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SUMMARY:Computational Astrocyence: Astrocytic Learning in Spiking Neural Networks LOCATION:CBIM 22 DESCRIPTION;ENCODING=QUOTED-PRINTABLE:
Abstract:
Prevailing over a century, the neuronal paradigm of studying the brain has left us with limitations in both our un derstanding of how the brain processes information to achieve biological in telligence and how such knowledge is translated into artificial intelligenc e. Overturning our assumptions of how brain behavior emerges at the network level, the recent exploration of astrocytes, the most abundant yet long-ne glected non-neuronal brain cells, has ignited a revolution in our fundament al understanding of intelligence.
In this talk, I will present our on going effort to harness and nurture the learning ability of non-neuronal ce lls and unleash it into Brain-Inspired Computing. I will propose a biophysi cally realistic multi-compartmental model of an astrocyte whose activity re plicates recent experimental results. I will describe how we integrated the astrocytic model into spiking Neural-Astrocytic Networks (SNANs), extendin g learning beyond weight changes and introducing time as a learnable comput ational component in our networks. I will demonstrate how we used SNANs to both explore and exploit these fundamentally different learning abilities o f astrocytes, by introducing single-shot memories, overcoming the limitatio ns that spike timing dependent plasticity (STDP) rules have.
Specific ally, I will propose: 1) a non-linear, diffusion-based astrocytic mechanism for detecting and imposing neuronal synchronization (Polykretis et al. 201 8a); 2) an astrocytic mechanism for spatially constraining plasticity into functional groups of neuronal inputs (Polykretis et al. 2018b) and 3) an in hibitory astrocytic mechanism that may increase the contrast among active a nd inactive information pathways (Polykretis et al. 2019). I will conclude my talk by demonstrating spike-based single-shot pattern learning using SNA Ns in Intel’s Loihi, a neuromorphic research chip that is available in a ha ndful of research sites, including our Lab.
Overall, my research iden tifies possible mechanisms of astrocytic-induced neuronal synchronization a nd plasticity, adding a distinct computational layer that expands informati on processing beyond neuronal activity. Our results show preliminary eviden ce that, by inheriting their properties from their biological counterparts, SNANs can intrinsically tolerate noisy, unexpected data while introduce ne w efficient types of learning. This is a new research area towards function ally predominant SNANs that we have started to explore - and expand.
This research is supported by Rutgers's Brain Health Institute (BHI) Pilot Research Award.
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