It remains a mystery how children acquire natural languages; languages far beyond the few symbols that a young chimp struggles to learn, and with complex rules that incomparably surpass the repetitive structure of bird songs. How should one explain the emergence of such a capacity from the basic elements of the nervous system, namely neuronal networks? To understand the brain mechanisms underlying the language phenomenon, specifically sentence construction, different approaches have been attempted to implement an artificial neural network that encodes words and constructs sentences (see e.g. (Hummel, J.E. and Holyoak, 1997; Huyck, 2009; Velde and de Kamps, 2006; Stewart and Eliasmith, 2009)). These attempts differ on how the sentence constituents (parts) are represented—either individually and locally, or in a distributed fashion—and on how these constituents are bound together. In LISA (Hummel, J.E. and Holyoak, 1997), each sentence constituent (either a word, a phrase, or even a proposition) is represented individually by a unit—intended to be a population of neurons (Hummel and Holyoak, 2003)—and relevant constituents synchronously get activated in the construction of a sentence (or the inference of a proposition). Considering the productivity of the language—the ability of humans to create many possible sentences out of a limited vocabulary—this representation results in an exponential growth in the number of units needed for structure representation. In order to avoid this problem, Neural Blackboard Architectures (Velde and de Kamps, 2006) were proposed as systems endowed with dynamic bindings between assemblies of words, roles (e.g. theme or agent), and word categories (e.g. nouns or verbs). A neural blackboard architecture resembles a switchboard (a blackboard) that wires sentence constituents together via circuits, using highly complex and meticulously (unrealistic) organized connections. As opposed to localized approaches, in a Vector Symbolic Architecture (Gayler, 2003; Plate, 1991), words are represented in a fully distributed fashion on a vector. The words are bound (and merged) together by algebraic operations—e.g. tensor products (Smolensky, 1990) or circular convolution (Plate, 1991)—in the vector space. In order to give a biological account, some steps have been attempted towards the neural implementation of such operations (Stewart and Eliasmith, 2009). Another distributed approach was toward implementing a simple recurrent neural network that predicts the next word in a sentence (Elman, 1991). Apart from the limited language size that the network could deal with (Elman, 1993), this system lacked an explicit representation of syntactic constituents, thus resulting in a lack of grammatical knowledge in the network (Borensztajn, 2011; Velde and de Kamps, 2006). However, despite all these attempts, there remains the lack of a neural model that addresses the challenges of language size, semantic and syntactic distinction, word binding, and word implementation in a neurally plausible manner. We are exploring a novel approach to address these challenges, that involves first constructing an artificial language of intermediate complexity and then implementing a neural network, as a simplified cortical model of sentence production, which stores the vocabulary and the grammar of the artificial language in a neurally inspired manner on two components: one semantic and one syntactic. As the training language of the network, we have constructed BLISS (Pirmoradian and Treves, 2011), a scaled-down synthetic language of intermediate complexity, with about 150 words, 40 production rules, and a definition of semantics that is reduced to statistical dependence between words. In Chapter 2, we will explain the details of the implementation of BLISS. As a sentence production model, we have implemented a Potts attractor neural network, whose units hypothetically represent patches of cortex. The choice of the Potts network, for sentence production, has been mainly motivated by the latching dynamics it exhibits (Kropff and Treves, 2006); that is, an ability to spontaneously hop, or latch, across memory patterns, which have been stored as dynamical attractors, thus producing a long or even infinite sequence of patterns, at least in some regimes (Russo and Treves, 2012). The goal is to train the Potts network with a corpus of sentences in BLISS. This involves setting first the structure of the network, then the generating algorithm for word representations, and finally the protocol to train the network with the specific transitions present in the BLISS corpus, using both auto- and hetero-associative learning rules. In Chapter 3, we will explain the details of the procedure we have adapted for word representation in the network. The last step involves utilizing the spontaneous latching dynamics exhibited by the Potts network, the word representation we have developed, and crucially hetero-associative weights favouring specific transitions, to generate, with a suitable associative training procedure, sentences ”uttered” by the network. This last stage of spontaneous sentence production by the network has been explained in Chapter 4.
|Titolo:||Towards Artificial Language Learning in a Potts Attractor Network|
|Data di pubblicazione:||28-gen-2013|
|Appare nelle tipologie:||8.1 PhD thesis|