Interactive reservoir computing for chunking information streams

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.

Energy-Efficient Sleep Apnea Detection Using a Hyperdimensional Computing Framework Based on Wearable Bracelet Photoplethysmography

OBJECTIVE

Sleep apnea syndrome (SAS) is a common sleep disorder, which has been shown to be an important contributor to major neurocognitive and cardiovascular sequelae. Considering current diagnostic strategies are limited with bulky medical devices and high examination expenses, a large number of cases go undiagnosed. To enable large-scale screening for SAS, wearable photoplethysmography (PPG) technologies have been used as an early detection tool. However, existing algorithms are energy-intensive and require large amounts of memory resources, which are believed to be the major drawbacks for further promotion of wearable devices for SAS detection.

METHODS

In this paper, an energy-efficient method of SAS detection based on hyperdimensional computing (HDC) is proposed. Inspired by the phenomenon of chunking in cognitive psychology as a memory mechanism for improving working memory efficiency, we proposed a one-dimensional block local binary pattern (1D-BlockLBP) encoding scheme combined with HDC to preserve dominant dynamical and temporal characteristics of pulse rate signals from wearable PPG devices.

RESULTS

Our method achieved 70.17 % accuracy in sleep apnea segment detection, which is comparable with traditional machine learning methods. Additionally, our method achieves up to 67× lower memory footprint, 68× latency reduction, and 93× energy saving on the ARM Cortex-M4 processor.

CONCLUSION

The simplicity of hypervector operations in HDC and the novel 1D-BlockLBP encoding effectively preserve pulse rate signal characteristics with high computational efficiency.

SIGNIFICANCE

This work provides a scalable solution for long-term home-based monitoring of sleep apnea, enhancing the feasibility of consistent patient care.

Computational insights on asymmetrical D1 and D2 receptor-mediated chunking: implications for OCD and Schizophrenia

Repetitive thoughts and motor programs including perseveration are bridge symptoms characteristic of obsessive compulsive disorder (OCD), schizophrenia and in the co-morbid overlap of these conditions. The above pathologies are sensitive to altered activation and kinetics of dopamine D 1 and D 2 receptors that differently influence sequence learning and recall. Recognizing start and stop elements of motor and cognitive behaviors has crucial importance. During chunking, frequent components of temporal strings are concatenated into single units. We extended a published computational model (Asabuki et al. 2018), where two populations of neurons are connected and simulated in a reservoir computing framework. These neural pools were adopted to represent D1 and D2 striatal neuronal populations. We investigated how specific neural and striatal circuit parameters can influence start/stop signaling and found that asymmetric intra-network connection probabilities, synaptic weights and differential time constants may contribute to signaling of start/stop elements within learned sequences. Asymmetric coupling between the striatal D 1 and D 2 neural populations was also demonstrated to be beneficial. Our modeling results predict that dynamical differences between the two dopaminergic striatal populations and the interaction between them may play complementary roles in chunk boundary signaling. Start and stop dichotomies can arise from the larger circuit dynamics as well, since neural and intra-striatal connections only partially support a clear division of labor.

Association between different sensory modalities based on concurrent time series data obtained by a collaborative reservoir computing model

Humans perceive the external world by integrating information from different modalities, obtained through the sensory organs. However, the aforementioned mechanism is still unclear and has been a subject of widespread interest in the fields of psychology and brain science. A model using two reservoir computing systems, i.e., a type of recurrent neural network trained to mimic each other's output, can detect stimulus patterns that repeatedly appear in a time series signal. We applied this model for identifying specific patterns that co-occur between information from different modalities. The model was self-organized by specific fluctuation patterns that co-occurred between different modalities, and could detect each fluctuation pattern. Additionally, similarly to the case where perception is influenced by synchronous/asynchronous presentation of multimodal stimuli, the model failed to work correctly for signals that did not co-occur with corresponding fluctuation patterns. Recent experimental studies have suggested that direct interaction between different sensory systems is important for multisensory integration, in addition to top-down control from higher brain regions such as the association cortex. Because several patterns of interaction between sensory modules can be incorporated into the employed model, we were able to compare the performance between them; the original version of the employed model incorporated such an interaction as the teaching signals for learning. The performance of the original and alternative models was evaluated, and the original model was found to perform the best. Thus, we demonstrated that feedback of the outputs of appropriately learned sensory modules performed the best when compared to the other examined patterns of interaction. The proposed model incorporated information encoded by the dynamic state of the neural population and the interactions between different sensory modules, both of which were based on recent experimental observations; this allowed us to study the influence of the temporal relationship and frequency of occurrence of multisensory signals on sensory integration, as well as the nature of interaction between different sensory signals.

Neural mechanisms for learning hierarchical structures of information

Spatial and temporal information from the environment is often hierarchically organized, so is our knowledge formed about the environment. Identifying the meaningful segments embedded in hierarchically structured information is crucial for cognitive functions, including visual, auditory, motor, memory, and language processing. Segmentation enables the grasping of the links between isolated entities, offering the basis for reasoning and thinking. Importantly, the brain learns such segmentation without external instructions. Here, we review the underlying computational mechanisms implemented at the single-cell and network levels. The network-level mechanism has an interesting similarity to machine-learning methods for graph segmentation. The brain possibly implements methods for the analysis of the hierarchical structures of the environment at multiple levels of its processing hierarchy.

Somatodendritic consistency check for temporal feature segmentation

The brain identifies potentially salient features within continuous information streams to process hierarchical temporal events. This requires the compression of information streams, for which effective computational principles are yet to be explored. Backpropagating action potentials can induce synaptic plasticity in the dendrites of cortical pyramidal neurons. By analogy with this effect, we model a self-supervising process that increases the similarity between dendritic and somatic activities where the somatic activity is normalized by a running average. We further show that a family of networks composed of the two-compartment neurons performs a surprisingly wide variety of complex unsupervised learning tasks, including chunking of temporal sequences and the source separation of mixed correlated signals. Common methods applicable to these temporal feature analyses were previously unknown. Our results suggest the powerful ability of neural networks with dendrites to analyze temporal features. This simple neuron model may also be potentially useful in neural engineering applications.

The authors propose a learning rule for a neuron model with dendrite. In their model, somatodendritic interaction implements self-supervised learning applicable to a wide range of sequence learning tasks, including spike pattern detection, chunking temporal input and blind source separation.