Towards Artificial Language Learning in a Potts Attractor Network

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\u2014either individually and locally, or in a distributed fashion\u2014and 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\u2014intended to be a population of neurons (Hummel and Holyoak, 2003)\u2014and relevant constituents synchronously get activated in the construction of a sentence (or the inference of a proposition). Considering the productivity of the language\u2014the ability of humans to create many possible sentences out of a limited vocabulary\u2014this 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\u2014e.g. tensor products (Smolensky, 1990) or circular convolution (Plate, 1991)\u2014in 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 \u201duttered\u201d by the network. This last stage of spontaneous sentence production by the network has been explained in Chapter 4

Associative latching dynamics vs. syntax

We model the cortical dynamics underlying a free association between two memories. Computationally, this process may be realized as the spontaneous retrieval of a second memory after the recall of the first one by an external cue, what we call a latching transition. As a global cortical model, we study an associative memory Potts network with adaptive threshold, showing latching transitions. With many correlated stored patterns this unstable dynamics can proceed indefinitely, producing a sequence of spontaneously retrieved patterns. This paper describes the informational properties of latching sequences expressed by the Potts network, and compares them with those of the sentences comprising the corpus of a simple artificial language we are developing, BLISS. Potts network dynamics, unlike BLISS sentences, appear to have the memory properties of a second-order Markov chain

A super-resolution approach for receptive fields estimation of neuronal ensembles

International audienceThe Spike Triggered Average (STA) is a classical technique to find a discrete approximation of the Receptive Fields (RFs) of sensory neurons [1], a required analysis in most experimental studies. One important parameter of the STA is the spatial resolution of the estimation, corresponding to the size of the blocks of the checkerboard stimulus images. In general, it is experimentally fixed to reach a compromise: If too small, neuronal responses might be too weak thus leading to RF with low Signal-to-Noise-Ratio; on the contrary, if too large, small RF will be lost, or not described with enough details, because of the coarse approximation. Other solutions were proposed consisting in starting from a small block size and updating it following the neuron response in a closed-loop to increase its response [2; 3; 4]. However, these solutions were designed for single cells and cannot be applied to simultaneous recordings of ensembles of neurons (since each RF has its own size and preferred stimulus). To solve this problem, we introduced a modified checkerboard stimulus where blocks are shifted randomly in space at fixed time steps. This idea is inspired from super-resolution techniques developed in image processing [4]. The main interest is that the block size can be large, enabling strong responses, while the resolution can be finer since it depends on the shift minimum size. In [5] was shown that the STA remains an unbiased RF estimator and, using simulated spike trains from an ensemble of Linear Nonlinear Poisson cascade neurons, it was predicted that this approach improves RF estimation over the neuron ensemble. Here, we test these predictions experimentally on the RFs estimation of 8460 ganglion cells from two mouse retinas, using recordings performed with a large scale high-density multielectrode array. To illustrate the main interest of the approach, in Figure 1 we show a representative example of STA for one neuron where RFs have been obtained using the three following stimuli (all presented during 15min, for one retina displayed at 10 Hz, for the other at 30 Hz): (A) standard checkerboard stimulus with block size of 160μm, (B) standard checkerboard stimulus with block size of 40μm, (C) checkerboard stimulus with block size of 160μm and arbitrary shifts of 40μm in x and y-directions. Results show spatial resolution can be improved in case (C), while nothing could be obtained in (B) by changing only the block size of the standard stimulus. At the population level, plot (D) shows the number of the RFs that could be recovered for each stimuli, using a decision criteria based of the RFs value distribution. Most of the RFs were mapped with both methods (A) and (C) (49.9%). However, the proposed case (C) allows to recover 51% of the mapped RFs at a resolution of 40μm, while in the classical case (A), 41% of the RFs could be found at a resolution of only 160μm. Thus, the method does improve the quality of the RF estimation and the amount of successfully mapped RFs in neural ensembles

Unsupervised spike sorting for large-scale, high-density multielectrode arrays

electrophysiology; high-density multielectrode array; neural cultures; retina; spike sortin

BLISS: an artificial language for learnability studies

To explore neurocognitive mechanisms underlying the human language faculty, cognitive scientists use artificial languages to control more precisely the language learning environment and to study selected aspects of natural languages. Artificial languages applied in cognitive studies are usually designed ad hoc, to only probe a specific hypothesis, and they include a miniature grammar and a very small vocabulary. The aim of the present study is the construction of an artificial language incorporating both syntax and semantics, BLISS. Of intermediate complexity, BLISS mimics natural languages by having a vocabulary, syntax, and some semantics, as defined by a degree of non-syntactic statistical dependence between words. We quantify, using information theoretical measures, dependencies between words in BLISS sentences as well as differences between the distinct models we introduce for semantics. While modeling English syntax in its basic version, BLISS can be easily varied in its internal parametric structure, thus allowing studies of the relative learnability of different parameter sets