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Free, publicly-accessible full text available December 10, 2025
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In recent years, many researchers have proposed new models for synaptic plasticity in the brain based on principles of machine learning. The central motivation has been the development of learning algorithms that are able to learn difficult tasks while qualifying as "biologically plausible". However, the concept of a biologically plausible learning algorithm is only heuristically defined as an algorithm that is potentially implementable by biological neural networks. Further, claims that neural circuits could implement any given algorithm typically rest on an amorphous concept of "locality" (both in space and time). As a result, it is unclear what many proposed local learning algorithms actually predict biologically, and which of these are consequently good candidates for experimental investigation. Here, we address this lack of clarity by proposing formal and operational definitions of locality. Specifically, we define different classes of locality, each of which makes clear what quantities cannot be included in a learning rule if an algorithm is to qualify as local with respect to a given (biological) constraint. We subsequently use this framework to distill testable predictions from various classes of biologically plausible synaptic plasticity models that are robust to arbitrary choices about neural network architecture. Therefore, our framework can be used to guide claims of biological plausibility and to identify potential means of experimentally falsifying a proposed learning algorithm for the brain.more » « less
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Abstract The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) is a 26-ton water Cherenkov neutrino detector installed on the Booster Neutrino Beam (BNB) at Fermilab. Its main physics goals are to perform a measurement of the neutron yield from neutrino-nucleus interactions, as well as a measurement of the charged-current cross section of muon neutrinos. An equally important focus is the research and development of new detector technologies and target media. Specifically, water-based liquid scintillator (WbLS) is of interest as a novel detector medium, as it allows for the simultaneous detection of Cherenkov light and scintillation. This paper presents the deployment of a 366 L WbLS vessel in ANNIE in March 2023 and the subsequent detection of both Cherenkov light and scintillation from the WbLS. This proof-of-concept allows for the future development of reconstruction and particle identification algorithms in ANNIE, as well as dedicated analyses within the WbLS volume, such as the search for neutral-current events and the hadronic scintillation component.
Free, publicly-accessible full text available May 1, 2025 -
Abstract Neutrinos from very nearby supernovae, such as Betelgeuse, are expected to generate more than ten million events over 10 s in Super-Kamokande (SK). At such large event rates, the buffers of the SK analog-to-digital conversion board (QBEE) will overflow, causing random loss of data that are critical for understanding the dynamics of the supernova explosion mechanism. In order to solve this problem, two new data-acquisition (DAQ) modules were developed to aid in the observation of very nearby supernovae. The first of these, the SN module, is designed to save only the number of hit photomultiplier tubes during a supernova burst and the second, the Veto module, prescales the high-rate neutrino events to prevent the QBEE from overflowing based on information from the SN module. In the event of a very nearby supernova, these modules allow SK to reconstruct the time evolution of the neutrino event rate from beginning to end using both QBEE and SN module data. This paper presents the development and testing of these modules together with an analysis of supernova-like data generated with a flashing laser diode. We demonstrate that the Veto module successfully prevents DAQ overflows for Betelgeuse-like supernovae as well as the long-term stability of the new modules. During normal running the Veto module is found to issue DAQ vetos a few times per month resulting in a total dead-time less than 1 ms, and does not influence ordinary operations. Additionally, using simulation data we find that supernovae closer than 800 pc will trigger the Veto module, resulting in a prescaling of the observed neutrino data.