Connectome-based reservoir computing with the conn2res toolbox

Personalized brain models link cognitive decline progression to underlying synaptic and connectivity degeneration

Cognitive decline is a condition affecting almost one sixth of the elder population and is widely regarded as one of the first manifestations of Alzheimer's disease. Despite the extensive body of knowledge on the condition, there is no clear consensus on the structural defects and neurodegeneration processes determining cognitive decline evolution. Here, we introduce a Brain Network Model (BNM) simulating the effects of neurodegeneration on neural activity during cognitive processing. The model incorporates two key parameters accounting for distinct pathological mechanisms: synaptic degeneration, primarily leading to hyperexcitation, and brain disconnection. Through parameter optimization, we successfully replicated individual electroencephalography (EEG) responses recorded during task execution from 145 participants spanning different stages of cognitive decline. The cohort included healthy controls, patients with subjective cognitive decline (SCD), and those with mild cognitive impairment (MCI) of the Alzheimer type. Through model inversion, we generated personalized BNMs for each participant based on individual EEG recordings. These models revealed distinct network configurations corresponding to the patient's cognitive condition, with virtual neurodegeneration levels directly proportional to the severity of cognitive decline. Strikingly, the model uncovered a neurodegeneration-driven phase transition leading to two distinct regimes of neural activity underlying task execution. On either side of this phase transition, increasing synaptic degeneration induced changes in neural activity that closely mirrored experimental observations across cognitive decline stages. This enabled the model to directly link synaptic degeneration and hyperexcitation to cognitive decline severity. Furthermore, the model pinpointed posterior cingulum fiber degeneration as the structural driver of this phase transition. Our findings highlight the potential of BNMs to account for the evolution of neural activity across stages of cognitive decline while elucidating the underlying neurodegenerative mechanisms. This approach provides a novel framework for understanding how structural and functional brain alterations contribute to cognitive deterioration along the Alzheimer's continuum.

Role of short-term plasticity and slow temporal dynamics in enhancing time series prediction with a brain-inspired recurrent neural network

Typical reservoir networks are based on random connectivity patterns that differ from brain circuits in two important ways. First, traditional reservoir networks lack synaptic plasticity among recurrent units, whereas cortical networks exhibit plasticity across all neuronal types and cortical layers. Second, reservoir networks utilize random Gaussian connectivity, while cortical networks feature a heavy-tailed distribution of synaptic strengths. It is unclear what are the computational advantages of these features for predicting complex time series. In this study, we integrated short-term plasticity (STP) and lognormal connectivity into a novel recurrent neural network (RNN) framework. The model exhibited rich patterns of population activity characterized by slow coordinated fluctuations. Using graph spectral decomposition, we show that weighted networks with lognormal connectivity and STP yield higher complexity than several graph types. When tested on various tasks involving the prediction of complex time series data, the RNN model outperformed a baseline model with random connectivity as well as several other network architectures. Overall, our results underscore the potential of incorporating brain-inspired features such as STP and heavy-tailed connectivity to enhance the robustness and performance of artificial neural networks in complex data prediction and signal processing tasks.

Evolution beats random chance: Performance-dependent network evolution for enhanced computational capacity

The quest to understand structure-function relationships in networks across scientific disciplines has intensified. However, the optimal network architecture remains elusive, particularly for complex information processing. Therefore, we investigate how optimal and specific network structures form to efficiently solve distinct tasks using a framework of performance-dependent network evolution, leveraging reservoir computing principles. Our study demonstrates that task-specific minimal network structures obtained through this framework consistently outperform networks generated by alternative growth strategies and Erdős-Rényi random networks. Evolved networks exhibit unexpected sparsity and adhere to scaling laws in node-density space while showcasing a distinctive asymmetry in input and information readout node distribution. Consequently, we propose a heuristic for quantifying task complexity from performance-dependently evolved networks, offering valuable insights into the evolutionary dynamics of the network structure-function relationship. Our findings advance the fundamental understanding of process-specific network evolution and shed light on the design and optimization of complex information processing mechanisms, notably in machine learning.

