@misc{10481/84225,
year = {2023},
month = {7},
url = {https://hdl.handle.net/10481/84225},
abstract = {A fruitful approach towards neuromorphic computing is to mimic mechanisms of the brain in physical devices,
which has led to successful replication of neuronlike dynamics and learning in the past. However, there remains a
large set of neural self-organization mechanisms whose role for neuromorphic computing has yet to be explored.
One such mechanism is homeostatic plasticity, which has recently been proposed to play a key role in shaping
network dynamics and correlations. Here, we study—from a statistical-physics point of view—the emergent
collective dynamics in a homeostatically regulated neuromorphic device that emulates a network of excitatory
and inhibitory leaky integrate-and-fire neurons. Importantly, homeostatic plasticity is only active during the
training stage and results in a heterogeneous weight distribution that we fix during the analysis stage. We verify
the theoretical prediction that reducing the external input in a homeostatically regulated neural network increases
temporal correlations, measuring autocorrelation times exceeding 500 ms, despite single-neuron timescales of
only 20ms, both in experiments on neuromorphic hardware and in computer simulations. However, unlike
theoretically predicted near-critical fluctuations, we find that temporal correlations can originate from an
emergent bistability.We identify this bistability as a fluctuation-induced stochastic switching between metastable
active and quiescent states in the vicinity of a nonequilibrium phase transition. Our results thereby constitute a
complementary mechanism for emergent autocorrelations in networks of spiking neurons with implications for
future developments in neuromorphic computing},
organization = {European
Union Sixth Framework Programme (FP6/2002-2006)},
organization = {Grant Agreement No. 15879 (FACETS)},
organization = {The European
Union Seventh Framework Programme (FP7/2007-2013) under
Grant Agreements No. 604102 (HBP),},
organization = {No. 269921
(BrainScaleS)},
organization = {No. 243914 (Brain-i-Nets)},
organization = {The Horizon
2020 Framework Programme (H2020/2014-2020)},
organization = {Grant Agreements No. 720270},
organization = {No. 785907},
organization = {No. 945539
(HBP)},
organization = {the Deutsche Forschungsgemeinschaft (DFG, German
Research Foundation) under Germany’s Excellence Strategy
EXC 2181/1-390900948 (the Heidelberg STRUCTURES
Excellence Cluster)},
organization = {The Helmholtz Association Initiative
and Networking Fund [Advanced Computing Architectures
(ACA)] under Project No. SO-092},
organization = {the Helmholtz Association Initiative
and Networking Fund [Advanced Computing Architectures
(ACA)] under Project No. SO-092},
organization = {The Spanish
Ministry and Agencia Estatal de Investigación (AEI)
through Project I+D+i (Reference No. PID2020-113681GBI00)},
organization = {MICIN/AEI/10.13039/501100011033 and
FEDER “A way to make Europe},
organization = {Consejería
de Conocimiento, Investigación Universidad, Junta de Andalucía,
and European Regional Development Fund},
organization = {Project
No. P20-00173},
organization = {The Plan Propio
de Investigación y Transferencia de la Universidad de
Granada},
organization = {Grant No. INST 39/963-1 FUGG (bwForCluster NEMO},
publisher = {American Physical Society},
title = {Autocorrelations from emergent bistability in homeostatic spiking neural networks on neuromorphic hardware},
doi = {10.1103/PhysRevResearch.5.033035},
author = {Cramer, Benjamin and Muñoz Martínez, Miguel Ángel},
}