Implementation and predictions of the Blackboard Architecture in biologically inspired artificial networks

An important challenge in neurolinguistics is to understand the parsing process by which words are combined into larger constituents during sentence understanding. Few attempts have been made to model parsing with biological neural networks. The Neural Blackboard Architecture (NBA), proposed by Van der Velde and De Kamps(Velde 2006) is one of them. It was designed to answer many challenges in the neural modeling of sentence processing, including the ones detailed by Jackendoff(Jackendoff 2002). Illustration of the abstract circuit specification given by the NBA, to represent and parse utterance into tree structures, is shown in Figure \ref{Blackboard}. Here we expand on previous simulations of the Blackboard Architecture(Velde 2015) on leaky-integrage-and-fire (LIF) populations with population density techniques(de Kamps 2013) implemented in MIIND(Kamps 2008), to compare simulated time courses of neural activity associated to sentence parsing with functional magnetic resonance data (fMRI) and intracranial recordings (electro-corticography; ECOG).

Our simulations suggest that the neural dynamics of the simulated circuit of LIF neural populations, without tuning of the circuit parameters, already approximate the qualitative behavior of several neuroimaging measurements. In the case of Bold-fMRI measurements, we considered the work of Pallier et al(citation not found: Pallier_2011). Manipulating the size of constituents in sequences of words presented visually to participants, they observed a sublinear increase of the amplitude of hemodynamic responses in language related regions as a function of constituent size. We confirmed that simulating the same phrases under a phrase grammar theory with a simple bottom-up parsing scheme leads to the mentioned pattern. WHAT ABOUT UNIVERSAL DEPENDENCIES In Figure \ref{pnas} we portray the simulated neural time courses and their respective hemodynamics. We also show how our obtained amplitudes compare with those in a region of the posterior superior temporal sulcus regions (pSTS). In the case of ECOG recordings, recent work from Nelson et al. (under review), provides evidence the the Local Field Potentials have increase with phrase constituent size and drop after binding of words into constituents. We confirm both qualitative properties from a preliminary simulation of right branched phrases of increasing number of words. In Figure \ref{nelson} we show the contrasted neural time courses of our simulation and a plot taken from Nelson et al.(under review). We also depict the LIF neural populations time courses inside a compartment circuit of the Neural Blackboard Architecture that originates the sudden drops of neural activity during constituent binding.

Currently we are developing a systematic link between the parameters of the implemented circuit and the measurements to be able to make quantitative predictions to investigate natural clusters of neural activity of phrases taken from a corpus. We hope to determine candidate sets of maximally different phrases under diverse grammar theories and parsing schemes to compare these theories in posterior experiments.

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