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.
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.
The project developing as expected and we are happy to report that we have substantially expanded the boundaries of the state of the art, such that our current methods are the most cutting-edge approaches to finding new plasticity rules. All Aims as described in the original grant have been fulfilled.