Piezo Ion Channels and Their Association With Haptic Technology Use: A Narrative Review

The recent identification of Piezo ion channels demonstrating a mechano-sensitive impact on neurons revealed distinct Piezo-1 and 2 types. While Piezo-1 predominates in neurons linked to non-sensory stimulation, such as pressure in blood vessels, Piezo-2 predominates in neurons linked to sensory stimulation, such as touch. Piezo-1 and 2 have a major bidirectional impact on transient receptor potential (TRP) ion channels, and TRPs also impact neurotransmitter release. Particularly existent in dorsal root ganglion (DRG) neurons, which are located in nerve endings, Piezo-2 is a key DRG activator. Subsequent Piezo findings have been vital to recent medical haptic technology developments, in tandem with breakthroughs in the emerging neurology subfield of connectomics plus AI developments. Included in this review are a historical Piezo overview, the interrelationship of Piezo channels with TRPs, inclusive of TRPV1/TRPV8, the impact on medical and rehab haptic technology, a focus on haptic technology use in stroke survivor rehab inclusive of pain mitigation, and the development of a haptic technology patch aimed at alleviating pain and/or anxiety. Neurogenic pain resulting from hyperalgesia/allodynia in stroke survivors is a potential target for drugs and haptics aimed at pain reduction; patients experiencing neuropathic or psychosomatic pain are other prime targets. Increased Piezo knowledge may promote more precisely targeted haptic therapeutic developments.

Competitive interactions shape brain dynamics and computation across species

Adaptive cognition relies on cooperation across anatomically distributed brain circuits. However, specialised neural systems are also in constant competition for limited processing resources. How does the brain's network architecture enable it to balance these cooperative and competitive tendencies? Here we use computational whole-brain modelling to examine the dynamical and computational relevance of cooperative and competitive interactions in the mammalian connectome. Across human, macaque, and mouse we show that the architecture of the models that most faithfully reproduce brain activity, consistently combines modular cooperative interactions with diffuse, long-range competitive interactions. The model with competitive interactions consistently outperforms the cooperative-only model, with excellent fit to both spatial and dynamical properties of the living brain, which were not explicitly optimised but rather emerge spontaneously. Competitive interactions in the effective connectivity produce greater levels of synergistic information and local-global hierarchy, and lead to superior computational capacity when used for neuromorphic computing. Altogether, this work provides a mechanistic link between network architecture, dynamical properties, and computation in the mammalian brain.

Organic Polymer-Based Photodiodes for Optoelectronic Reservoir Computing with Time-Based Coding

The integration of optoelectronic devices with reservoir computing offers a novel and effective approach to in-sensor computing. This work presents a hybrid digital-physical solution that leverages the high-performance poly[(bithiophene)-alternate-(2,5-di(2-octyldodecyl)-3,6-di(thienyl)-pyrrolyl pyrrolidone)] (DPPT-TT) organic polymer-based photodiodes for the hardware implementation of reservoir computing system. The photodiodes demonstrate nonlinear photoelectric responses, fading memory, and cyclical stability, in relation to the temporal information on light stimuli. These attributes enable effective mapping, historical context sensitivity, and consistent performance, with time-encoded inputs, which are particularly essential for accurate and continuous processing of time series data. The optoelectronic reservoir computing system with pulse width modulation (PWM) coding demonstrates impressive performance in the prediction of chaotic sequences, achieving a normalized root-mean-square error as low as 0.095 with optimized parameters. The DPPT-TT-based photodiodes and time-based coding offer a hardware-efficient solution for reservoir computing, significantly advancing Internet of Things applications.

Higher-order Granger reservoir computing: simultaneously achieving scalable complex structures inference and accurate dynamics prediction

Recently, machine learning methods, including reservoir computing (RC), have been tremendously successful in predicting complex dynamics in many fields. However, a present challenge lies in pushing for the limit of prediction accuracy while maintaining the low complexity of the model. Here, we design a data-driven, model-free framework named higher-order Granger reservoir computing (HoGRC), which owns two major missions: The first is to infer the higher-order structures incorporating the idea of Granger causality with the RC, and, simultaneously, the second is to realize multi-step prediction by feeding the time series and the inferred higher-order information into HoGRC. We demonstrate the efficacy and robustness of the HoGRC using several representative systems, including the classical chaotic systems, the network dynamical systems, and the UK power grid system. In the era of machine learning and complex systems, we anticipate a broad application of the HoGRC framework in structure inference and dynamics prediction.