Search Talks

Oscillatory phenomena are ubiquitous in the brain. Although there are oscillator-based models of brain dynamics, they do not seem to enjoy the universal computational properties of rate-coded and spiking neuron network models. Use of oscillator-based models is often limited to special phenomena like locomotor rhythms and oscillatory attractor-based memories. If neuronal ensembles are taken to be the basic functional units of brain dynamics, it is desirable to develop oscillator-based models that can explain a wide variety of neural phenomena. To this end, we aim to develop a generalized network of oscillatory neurons. Specifically we propose a novel neural network architecture consisting of Hopf oscillators described in the complex domain. The oscillators can adapt their intrinsic frequencies by tracking the frequency components of the input signals. The oscillators are also laterally connected with each other through a special form of coupling we labeled as “power coupling”. Power coupling allows two oscillators with arbitrarily different intrinsic frequencies to interact at a constant normalized phase difference. The network can be operated in two phases. In the encoding phase the oscillators comprising the network perform a Fourier-like decomposition of the input signal(s). In the reconstruction phase, outputs the trained oscillators are combined to reconstruct the training signals. As a salient example, the network can be trained to reconstruct Electroencephalogram (EEG) signals, paving the way to an exciting class of large scale brain models.

Birdsong is a complex motor activity that arises from the interaction between the central and peripheral nervous systems, the body, and the environment. Its striking similarities to human speech, both in production and learning, make songbirds powerful animal models for studying learned motor skills. In this talk, I will present an interdisciplinary approach to understanding the emergence of behavior. Specifically, I will show neuronal recordings from a telencephalic region involved in sensorimotor integration, revealing well-defined oscillations in local field potentials synchronized with the rhythmic structure of canary (Serinus canaria) song. I will introduce a low-dimensional mathematical model of a neural network that replicates the neural dynamics observed in the experiments. This work highlights the value of low-dimensional models as tools for exploring the neuroscience of perception and generation of complex motor behaviors.

Nonlinear dynamics aims to elucidate the basic mechanisms necessary to reflect the temporal behavior of a natural system. The data analysis and modeling techniques proposed by artificial intelligence (deep networks, computational reservoirs, recurrent networks, as examples), on the other hand, ostensibly resign the mechanistic vision for a data-oriented modeling paradigm.  In these lectures, these apparently antagonistic approaches will be analyzed in parallel, using as examples my work on the physics of birdsong production and vocal learning.

The next generation of extragalactic surveys will reveal the full diversity of the galaxy assembly process: from environment-dependent evolution to the build-up of mass inside galaxies, and with a complete accounting of all relevant processes/constituents ensured by multi-wavelength coverage. Observations at radio wavelengths carry a unique potential in that they can probe star-formation activity and cold gas content, i.e. place constraints on both galaxy growth rates and the fuel for future galaxy growth. With the construction of the Square Kilometre Array (SKA), radio astronomy will enter a new era; high-sensitivity, high-resolution and dust-unbiased radio imaging will provide a confusion-free census of star formation and black hole activity, regardless of whether these occur in dust-obscured or optically thin regions. I will review the high-priority science cases developed by the SKA extragalactic continuum science working group and outline the challenges brought about by our still only partial understanding of some of the astrophysical mechanisms producing radio emission (e.g. the relation between star formation rate and radio continuum luminosity).
Thanks to an order of magnitude increase in survey speed compared to existing radio telescopes, SKA surveys will detect millions of galaxies. I will summarize how - in addition to advancing our understanding of the drivers of the cosmic star-formation history - commensal survey design will ensure that information on galaxy shapes, redshifts, spatial distribution and polarization can be used for cosmological studies and to trace the evolution of magnetic fields in galaxies and the cosmic web.