Bidirectional interaction between visual and motor generative models using Predictive Coding and Active Inference

Chaotic recurrent neural networks for brain modelling: A review

Even in the absence of external stimuli, the brain is spontaneously active. Indeed, most cortical activity is internally generated by recurrence. Both theoretical and experimental studies suggest that chaotic dynamics characterize this spontaneous activity. While the precise function of brain chaotic activity is still puzzling, we know that chaos confers many advantages. From a computational perspective, chaos enhances the complexity of network dynamics. From a behavioural point of view, chaotic activity could generate the variability required for exploration. Furthermore, information storage and transfer are maximized at the critical border between order and chaos. Despite these benefits, many computational brain models avoid incorporating spontaneous chaotic activity due to the challenges it poses for learning algorithms. In recent years, however, multiple approaches have been proposed to overcome this limitation. As a result, many different algorithms have been developed, initially within the reservoir computing paradigm. Over time, the field has evolved to increase the biological plausibility and performance of the algorithms, sometimes going beyond the reservoir computing framework. In this review article, we examine the computational benefits of chaos and the unique properties of chaotic recurrent neural networks, with a particular focus on those typically utilized in reservoir computing. We also provide a detailed analysis of the algorithms designed to train chaotic RNNs, tracing their historical evolution and highlighting key milestones in their development. Finally, we explore the applications and limitations of chaotic RNNs for brain modelling, consider their potential broader impacts beyond neuroscience, and outline promising directions for future research.

Writing letters in two graphic systems: Behavioral and neural correlates in Latin-Arabic biscripters

Biscriptuality is the ability to read and write using two scripts. Despite the increasing number of biscripters, this phenomenon remains poorly understood. Here, we focused on investigating graphomotor processing in French-Arabic biscripters. We chose the French and Arabic alphabets because they have comparable visuospatial complexity and linguistic features, but differ dramatically in their graphomotor characteristics. In a first experiment we describe the graphomotor features of the two alphabets and showed that while Arabic and Latin letters are produced with the same velocity and fluency, Arabic letters require more pen lifts, contain more right-to-left strokes and clockwise curves, and take longer to write than Latin letters. These results suggest that Arabic and Latin letters are produced via different motor patterns. In a second experiment we used functional magnetic resonance imaging to ask whether writing the two scripts relies upon partially distinct or fully overlapping neural networks, and whether the elements of the previously described handwriting network are recruited to the same extent by the two scripts. We found that both scripts engaged the so-called "writing network", but that within the network, Arabic letters recruited the left superior parietal lobule (SPL) and the left primary motor cortex (M1) more strongly than Latin letters. Both regions have previously been identified as holding scale-invariant representations of letter trajectories. Arabic and Latin letters also activated distinct regions that do not belong to the writing network. Complementary analyses indicate that the differences observed between scripts at the neural level could be driven by the specific graphomotor features of each script. Overall, our results indicate that particular features of the practiced scripts can lead to different motor organization at both the behavioral and brain levels in biscripters.

Turn-Taking Mechanisms in Imitative Interaction: Robotic Social Interaction Based on the Free Energy Principle

This study explains how the leader-follower relationship and turn-taking could develop in a dyadic imitative interaction by conducting robotic simulation experiments based on the free energy principle. Our prior study showed that introducing a parameter during the model training phase can determine leader and follower roles for subsequent imitative interactions. The parameter is defined as w, the so-called meta-prior, and is a weighting factor used to regulate the complexity term versus the accuracy term when minimizing the free energy. This can be read as sensory attenuation, in which the robot's prior beliefs about action are less sensitive to sensory evidence. The current extended study examines the possibility that the leader-follower relationship shifts depending on changes in w during the interaction phase. We identified a phase space structure with three distinct types of behavioral coordination using comprehensive simulation experiments with sweeps of w of both robots during the interaction. Ignoring behavior in which the robots follow their own intention was observed in the region in which both ws were set to large values. One robot leading, followed by the other robot was observed when one w was set larger and the other was set smaller. Spontaneous, random turn-taking between the leader and the follower was observed when both ws were set at smaller or intermediate values. Finally, we examined a case of slowly oscillating w in anti-phase between the two agents during the interaction. The simulation experiment resulted in turn-taking in which the leader-follower relationship switched during determined sequences, accompanied by periodic shifts of ws. An analysis using transfer entropy found that the direction of information flow between the two agents also shifted along with turn-taking. Herein, we discuss qualitative differences between random/spontaneous turn-taking and agreed-upon sequential turn-taking by reviewing both synthetic and empirical studies.

Initialization of latent space coordinates via random linear projections for learning robotic sensory-motor sequences

Robot kinematic data, despite being high-dimensional, is highly correlated, especially when considering motions grouped in certain primitives. These almost linear correlations within primitives allow us to interpret motions as points drawn close to a union of low-dimensional affine subspaces in the space of all motions. Motivated by results of embedding theory, in particular, generalizations of the Whitney embedding theorem, we show that random linear projection of motor sequences into low-dimensional space loses very little information about the structure of kinematic data. Projected points offer good initial estimates for values of latent variables in a generative model of robot sensory-motor behavior primitives. We conducted a series of experiments in which we trained a Recurrent Neural Network to generate sensory-motor sequences for a robotic manipulator with 9 degrees of freedom. Experimental results demonstrate substantial improvement in generalization abilities for unobserved samples during initialization of latent variables with a random linear projection of motor data over initialization with zero or random values. Moreover, latent space is well-structured such that samples belonging to different primitives are well separated from the onset of the training process.

Digital computing through randomness and order in neural networks

We propose that coding and decoding in the brain are achieved through digital computation using three principles: relative ordinal coding of inputs, random connections between neurons, and belief voting. Due to randomization and despite the coarseness of the relative codes, we show that these principles are sufficient for coding and decoding sequences with error-free reconstruction. In particular, the number of neurons needed grows linearly with the size of the input repertoire growing exponentially. We illustrate our model by reconstructing sequences with repertoires on the order of a billion items. From this, we derive the Shannon equations for the capacity limit to learn and transfer information in the neural population, which is then generalized to any type of neural network. Following the maximum entropy principle of efficient coding, we show that random connections serve to decorrelate redundant information in incoming signals, creating more compact codes for neurons and therefore, conveying a larger amount of information. Henceforth, despite the unreliability of the relative codes, few neurons become necessary to discriminate the original signal without error. Finally, we discuss the significance of this digital computation model regarding neurobiological findings in the brain and more generally with artificial intelligence algorithms, with a view toward a neural information theory and the design of digital neural networks.

Continual Sequence Modeling With Predictive Coding

Recurrent neural networks (RNNs) have been proved very successful at modeling sequential data such as language or motions. However, these successes rely on the use of the backpropagation through time (BPTT) algorithm, batch training, and the hypothesis that all the training data are available at the same time. In contrast, the field of developmental robotics aims at uncovering lifelong learning mechanisms that could allow embodied machines to learn and stabilize knowledge in continuously evolving environments. In this article, we investigate different RNN designs and learning methods, that we evaluate in a continual learning setting. The generative modeling task consists in learning to generate 20 continuous trajectories that are presented sequentially to the learning algorithms. Each method is evaluated according to the average prediction error over the 20 trajectories obtained after complete training. This study focuses on learning algorithms with low memory requirements, that do not need to store past information to update their parameters. Our experiments identify two approaches especially fit for this task: conceptors and predictive coding. We suggest combining these two mechanisms into a new proposed model that we label PC-Conceptors that outperforms the other methods presented in this study.