Developing novel neural network tools for accurate and interpretable dynamical modeling of neural circuits
开发新型神经网络工具,用于准确且可解释的神经回路动态建模
基本信息
- 批准号:10752956
- 负责人:
- 金额:$ 7.66万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-08-01 至 2026-07-31
- 项目状态:未结题
- 来源:
- 关键词:AddressAffectArchitectureAreaBehaviorBiologicalBrainCommunicationComplexComputer ModelsCoupledCouplingDataData SetDecision MakingDifferential EquationDimensionsDorsalElectrodesGenerationsGeometryGoalsHumanIndividualIntuitionInvestigationLearningMeasurementMethodologyMethodsModelingMonkeysMotorMotor CortexMovementNeural Network SimulationNeuronsNeurosciencesOutputPatternPerformancePopulationPublishingRewardsSpeedSystemSystems TheoryTestingTimeTrainingVariantartificial neural networkautoencoderbiological systemscognitive processdynamic systemfrontal lobein silicoinnovationinsightinventionmultidimensional dataneuralneural circuitneural modelneural networkneural network architectureneuromechanismneuronal patterningnovelreconstructionrecurrent neural networkterabytetheoriestime intervaltool
项目摘要
Abstract
In recent years, the number of neurons that we can record simultaneously has seen an exponential
increase, presenting a daunting challenge: how do we analyze these complex and high-dimensional
datasets to gain insight into how neural circuits perform computation? Tools from dynamical systems
theory have successfully unraveled the computational machinery of artificial recurrent neural networks
(RNNs) trained to perform goal-directed tasks. If we could apply these tools to biological neural circuits,
it would provide unparalleled access to the inner workings of the brain and potentially allow us to
connect theories of neural computation to real biological data. However, for these tools to be useful,
we need to create in silico replicas whose dynamics faithfully represent the dynamics of the underlying
biological system.
To date, the best in silico replicas of biological networks are RNNs trained to produce output that
matches recorded patterns of neuronal firing. While this approach is rapidly growing in popularity, it has
critical flaws. Current training methodologies are not constrained to produce accurate representations
of the underlying dynamics; in fact, RNNs are actually rewarded for inventing superfluous dynamics, so
long as those dynamics help to reproduce recorded neural data. Additionally, these models often
assume that the relationship (“embedding”) between latent activity and neural firing rates is linear; when
this assumption proves false, the dynamical accuracy suffers. The problems of superfluous dynamics
and non-linear embedding are especially severe when attempting to model a system of interacting
neural circuits.
The objective of this proposal is to develop a novel artificial neural network architecture that
addresses the above challenges and allows our in-silico models to capture accurate dynamics that are
built both within and across-circuits. My approach combines two key components: 1) neural ordinary
differential equations (NODEs), a computational architecture that we have demonstrated learns
dynamics more accurately and compactly than RNNs and 2) invertible neural network (INN) readouts,
which eliminate superfluous dynamics and allow the model to approximate nonlinear embeddings. I will
validate the ability of this model, called an Ordinary Differential equation auto-encoder with Invertible
readout (ODIN), to find accurate within- and across-circuit dynamics using synthetic neural data and
previously-collected multi-electrode recordings from monkeys. This tool will help to build a bridge
between neural data and both local and distributed neural computations.
摘要
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Christopher Versteeg其他文献
Christopher Versteeg的其他文献
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