590 research outputs found
Digital twin brain: a bridge between biological intelligence and artificial intelligence
In recent years, advances in neuroscience and artificial intelligence have
paved the way for unprecedented opportunities for understanding the complexity
of the brain and its emulation by computational systems. Cutting-edge
advancements in neuroscience research have revealed the intricate relationship
between brain structure and function, while the success of artificial neural
networks highlights the importance of network architecture. Now is the time to
bring them together to better unravel how intelligence emerges from the brain's
multiscale repositories. In this review, we propose the Digital Twin Brain
(DTB) as a transformative platform that bridges the gap between biological and
artificial intelligence. It consists of three core elements: the brain
structure that is fundamental to the twinning process, bottom-layer models to
generate brain functions, and its wide spectrum of applications. Crucially,
brain atlases provide a vital constraint, preserving the brain's network
organization within the DTB. Furthermore, we highlight open questions that
invite joint efforts from interdisciplinary fields and emphasize the
far-reaching implications of the DTB. The DTB can offer unprecedented insights
into the emergence of intelligence and neurological disorders, which holds
tremendous promise for advancing our understanding of both biological and
artificial intelligence, and ultimately propelling the development of
artificial general intelligence and facilitating precision mental healthcare
MT-SNN: Spiking Neural Network that Enables Single-Tasking of Multiple Tasks
In this paper we explore capabilities of spiking neural networks in solving
multi-task classification problems using the approach of single-tasking of
multiple tasks. We designed and implemented a multi-task spiking neural network
(MT-SNN) that can learn two or more classification tasks while performing one
task at a time. The task to perform is selected by modulating the firing
threshold of leaky integrate and fire neurons used in this work. The network is
implemented using Intel's Lava platform for the Loihi2 neuromorphic chip. Tests
are performed on dynamic multitask classification for NMNIST data. The results
show that MT-SNN effectively learns multiple tasks by modifying its dynamics,
namely, the spiking neurons' firing threshold.Comment: 4 pages, 2 figure
Real-time hebbian learning from autoencoder features for control tasks
Neural plasticity and in particular Hebbian learning play an important role in many research areas related to artficial life. By allowing artificial neural networks (ANNs) to adjust their weights in real time, Hebbian ANNs can adapt over their lifetime. However, even as researchers improve and extend Hebbian learning, a fundamental limitation of such systems is that they learn correlations between preexisting static features and network outputs. A Hebbian ANN could in principle achieve significantly more if it could accumulate new features over its lifetime from which to learn correlations. Interestingly, autoencoders, which have recently gained prominence in deep learning, are themselves in effect a kind of feature accumulator that extract meaningful features from their inputs. The insight in this paper is that if an autoencoder is connected to a Hebbian learning layer, then the resulting Realtime Autoencoder-Augmented Hebbian Network (RAAHN) can actually learn new features (with the autoencoder) while simultaneously learning control policies from those new features (with the Hebbian layer) in real time as an agent experiences its environment. In this paper, the RAAHN is shown in a simulated robot maze navigation experiment to enable a controller to learn the perfect navigation strategy significantly more often than several Hebbian-based variant approaches that lack the autoencoder. In the long run, this approach opens up the intriguing possibility of real-time deep learning for control
Investigating White Matter Lesion Load, Intrinsic Functional Connectivity, and Cognitive Abilities in Older Adults
Changes to the while matter of the brain disrupt neural communication between spatially distributed brain regions and are associated with cognitive changes in later life. While approximately 95% of older adults experience these brain changes, not everyone who has significant white matter damage displays cognitive impairment. Few studies have investigated the association between white matter changes and cognition in the context of functional brain network integrity. This study used a data-driven, multivariate analytical model to investigate intrinsic functional connectivity patterns associated with individual variability in white matter lesion load as related to fluid and crystallized intelligence in a sample of healthy older adults (n = 84). Several primary findings were noted. First, a reliable pattern emerged associating whole-brain resting-state functional connectivity with individual variability in measures of white matter lesion load, as indexed by total white matter lesion volume and number of lesions. Secondly, white matter lesion load was associated with increased network disintegration and dedifferentiation. Specifically, lower white matter lesion load was associated with greater within- versus between-network connectivity. Higher white matter lesion load was associated with greater between-network connectivity compared to within. These associations between intrinsic functional connectivity and white matter lesion load were not reliably associated with crystallized and fluid intelligence performance. These results suggest that changes to the white matter of the brain in typically aging older adults are characterized by increased functional brain network dedifferentiation. The findings highlight the role of white matter lesion load in altering the functional network architecture of the brain
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Preliminary prediction of individual response to electroconvulsive therapy using whole-brain functional magnetic resonance imaging data.
Electroconvulsive therapy (ECT) works rapidly and has been widely used to treat depressive disorders (DEP). However, identifying biomarkers predictive of response to ECT remains a priority to individually tailor treatment and understand treatment mechanisms. This study used a connectome-based predictive modeling (CPM) approach in 122 patients with DEP to determine if pre-ECT whole-brain functional connectivity (FC) predicts depressive rating changes and remission status after ECT (47 of 122 total subjects or 38.5% of sample), and whether pre-ECT and longitudinal changes (pre/post-ECT) in regional brain network biomarkers are associated with treatment-related changes in depression ratings. Results show the networks with the best predictive performance of ECT response were negative (anti-correlated) FC networks, which predict the post-ECT depression severity (continuous measure) with a 76.23% accuracy for remission prediction. FC networks with the greatest predictive power were concentrated in the prefrontal and temporal cortices and subcortical nuclei, and include the inferior frontal (IFG), superior frontal (SFG), superior temporal (STG), inferior temporal gyri (ITG), basal ganglia (BG), and thalamus (Tha). Several of these brain regions were also identified as nodes in the FC networks that show significant change pre-/post-ECT, but these networks were not related to treatment response. This study design has limitations regarding the longitudinal design and the absence of a control group that limit the causal inference regarding mechanism of post-treatment status. Though predictive biomarkers remained below the threshold of those recommended for potential translation, the analysis methods and results demonstrate the promise and generalizability of biomarkers for advancing personalized treatment strategies
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