Introducing principles of synaptic integration in the optimization of deep neural networks

Neuroadaptation in neurodegenerative diseases: compensatory mechanisms and therapeutic approaches

Progressive neuronal loss is a hallmark of neurodegenerative diseases including Alzheimer's, Parkinson's, Huntington's, and Amyotrophic Lateral Sclerosis (ALS), which cause cognitive and motor impairment. Delaying the onset and course of symptoms is largely dependent on neuroadaptation, the brain's ability to restructure in response to damage. The molecular, cellular, and systemic processes that underlie neuroadaptation are examined in this study. These mechanisms include gliosis, neurogenesis, synaptic plasticity, and changes in neurotrophic factors. Axonal sprouting, dendritic remodelling, and compensatory alterations in neurotransmitter systems are important adaptations observed in NDDs; nevertheless, these processes may shift to maladaptive plasticity, which would aid in the advancement of the illness. Amyloid and tau pathology-induced synaptic alterations in Alzheimer's disease emphasize compensatory network reconfiguration. Dopamine depletion causes a major remodelling of the basal ganglia in Parkinson's disease, and non-dopaminergic systems compensate. Both ALS and Huntington's disease rely on motor circuit rearrangement and transcriptional dysregulation to slow down functional deterioration. Neuroadaptation is, however, constrained by oxidative stress, compromised autophagy, and neuroinflammation, particularly in elderly populations. The goal of emerging therapy strategies is to improve neuroadaptation by pharmacologically modifying neurotrophic factors, neuroinflammation, and synaptic plasticity. Neurostimulation, cognitive training, and physical rehabilitation are instances of non-pharmacological therapies that support neuroplasticity. Restoring compensating systems may be possible with the use of stem cell techniques and new gene treatments. The goal of future research is to combine biomarkers and individualized medicines to maximize neuroadaptive responses and decrease the course of illness. In order to reduce neurodegeneration and enhance patient outcomes, this review highlights the dual function of neuroadaptation in NDDs and its potential as a therapeutic target.

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.

Metaplasticity-Enabled Graphene Quantum Dot Devices for Mitigating Catastrophic Forgetting in Artificial Neural Networks

The limitations of deep neural networks in continuous learning stem from oversimplifying the complexities of biological neural circuits, often neglecting the dynamic balance between memory stability and learning plasticity. In this study, artificial synaptic devices enhanced with graphene quantum dots (GQDs) that exhibit metaplasticity is introduced, a higher-order form of synaptic plasticity that facilitates the dynamic regulation of memory and learning processes similar to those observed in biological systems. The GQDs-assisted devices utilize interface-mediated modifications in asymmetric conductive pathways, replicating classical synaptic plasticity mechanisms. This allows for repeatable and linearly programmable adjustments to future weight changes linked to historical weights. Incorporating metaplasticity is essential for achieving generalization within deep neural networks, which enables them to adapt more fluidly to new information while retaining previously acquired knowledge. The GQDs-device-based system achieved a 97% accuracy on the fourth MNIST dataset task, while consistently achieving performance levels above 94% on prior tasks. This performance substantiates the feasibility of directly transferring metaplasticity principles to deep neural networks, thereby addressing the challenges associated with catastrophic forgetting. These findings present a promising hardware solution for developing neuromorphic systems with robust and sustained learning capabilities that can effectively bridge the gap between artificial and biological neural networks.

Ionic Potential Relaxation Effect in a Hydrogel Enabling Synapse-Like Information Processing

The next-generation brain-like intelligence based on neuromorphic architectures emphasizes learning the ionic language of the brain, aiming for efficient brain-like computation and seamless human-computer interaction. Ionic neuromorphic devices, with ions serving as information carriers, provide possibilities to achieve this goal. Soft and biocompatible ionic conductive hydrogels are an ideal substrate for constructing ionic neuromorphic devices, but it remains a challenge to modulate the ion transport behavior in hydrogels to mimic neuroelectric signals. Here, we describe an ionic potential relaxation effect in a hydrogel device prepared by sandwiching a layer of polycationic hydrogel (CH) between two layers of neutral hydrogel (NH), allowing this device to simulate various electrical signal patterns observed in biological synapses, including short- and long-term plasticity patterns. Theoretical and experimental results show that the selective permeation and hysteretic diffusion of ions caused by the anion selectivity of the CH layer are responsible for potential relaxation. Such an effect allows us with hydrogels to enable synapse-like information processing functions, including tactile perception, learning, memory, and neuromorphic computing. Additionally, the hydrogel device can operate stably even under 180° bending and 50% tensile strain, expanding the pathway for implementing advanced brain-like intelligent systems.

Synaptic Transistors Based on PVA: Chitosan Biopolymer Blended Electric-Double-Layer with High Ionic Conductivity

This study proposed a biocompatible polymeric organic material-based synaptic transistor gated with a biopolymer electrolyte. A polyvinyl alcohol (PVA):chitosan (CS) biopolymer blended electrolyte with high ionic conductivity was used as an electrical double layer (EDL). It served as a gate insulator with a key function as an artificial synaptic transistor. The frequency-dependent capacitance characteristics of PVA:CS-based biopolymer EDL were evaluated using an EDL capacitor (Al/PVA: CS blended electrolyte-based EDL/Pt configuration). Consequently, the PVA:CS blended electrolyte behaved as an EDL owing to high capacitance (1.53 µF/cm²) at 100 Hz and internal mobile protonic ions. Electronic synaptic transistors fabricated using the PVA:CS blended electrolyte-based EDL membrane demonstrated basic artificial synaptic behaviors such as excitatory post-synaptic current modulation, paired-pulse facilitation, and dynamic signal-filtering functions by pre-synaptic spikes. In addition, the spike-timing-dependent plasticity was evaluated using synaptic spikes. The synaptic weight modulation was stable during repetitive spike cycles for potentiation and depression. Pattern recognition was conducted through a learning simulation for artificial neural networks (ANNs) using Modified National Institute of Standards and Technology datasheets to examine the neuromorphic computing system capability (high recognition rate of 92%). Therefore, the proposed synaptic transistor is suitable for ANNs and shows potential for biological and eco-friendly neuromorphic systems.

Explaining cocktail party effect and McGurk effect with a spiking neural network improved by Motif-topology

Network architectures and learning principles have been critical in developing complex cognitive capabilities in artificial neural networks (ANNs). Spiking neural networks (SNNs) are a subset of ANNs that incorporate additional biological features such as dynamic spiking neurons, biologically specified architectures, and efficient and useful paradigms. Here we focus more on network architectures in SNNs, such as the meta operator called 3-node network motifs, which is borrowed from the biological network. We proposed a Motif-topology improved SNN (M-SNN), which is further verified efficient in explaining key cognitive phenomenon such as the cocktail party effect (a typical noise-robust speech-recognition task) and McGurk effect (a typical multi-sensory integration task). For M-SNN, the Motif topology is obtained by integrating the spatial and temporal motifs. These spatial and temporal motifs are first generated from the pre-training of spatial (e.g., MNIST) and temporal (e.g., TIDigits) datasets, respectively, and then applied to the previously introduced two cognitive effect tasks. The experimental results showed a lower computational cost and higher accuracy and a better explanation of some key phenomena of these two effects, such as new concept generation and anti-background noise. This mesoscale network motifs topology has much room for the future.