Wojtak Weronika, Coombes Stephen, Avitabile Daniele, Bicho Estela, Erlhagen Wolfram
Research Centre of Mathematics, University of Minho, Guimarães, Portugal.
Research Centre Algoritmi, University of Minho, Guimarães, Portugal.
Cogn Neurodyn. 2024 Dec;18(6):3273-3289. doi: 10.1007/s11571-023-09979-3. Epub 2023 May 29.
Continuous bump attractor networks (CANs) have been widely used in the past to explain the phenomenology of working memory (WM) tasks in which continuous-valued information has to be maintained to guide future behavior. Standard CAN models suffer from two major limitations: the stereotyped shape of the bump attractor does not reflect differences in the representational quality of WM items and the recurrent connections within the network require a biologically unrealistic level of fine tuning. We address both challenges in a two-dimensional (2D) network model formalized by two coupled neural field equations of Amari type. It combines the lateral-inhibition-type connectivity of classical CANs with a locally balanced excitatory and inhibitory feedback loop. We first use a radially symmetric connectivity to analyze the existence, stability and bifurcation structure of 2D bumps representing the conjunctive WM of two input dimensions. To address the quality of WM content, we show in model simulations that the bump amplitude reflects the temporal integration of bottom-up and top-down evidence for a specific combination of input features. This includes the network capacity to transform a stable subthreshold memory trace of a weak input into a high fidelity memory representation by an unspecific cue given retrospectively during WM maintenance. To address the fine-tuning problem, we test numerically different perturbations of the assumed radial symmetry of the connectivity function including random spatial fluctuations in the connection strength. Different to the behavior of standard CAN models, the bump does not drift in representational space but remains stationary at the input position.
连续脉冲吸引子网络（CANs）在过去已被广泛用于解释工作记忆（WM）任务的现象学，在这些任务中，必须维持连续值信息以指导未来行为。标准的CAN模型存在两个主要局限性：脉冲吸引子的刻板形状不能反映WM项目表征质量的差异，并且网络内的递归连接需要生物学上不现实的精细调谐水平。我们在由两个耦合的阿马里型神经场方程形式化的二维（2D）网络模型中解决了这两个挑战。它将经典CANs的侧向抑制型连接与局部平衡的兴奋性和抑制性反馈回路相结合。我们首先使用径向对称连接来分析表示两个输入维度的联合WM的二维脉冲的存在性、稳定性和分岔结构。为了解决WM内容的质量问题，我们在模型模拟中表明，脉冲幅度反映了针对输入特征的特定组合的自下而上和自上而下证据的时间整合。这包括网络通过在WM维持期间追溯给出的非特定线索将弱输入的稳定亚阈值记忆痕迹转换为高保真记忆表征的能力。为了解决精细调谐问题，我们对连接函数假定的径向对称性的不同扰动进行了数值测试，包括连接强度的随机空间波动。与标准CAN模型的行为不同，脉冲在表征空间中不会漂移，而是在输入位置保持静止。
