WHEN: April 23 at 11:00.
WHERE: Room s208 (-2 floor), Omega building(Campus Nord of UPC).

Biological neural networks exhibit ongoing, spatiotemporal patterns of spiking activity. Evidence shows that spike dynamics shift from one transient attractor to another, i.e. they appear to be metastable. Metastability is theorized to be adaptive for neural and cognitive function, but learning must somehow remain stable in the context of highly variable spike dynamics. Stable learning is challenging in part because it appears that functions of homeostatic regulation and learning are both expressed through potentiation and de-potentiation of synapses.  In this talk, Prof. Kello will present a spiking neural network model that integrates homeostatic regulation with learning via a local, biological plausible process of synaptic modulation.  Homeostatic regulation towards the critical branching point results in power law spike dynamics, while learning shapes those dynamics to maximize reward and minimize punishment.  The model is shown to simulate intrinsic fluctuations in neural and behavioral activity, and the efficacy of learning is demonstrated using time-delayed XOR classification as a simple test function, and real-time phoneme recognition in naturalistic speech as a more challenging test.