Collaborative Research: Emergent Sequences in Inhibition-Dominated Recurrent Networks

合作研究：抑制主导的循环网络中的涌现序列

基本信息

批准号：
1951165
负责人：
Carina Curto
金额：
$ 14.99万
依托单位：
Pennsylvania State Univ University Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1951165&HistoricalAwards=false
关键词：
Collaborative Research Emergent Sequences Inhibition

项目摘要

Sequences of neural activity arise in many brain areas, including cortex, hippocampus, and central pattern generator circuits that underlie rhythmic behaviors like locomotion. Moreover, sequences that occur in hippocampus while the animal is at rest or asleep are believed to be critical for memory processing and consolidation. These sequences are examples of internally generated activity: that is, neural activity that is shaped primarily by the structure of recurrent connections between neurons. The goal of this research is to advance the mathematical theory of sequence generation. A fundamental question is what types of network architectures underlie emergent sequences. This work will investigate the mechanisms for sequence generation in recurrently connected networks with complex patterns of connectivity and inhibition-dominated dynamics. The theory will then be used to understand and model neural sequences, with a focus on hippocampal sequences. Although this work is motivated by neuroscience, the phenomenon of sequential activity emerging from competition between units is sufficiently common that the mathematical results derived here are likely to be useful in a variety of broader contexts in the biological and social sciences.The main goal of this research is to understand, and be able to predict, the set of neural activity sequences in a recurrent network from the underlying structure of connectivity. In addition to providing new insights about sequence generation in the brain, this study will elucidate structure-function relationships in recurrent networks and provide tools for analyzing networks to identify dynamically relevant motifs. This research will be carried out in the context of a special family of inhibition-dominated threshold-linear networks, which are a commonly used firing rate model of recurrent network dynamics. These networks naturally give rise to an abundance of sequences, and the dynamics are tightly connected to the underlying connectivity graph. Moreover, they are mathematically tractable and thus amenable to a mathematical theory of sequence generation. Project 1 focuses on network architectures built from directional graphs, a new type of graph exhibiting directional dynamics without necessarily having a feedforward architecture, thus providing an important generalization of synfire chains. Project 2 addresses the anatomy of a sequence and its decomposition into “core” and “peripheral” components, with the core being a network motif that supports a sequential attractor, and the periphery consisting of additional neurons that are recruited by the attractor. Finally, Project 3 uses the theory developed in earlier projects to analyze and model various phenomena observed in hippocampal sequences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

神经活动序列出现在许多大脑区域，包括皮层、海马体和中枢模式生成电路，它们是运动等节律行为的基础。此外，动物休息或睡眠时海马体中发生的序列被认为对于记忆处理至关重要。这些序列是内部生成活动的例子：即主要由神经元之间的循环连接结构形成的神经活动。这项研究的目标是推进序列生成的数学理论。类型这项工作将研究具有复杂连接模式和抑制主导动力学的循环连接网络中的序列生成机制，然后该理论将用于理解和建模神经序列，重点是海马序列。尽管这项工作是由神经科学推动的，但单元之间的竞争所出现的顺序活动现象非常普遍，因此这里得出的数学结果可能在生物和社会科学的各种更广泛的背景下有用。这项工作的主要目标研究的目的是理解并能够从连接的底层结构中预测循环网络中的神经活动序列集除了提供关于大脑中序列生成的新见解之外，这项研究还将阐明循环网络中的结构-功能关系。提供用于分析网络以识别动态相关主题的工具。这项研究将在抑制主导的阈值线性网络的特殊家族的背景下进行，这些网络是循环网络动态的常用放电率模型。产生丰富的序列，并且此外，它们在数学上易于处理，因此适合序列生成的数学理论。项目 1 专注于由方向图构建的网络架构，这是一种无需具有方向动态性的新型图。前馈架构，从而提供了 Synfire 链的重要概括，项目 2 解决了序列的解剖及其分解为“核心”和“外围”组件的问题，核心是支持顺序吸引子的网络主题，以及最后，项目 3 使用早期项目中开发的理论来分析和模拟在海马序列中观察到的各种现象。该奖项反映了 NSF 的法定使命，并通过评估被认为值得支持。利用基金会的智力优势和更广泛的影响审查标准。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Stable fixed points of combinatorial threshold-linear networks

组合阈值线性网络的稳定不动点

DOI：
10.1016/j.aam.2023.102652
发表时间：
2024
期刊：
Advances in Applied Mathematics
影响因子：
1.1
作者：
Curto, Carina;Geneson, Jesse;Morrison, Katherine
通讯作者：
Morrison, Katherine

Nerve Theorems for Fixed Points of Neural Networks

神经网络不动点的神经定理

DOI：
10.1007/978-3-030-95519-9_6
发表时间：
2022
期刊：
Association for Women in Mathematics series
影响因子：
0
作者：
Santander, D. E.;Ebli, S.;Patania, A.;Sanderson, N.;Burtscher, F.;Morrison, K.;Curto, C.
通讯作者：
Curto, C.

Periodic neural codes and sound localization in barn owls

仓鸮的周期性神经编码和声音定位

DOI：
10.2140/involve.2022.15.1
发表时间：
2022
期刊：
a Journal of Mathematics
影响因子：
0
作者：
Brown, Lindsey S.;Curto, Carina
通讯作者：
Curto, Carina

Graph Rules for Recurrent Neural Network Dynamics

递归神经网络动力学的图规则

DOI：
10.1090/noti2661
发表时间：
2023
期刊：
Notices of the American Mathematical Society
影响因子：
0
作者：
Curto, Carina;Morrison, Katherine
通讯作者：
Morrison, Katherine

Sequence generation in inhibition-dominated neural networks

抑制主导的神经网络中的序列生成

DOI：
发表时间：
2022
期刊：
SIAM journal on applied dynamical systems
影响因子：
2.1
作者：
Parmelee, Caitlin;Londono Alvarez, Juliana;Curto, Carina;Morrison, Katherine
通讯作者：
Morrison, Katherine