IEEE transactions on biomedical circuits and systems
37015137
In this article, we present a spiking neural network (SNN) based on both SRAM processing-in-memory (PIM) macro and on-chip unsupervised learning with Spike-Time-Dependent Plasticity (STDP). Co-design of algorithm and hardware for hardware-friendly SN...
Backpropagation is widely used to train artificial neural networks, but its relationship to synaptic plasticity in the brain is unknown. Some biological models of backpropagation rely on feedback projections that are symmetric with feedforward connec...
Synaptic plasticity, or the ability of a brain to change one or more of its functions or structures at the synaptic level, has generated and is still generating a lot of interest from the scientific community especially from neuroscientists. These in...
Animals rely on different decision strategies when faced with ambiguous or uncertain cues. Depending on the context, decisions may be biased towards events that were most frequently experienced in the past, or be more explorative. A particular type o...
Large-scale recordings of neural activity over days and weeks have revealed that neural representations of familiar tasks, precepts and actions continually evolve without obvious changes in behaviour. We hypothesise that this steady drift in neural a...
Understanding the effect of spike-timing-dependent plasticity (STDP) is key to elucidating how neural networks change over long timescales and to design interventions aimed at modulating such networks in neurological disorders. However, progress is r...
While neural plasticity has long been studied as the basis of learning, the growth of large-scale neural recording techniques provides a unique opportunity to study how learning-induced activity changes are coordinated across neurons within the same ...
Synchronization is a widespread phenomenon in the brain. Despite numerous studies, the specific parameter configurations of the synaptic network structure and learning rules needed to achieve robust and enduring synchronization in neurons driven by s...
IEEE transactions on neural networks and learning systems
37389999
The brain's mystery for efficient and intelligent computation hides in the neuronal encoding, functional circuits, and plasticity principles in natural neural networks. However, many plasticity principles have not been fully incorporated into artific...
IEEE transactions on neural networks and learning systems
37581976
Spiking neural networks (SNNs) have captivated the attention worldwide owing to their compelling advantages in low power consumption, high biological plausibility, and strong robustness. However, the intrinsic latency associated with SNNs during infe...