A Hierarchical Neuronal Model for Generation and Online Recognition of Birdsongs

Abstract

The neuronal system underlying learning, generation and recognition of song in birds is one of the best-studied systems in the neurosciences. Here, we use these experimental findings to derive a neurobiologically plausible, dynamic, hierarchical model of birdsong generation and transform it into a functional model of birdsong recognition. The generation model consists of neuronal rate models and includes critical anatomical components like the premotor song-control nucleus HVC (proper name), the premotor nucleus RA (robust nucleus of the arcopallium), and a model of the syringeal and respiratory organs. We use Bayesian inference of this dynamical system to derive a possible mechanism for how birds can efficiently and robustly recognize the songs of their conspecifics in an online fashion. Our results indicate that the specific way birdsong is generated enables a listening bird to robustly and rapidly perceive embedded information at multiple time scales of a song. The resulting mechanism can be useful for investigating the functional roles of auditory recognition areas and providing predictions for future birdsong experiments.

Predictive Ensemble Decoding of Acoustical Features Explains Context-Dependent Receptive Fields

“…However, it has been rarely used to explain neuronal phenomena in the auditory domain (Turner and Sahani, 2007; Ramirez et al, 2011; Yildiz and Kiebel, 2011; Wacongne et al, 2012; Yildiz et al, 2013) and, as far as we know, has never been used for predicting activity at a single-neuron level as achieved here.…”

Sequences by Metastable Attractors: Interweaving Dynamical Systems and Experimental Data

“…For the neural field simulation, HO with multiple time scales as discussed by Yildiz and Kiebel [14] and beim Graben and Hutt [36] may be suitable to avoid alignment artifacts between MS.…”

“…Hence our simulation entails a desynchronization in comparison with the experimentally observed ERP dynamics. The reason for this deviation is the presence of only one time scale in the underlying dynamic model, reflected by the growth rates of neural populations in the Lotka-Volterra Equation (14) that are all of the same order of magnitude. Hence, the phasic MS 7 is not appropriately captured by our phenomenological model.…”

“…In recent years, several experimental studies of biological systems have revealed that the systems' activities evolve between some equilibria [10][11][12]. Such HOs between MS may occur in neural responses in the insect olfactory bulb [13], in bird songs [14], in human scalp brain activity at rest [15], during cognitive tasks [16,17], in schizophrenia [18] and during emergence from unconsciousness [19]. HOs are even hypothesized to represent a coding scheme in neural systems [1] termed chaotic itinerancy in this context [20,21].…”

“…For instance, in human cognitive neuroscience experiments measuring electroencephalographic activity (EEG), these MS are the so-called event-related components reflecting specific neural processing tasks during cognition [22,25]. For bird songs, MS are firing states of neural cell assemblies [14].…”