Emergence of Ramping Activity in Random Spiking Networks: A Biologically Plausible Model for Learning Timing

Simple item page
dc.contributor.advisorAlabastri, Alessandroen_US
dc.contributor.advisorShouval, Harelen_US
dc.creatorFeng, Boluen_US
dc.date.accessioned2025-05-30T21:11:16Zen_US
dc.date.available2025-05-30T21:11:16Zen_US
dc.date.created2025-05en_US
dc.date.issued2025-04-25en_US
dc.date.submittedMay 2025en_US
dc.date.updated2025-05-30T21:11:16Zen_US
dc.description.abstractThe ability to perceive and learn timing is crucial for animals to interact with the world. Experiments have shown that during motor planning, certain neurons in multiple brain regions exhibit slow ramping activity that gradually increases until an action begins. This ramping activity is believed to be the neural basis of timing. However, the mechanisms by which these ramping activities emerge from network dynamics and how they are learned through reinforcement learning remain poorly understood. We propose a spiking neural network model and a biologically realistic learning rule to learn this ramping behavior that can last on the order of seconds. Our model consists of a randomly connected recurrent neural network (RNN) that integrates input from a decision-making network. Synaptic connections are trained using an eligibility trace-based learning rule. Using mean-field theory, we analyze the RNN to determine the conditions necessary for it to function effectively as an integrator. This model qualitatively reproduces the ramping patterns observed in mouse brains during decision-making tasks at the single-neuron and population levels. Furthermore, an extended version of our model accounts for additional experimental findings in mouse decision-making tasks.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.urihttps://hdl.handle.net/1911/118534en_US
dc.language.isoenen_US
dc.subjectRamping activityen_US
dc.subjectSpiking neural networken_US
dc.subjectBiologically plausible learningen_US
dc.subjectNeural integratoren_US
dc.subjectMean-field theory analysisen_US
dc.titleEmergence of Ramping Activity in Random Spiking Networks: A Biologically Plausible Model for Learning Timingen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentApplied Physicsen_US
thesis.degree.disciplineApplied Physics/Electrical Eng, Applied Physics/Electrical Engen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Scienceen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
FENG-DOCUMENT-2025.pdf
Size:
11.17 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
PROQUEST_LICENSE.txt
Size:
5.84 KB
Format:
Plain Text
Description:
Download
No Thumbnail Available
Name:
LICENSE.txt
Size:
2.97 KB
Format:
Plain Text
Description:
Download
Collections
Rice University Theses and Dissertations