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首页> 外文期刊>Journal of Computational Neuroscience >Spatiotemporal discrimination in attractor networks with short-term synaptic plasticity
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Spatiotemporal discrimination in attractor networks with short-term synaptic plasticity

机译：具有短期突触可塑性的吸引子网络的时空区分

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We demonstrate that a randomly connected attractor network with dynamic synapses can discriminate between similar sequences containing multiple stimuli suggesting such networks provide a general basis for neural computations in the brain. The network contains units representing assemblies of pools of neurons, with preferentially strong recurrent excitatory connections rendering each unit bi-stable. Weak interactions between units leads to a multiplicity of attractor states, within which information can persist beyond stimulus offset. When a new stimulus arrives, the prior state of the network impacts the encoding of the incoming information, with short-term synaptic depression ensuring an itinerancy between sets of active units. We assess the ability of such a network to encode the identity of sequences of stimuli, so as to provide a template for sequence recall, or decisions based on accumulation of evidence. Across a range of parameters, such networks produce the primacy (better final encoding of the earliest stimuli) and recency (better final encoding of the latest stimuli) observed in human recall data and can retain the information needed to make a binary choice based on total number of presentations of a specific stimulus. Similarities and differences in the final states of the network produced by different sequences lead to predictions of specific errors that could arise when an animal or human subject generalizes from training data, when the training data comprises a subset of the entire stimulus repertoire. We suggest that such networks can provide the general purpose computational engines needed for us to solve many cognitive tasks.
机译：我们证明具有动态突触的随机连接的吸引子网络可以区分包含多个刺激的相似序列，表明此类网络为大脑中神经计算提供了一般基础。该网络包含代表神经元池集合的单元，优先的强循环兴奋性连接使每个单元呈双稳态。单元之间的弱交互会导致吸引子状态的多样性，在这种状态下，信息可以持续存在，而不是刺激抵消。当出现新的刺激时，网络的先前状态会影响传入信息的编码，而短期的突触抑制会确保活动单元集之间的迭代。我们评估这种网络编码刺激序列的身份的能力，从而为序列召回或基于证据积累的决策提供模板。在各种参数范围内，此类网络可产生在人类回忆数据中观察到的优先级（最早的刺激物更好的最终编码）和新近度（最新刺激物的更好最终编码），并且可以保留基于总数做出二元选择所需的信息。特定刺激的表现次数。由不同序列产生的网络最终状态的相似性和差异导致对特定错误的预测，当动物或人类受试者根据训练数据进行概括时，当训练数据包含整个刺激库的一个子集时，可能会出现特定错误。我们建议这样的网络可以提供我们解决许多认知任务所需的通用计算引擎。

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