间生成强的双向连接。然而,使用海马体培养物进行的实证研究表明,突触强度的变化幅度和方向也可以根据前突触和后突触放电的相对时间来决定,这符合基于尖峰时间依赖性可塑性(STDP)的规则。此外,电生理学研究在体内的位置细胞活动中发现了持久的“θ编码”时间相关性,其特征是当相应的位置场被遍历时,放电的相位超前。目前尚不清楚 STDP 和θ编码的神经动力学是否与认知图理论和海马体中先前的空间学习率编码模型兼容。在这里,我们证明了一种基于从海马体获得的经验数据的 STDP 规则,当前突触和后突触活动是随机的并且没有持久的序列偏置时,该规则可以介导率编码的赫布学习。我们随后证明,利用这种 STDP 规则和θ编码神经活动的尖峰递归神经网络允许在定向或随机探索重叠位置场的环境中快速发展认知图。因此,我们确定 STDP 和相位超前与认知图发展的率编码模型兼容。