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:
- 金额:$ 14.99万
- 依托单位:
- 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:
- 关键词:
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.
神经活动的序列出现在许多大脑区域,包括皮层、海马和中枢模式发生器电路,这些电路是运动等节律行为的基础。此外,当动物处于休息或睡眠状态时,海马体中发生的序列被认为是记忆处理和巩固的关键。这些序列是内部产生的活动的例子:也就是说,主要由神经元之间的递归连接结构形成的神经活动。本研究的目的是推进序列生成的数学理论。一个基本的问题是什么类型的网络架构下涌现序列。这项工作将调查的机制,序列生成的循环连接网络与复杂的模式连接和抑制主导的动态。然后,该理论将用于理解和建模神经序列,重点是海马序列。 虽然这项工作是由神经科学的动机,从单位之间的竞争出现的顺序活动的现象是足够普遍的,这里得出的数学结果可能是有用的,在各种更广泛的背景下,在生物和社会科学。这项研究的主要目标是了解,并能够预测,递归网络中的神经活动序列集来自底层的连接结构。除了提供有关大脑中序列生成的新见解外,这项研究还将阐明循环网络中的结构-功能关系,并提供分析网络以识别动态相关基序的工具。 这项研究将在一个特殊的家庭的抑制主导的阈值线性网络,这是一个常用的放电率模型经常性的网络动态的背景下进行。 这些网络自然会产生大量的序列,并且动态与底层的连接图紧密相连。 此外,它们在数学上是易处理的,因此服从于序列生成的数学理论。 项目1的重点是从方向图构建的网络架构,这是一种新型的图,展示了方向动态,而不一定具有前馈架构,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,核心是一个支持顺序吸引子的网络基序,外围由吸引子招募的额外神经元组成。 最后,项目3使用早期项目中开发的理论来分析和模拟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,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
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:
- 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:
- 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:
- 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:
- 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:
- 影响因子:2.1
- 作者:Parmelee, Caitlin;Londono Alvarez, Juliana;Curto, Carina;Morrison, Katherine
- 通讯作者:Morrison, Katherine
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Carina Curto其他文献
Model-based prediction of maximum pool size in the ribbon synapse
- DOI:
10.1186/1471-2202-16-s1-p41 - 发表时间:
2015-12-18 - 期刊:
- 影响因子:2.300
- 作者:
Caitlyn M Parmelee;Matthew Van Hook;Wallace B Thoreson;Carina Curto - 通讯作者:
Carina Curto
State-dependence of sensory-evoked responses in neocortex
- DOI:
10.1186/1471-2202-8-s2-p17 - 发表时间:
2007-07-06 - 期刊:
- 影响因子:2.300
- 作者:
Carina Curto;Shuzo Sakata;Vladimir Itskov;Kenneth D Harris - 通讯作者:
Kenneth D Harris
From spikes to space: reconstructing features of the environment from spikes alone
- DOI:
10.1186/1471-2202-8-s2-p158 - 发表时间:
2007-07-06 - 期刊:
- 影响因子:2.300
- 作者:
Vladimir Itskov;Carina Curto - 通讯作者:
Carina Curto
Carina Curto的其他文献
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{{ truncateString('Carina Curto', 18)}}的其他基金
Theory of threshold-linear networks and combinatorial neural codes.
阈值线性网络和组合神经代码的理论。
- 批准号:
1516881 - 财政年份:2015
- 资助金额:
$ 14.99万 - 项目类别:
Standard Grant
Memory encoding in spatially structured networks: dynamics, discrete geometry & topology
空间结构化网络中的记忆编码:动力学、离散几何
- 批准号:
1537228 - 财政年份:2014
- 资助金额:
$ 14.99万 - 项目类别:
Standard Grant
Memory encoding in spatially structured networks: dynamics, discrete geometry & topology
空间结构化网络中的记忆编码:动力学、离散几何
- 批准号:
1225666 - 财政年份:2012
- 资助金额:
$ 14.99万 - 项目类别:
Standard Grant
Stimulus representation and spontaneous activity in recurrent networks
循环网络中的刺激表征和自发活动
- 批准号:
0920845 - 财政年份:2009
- 资助金额:
$ 14.99万 - 项目类别:
Standard Grant
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