Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning.

Informations projet

SynapSeek

N° de convention de subvention: 819603

DOI

10.3030/819603

Projet clôturé

Date de signature de la CE 16 Mai 2019

Date de début 1 Juin 2019

Date de fin 31 Mai 2025

Financé au titre de

EXCELLENT SCIENCE - European Research Council (ERC)

Coût total

€ 1 798 605,00

Contribution de l’UE

€ 1 798 605,00

1 798 605,00

Coordonné par

INSTITUTE OF SCIENCE AND TECHNOLOGY AUSTRIA
Austria

Periodic Reporting for period 5 - SynapSeek (Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning.)

Période du rapport: 2024-06-01 au 2025-05-31

How do we learn to dance, play an instrument, or a game as complex as chess or go? How do we make a memory? The common answer to these questions is “through synaptic plasticity”, through changing the synaptic connectivity of neural circuits so that representative brain activity can be reliably triggered. Such connectivity changes are governed by rules, i.e. synaptic mechanisms which monitor the activity of their environment and stereotypically strengthen or weaken synapses accordingly. The shape and mode of operation of these rules in biological systems is still largely unknown: For the more than hundred different connection types in cortical circuits, only a handful of rules has been described at all. Similarly, testing observed rules in simulations of cortical function has only seen limited success. Our slow progress is due to the extraordinary difficulty of measuring and observing synapses without interference.
With “SYNAPSEEK”, we tried a new approach. We utilized the power of machine learning methods to deduce synaptic plasticity rules directly. Newly developed search algorithms and sheer computational power allowed us to integrate published data and infer synaptic rules in silico. We (1) developed a new mathematical expression of synaptic plasticity rules, experimentally appropriate and flexible enough to be implemented in a Machine Learning framework, dubbed SYNAPSEEK. We then (2) applied SYNAPSEEK to deduce the rules for building various neural structures with increasing complexity. Finally (3), we incorporated additional, experimental constraints to SYNAPSEEK to develop synaptic rules that shape network function as much as its structure. Our work produced, for the first time, thousands of self-consistent sets of synaptic plasticity rules, based on the circuit structure they must produce, and the function they are meant to support. SYNAPSEEK promises wide ranging applications, from a basic understanding of cortical development to better protocols for Deep Brain Stimulation.

The project hit major obstacles at about half-time of the grant, thanks to daunting computational and technical challenges. With the help of 4 very hard-working graduate students, we were able to overcome these challenges and ended up achieving more than we had envisioned, much beyond the realization of the Aims. In particular, we have seen the success of our framework in analysing and modifying synaptic plasticity rules with ML methods (Aim 1). We tested and expanded our techniques to infer plasticity rules in spiking networks as well, i.e. we fulfill the expectations of Aim 2 and 3, as hoped. Our work up to the end of period 4 has led to 25 poster presentations (NCCD’19, COSYNE ’20,21,22,23,24,25 BCCN 24-25, Neuromatch ’20, CNS2023, NeurIPS20,23, IST retreat ’20, Oxford Neuroscience symposium’19). Additionally, we have 7 manuscripts at NEURIPS 20,23,25, eLife, Plos Comp.Neurosc. and others. We have now started new work based on Aim 3, including experimental data to constrain our automated search methods.

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Periodic Reporting for period 5 - SynapSeek (Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning.)

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