Davide Badoni

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We present a model for spike-driven dynamics of a plastic synapse, suited for aVLSI implementation. The synaptic device behaves as a capacitor on short timescales and preserves the memory of two stable states (efficacies) on long timescales. The transitions (LTP/LTD) are stochastic because both the number and the distribution of neural spikes in any finite(More)
Electronic neuromorphic devices with on-chip, on-line learning should be able to modify quickly the synaptic couplings to acquire information about new patterns to be stored (synaptic plasticity) and, at the same time, preserve this information on very long time scales (synaptic stability). Here, we illustrate the electronic implementation of a simple(More)
We describe the implementation and illustrate the learning performance of an analog VLSI network of 32 integrate-and-fire neurons with spike-frequency adaptation and 2016 Hebbian bistable spike-driven stochastic synapses, endowed with a self-regulating plasticity mechanism, which avoids unnecessary synaptic changes. The synaptic matrix can be flexibly(More)
LANN27 is an electronic device implementing in discrete electronics a fully connected (full feedback) network of 27 neurons and 351 plastic synapses with stochastic Hebbian learning. Both neurons and synapses are dynamic elements, with two time constants--fast for neurons and slow for synapses. Learning, synaptic dynamics, is analogue and is driven in a(More)
We describe and demonstrate a neuromorphic, analog VLSI chip (termed F-LANN) hosting 128 integrate-andfire (IF) neurons with spike-frequency adaptation, and 16,384 plastic bistable synapses implementing a self-regulated form of Hebbian, spike-driven, stochastic plasticity. The chip is designed to offer a high degree of reconfigurability: each synapse may be(More)
We illustrate key features of an analog, VLSI (aVLSI) chip implementing a network composed of 32 integrateand-fire (IF) neurons with firing rate adaptation (AHP current), endowed with both a recurrent synaptic connectivity and AER-based connectivity with external, AER-compliant devices. Synaptic connectivity can be reconfigured at will as to the(More)
We describe and discuss an electronic implementation of an attractor neural network with plastic synapses. The network undergoes double dynamics, for the neurons as well as the synapses. Both dynamical processes are unsupervised. The synaptic dynamics is autonomous, in that it is driven exclusively and perpetually by neural activities. The latter follow the(More)
Investigation at a φ–factory can shed light on several debated issues in particle physics. We discuss: i) recent theoretical development and experimental progress in kaon physics relevant for the Standard Model tests in the flavor sector, ii) the sensitivity we can reach in probing CPT and Quantum Mechanics from time evolution of entangled kaon states, iii)(More)
D. Babusci h, D. Badoni r,s, I. Balwierz-Pytko g, G. Bencivenni h, C. Bini p,q, C. Bloise h, F. Bossi h, P. Branchini u, A. Budano t,u, L. Caldeira Balkeståhl w, G. Capon h, F. Ceradini t,u, P. Ciambrone h, E. Czerwiński g, E. Danè h, E. De Lucia h, G. De Robertis b, A. De Santis p,q, A. Di Domenico p,q, C. Di Donato l,m, R. Di Salvo s, D. Domenici h, O.(More)