Autocorrelations from emergent bistability in homeostatic spiking neural networks on neuromorphic hardware
Metadatos
Mostrar el registro completo del ítemEditorial
American Physical Society
Fecha
2023-07-19Referencia bibliográfica
Cramer, B., Kreft, M., Billaudelle, S., Karasenko, V., Leibfried, A., Müller, E., ... & Zierenberg, J. (2023). Autocorrelations from emergent bistability in homeostatic spiking neural networks on neuromorphic hardware. Physical Review Research, 5(3), 033035.[DOI: 10.1103/PhysRevResearch.5.033035]
Patrocinador
European Union Sixth Framework Programme (FP6/2002-2006); Grant Agreement No. 15879 (FACETS); The European Union Seventh Framework Programme (FP7/2007-2013) under Grant Agreements No. 604102 (HBP),; No. 269921 (BrainScaleS); No. 243914 (Brain-i-Nets); The Horizon 2020 Framework Programme (H2020/2014-2020); Grant Agreements No. 720270; No. 785907; No. 945539 (HBP); the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy EXC 2181/1-390900948 (the Heidelberg STRUCTURES Excellence Cluster); The Helmholtz Association Initiative and Networking Fund [Advanced Computing Architectures (ACA)] under Project No. SO-092; the Helmholtz Association Initiative and Networking Fund [Advanced Computing Architectures (ACA)] under Project No. SO-092; The Spanish Ministry and Agencia Estatal de Investigación (AEI) through Project I+D+i (Reference No. PID2020-113681GBI00); MICIN/AEI/10.13039/501100011033 and FEDER “A way to make Europe; Consejería de Conocimiento, Investigación Universidad, Junta de Andalucía, and European Regional Development Fund; Project No. P20-00173; The Plan Propio de Investigación y Transferencia de la Universidad de Granada; Grant No. INST 39/963-1 FUGG (bwForCluster NEMOResumen
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