# Understanding Multi-Layer Learning in a Biological Circuit

> **NIH NIH RF1** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2020 · $1,521,512

## Abstract

Work on learning in neural systems has focused largely on the effects of plasticity at synapses
that provide direct input to the neurons being studied. Learning a model of the environment or a
complex skill, however, relies on plasticity that is widely distributed and may occur at synapses
far from the neurons driving decisions or actions. As is well-known from multi-layer (or 'deep')
artificial networks, distributing learning over multiple layers is substantially more powerful but
also more difficult to implement than learning at a single layer. The fact that computer scientists
have solved such problems has revolutionized artificial intelligence and is rapidly reshaping the
human world. Understanding how the brain solves such problems is, undoubtedly, one of the
biggest challenges facing neuroscience today. However, progress along these lines has been
slow, due in part to the high degree of complexity of learning and memory circuits in mammals,
such as hippocampus and neocortex, that have been a major focus of research. This proposal
applies integrated experimental and theoretical approaches to a system with unique advantages
for understanding learning in multi-layer networks. The electrosensory lobe (ELL) of mormyrid
fish is the site of a continual learning process that predicts and cancels self-generated sensory
input in order to enhance detection of behaviorally-relevant stimuli. Building on this knowledge,
we propose to develop a model of the ELL spanning from cellular biophysics to network
dynamics with the goal of explaining how synaptic plasticity widely distributed across processing
layers and cell types gives rise to learning. To accomplish this, we will leverage cutting-edge
approaches for mapping synaptic connectivity at high-resolution and monitoring neural
population activity over the entire time course of learning. The proposed research is expected to
yield general insight into how sophisticated forms of learning are implemented in neural circuits.

## Key facts

- **NIH application ID:** 10053457
- **Project number:** 1RF1NS118448-01
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Laurence F. Abbott
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $1,521,512
- **Award type:** 1
- **Project period:** 2020-09-15 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10053457

## Citation

> US National Institutes of Health, RePORTER application 10053457, Understanding Multi-Layer Learning in a Biological Circuit (1RF1NS118448-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10053457. Licensed CC0.

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