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

Periodic Reporting for period 2 - SynapSeek (Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning.)

Reporting period: 2020-08-01 to 2022-01-31

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 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.

Here, we propose a new approach. By utilizing the growing power of machine learning methods we can deduce synaptic plasticity rules directly. Newly developed search algorithms and sheer computational power allow us to integrate published data and infer synaptic rules in silico. We aim to (1) develop a new mathematical expression of synaptic plasticity rules, experimentally appropriate and flexible enough to be implemented in a Machine Learning framework, dubbed SYNAPSEEK. Next (2), we will apply SYNAPSEEK to deduce the rules for building various neural structures with increasing complexity. Finally (3), we will incorporate additional constraints to SYNAPSEEK to develop synaptic rules that shape network function as much as its structure. Our work will establish, for the first time, canonical sets of synaptic plasticity rules, based on the circuit structure they must produce, and the function they are meant to support. SYNAPSEEK will have immediate and wide ranging applications, from a basic understanding of cortical development to better protocols for Deep Brain Stimulation.
The project is proceeding very much as envisioned and we are making good progress on the realisation of Aims 1 & 2. In particular, we have seen the rapid development and testing of a framework to analyse and modify synaptic plasticity rules with ML methods (Aim 1.1 and 1.3). Further, we have begun to test and expand our techniques to infer plasticity rules in spiking networks as well, i.e. we started to fulfil the expectations of Aim 2.1 as scheduled. Our work up to the end of period 1 has led to 5 poster presentations (NCCD’19, COSYNE ’20, Neuromatch ’20, IST retreat ’20, Oxford Neuroscience symposium ’19). Additionally, we have submitted a manuscript for publication to NEURIPS 20, (which was accepted in Period 2, at the time of writing this report). Since the presentation of the NeurIPS poster, we have started new work, on Aims 1-3, including a more expansive characterisation of plasticity rules, including their expression as polynomials and also neural networks themselves. After the lab's move, we have successfully hired two graduate students, and a postdoc is about to arrive. We have submitted two more abstracts to Cosyne 2022, for which we are currently awaiting the decision. Finally, we have just been asked to provide insight in the form of a commentary for a related paper in the journal NEURON.
The project is 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. We are very optimistic that most of the Aims as described in the original grant can be fulfilled. We have experienced a slow down in progress due to covid and the relocation of the lab, but as of January 2022 we are picking up speed in Austria and making good progress on several fronts.