Advances in birdsong modeling
« previous post | next post »
Eve Armstrong and Henry Abarbanel, "Model of the songbird nucleus HVC as a network of central pattern generators", Journal of neurophysiology, 2016:
We propose a functional architecture of the adult songbird nucleus HVC in which the core element is a "functional syllable unit" (FSU). In this model, HVC is organized into FSUs, each of which provides the basis for the production of one syllable in vocalization. Within each FSU, the inhibitory neuron population takes one of two operational states: (A) simultaneous firing wherein all inhibitory neurons fire simultaneously, and (B) competitive firing of the inhibitory neurons. Switching between these basic modes of activity is accomplished via changes in the synaptic strengths among the inhibitory neurons. The inhibitory neurons connect to excitatory projection neurons such that during state (A) the activity of projection neurons is suppressed, while during state (B) patterns of sequential firing of projection neurons can occur. The latter state is stabilized by feedback from the projection to the inhibitory neurons. Song composition for specific species is distinguished by the manner in which different FSUs are functionally connected to each other.
Ours is a computational model built with biophysically based neurons. We illustrate that many observations of HVC activity are explained by the dynamics of the proposed population of FSUs, and we identify aspects of the model that are currently testable experimentally. In addition, and standing apart from the core features of an FSU, we propose that the transition between modes may be governed by the biophysical mechanism of neuromodulation.
I'm really happy to see an exploration of biologically plausible frameworks for modeling the generation of serially-ordered behavior, of which birdsong and speech are among the best studied. And I also wonder whether models of this type offer an insightful way to approach the simple issues discussed in "Modeling repetitive behavior", 5/15/2015
But I stumbled on the cited paper by looking for other recent work by the author of a research report posted to arXiv.org just in time for the first of April: Eve Armstrong, "A Neural Networks Approach to Predicting How Things Might Have Turned Out Had I Mustered the Nerve to Ask Barry Cottonfield to the Junior Prom Back in 1997". This brilliant work contains many insightful passages, e.g.
Artificial neural networks (ANNs) are a type of machine learning algorithm in which a neurobiology-inspired architecture is created, via exposure to training data, to mimic the capability of a real brain to categorize information (e.g. Hopfield 1988, Jain & Mao 1996). This artificial brain, while astoundingly stupid by human standards (and by fruit-fly and sea-slug standards, incidentally), can nevertheless learn to be stupid rather quickly and thereafter serve as a powerful tool for particular predictive purposes.
The same search also uncovered "Non-detection of the Tooth Fairy at Optical Wavelengths".
So happy April 1, everyone.