Paolo di Prodi

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We present formal specification and verification of a robot moving in a complex network, using temporal sequence learning to avoid obstacles. Our aim is to demonstrate the benefit of using a formal approach to analyze such a system as a complementary approach to simulation. We first describe a classical closed-loop simulation of the system and compare this(More)
We introduce a new theoretical framework, based on Shan-non's communication theory and on Ashby's law of requisite variety, suitable for artificial agents using predictive learning. The framework quantifies the performance constraints of a predictive adaptive controller as a function of its learning stage. In addition, we formulate a practical measure,(More)
It has been shown that plasticity is not a fixed property but, in fact, changes depending on the location of the synapse on the neuron and/or changes of biophysical parameters. Here, we investigate how plasticity is shaped by feedback inhibition in a cortical microcircuit. We use a differential Hebbian learning rule to model spike-timing-dependent(More)
Spike-timing-dependent-plasticity (STDP)[1,2] is a special form of Hebbian learning [3] where the relative timing of post-and presynaptic activity determines the change in synaptic weight. More familiarly, the postsyn-aptic and presynaptic activity correspond respectively to the derivative of the membrane potential V m and the NMDA channel activation [4].(More)
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