Eligibility Propagation (e-prop) is a biologically inspired learning algorithm providing a plausible alternative to Backpropagation Through Time (BPTT) [1]. Despite its biological realism, e-prop inherits BPTT's vulnerability to catastrophic forgetting (CF), a phenomenon in which neural networks lose previously acquired knowledge when trained on new tasks.
Elastic Weight Consolidation (EWC)...
Biological neurons are remarkably energy-efficient, capable of performing complex computations with minimal energy consumption. Understanding these mechanisms can inform the design of efficient computational models and energy-aware learning systems. In this study, we implemented energy-dependent neuron and synaptic plasticity models—EDLIF (Energy-Dependent Leaky Integrate-and-Fire) and EDSTDP...
In computational neuroscience, systematically monitoring the performance of simulation software remains a major challenge due to continuous technological advances and the to the diversity of scales across relevant network models. Previous work by Albers et al. [1] introduced conceptual foundations and an open-source framework for benchmarking neuronal network simulators. However, two years of...
Brain-wide neural function involves the communication between neural networks from distinct anatomical areas and different evolutionary antiquity, as in the mammal cortico-thalamo-cortical loop formed by S1, M2, and TH [1, 2]. Despite its evident importance, the mechanisms by which the information is relayed in this "inter-area" communication remain hazy. Additionally, it is still unknown how...
We introduce a fully spiking, three-dimensional model of the hippocampus–neocortex loop that captures the complementary learning systems (CLS) functions [1] of rapid pattern separation, sequence completion and slow cortical consolidation. Dentate gyrus (DG), CA3 and CA1 are represented as spatially distinct 3-D liquid-state networks [2] whose connectivity mirrors hippocampal anatomy: extremely...
Transcranial direct current stimulation (tDCS) is increasingly used to modulate motor learning and rehabilitation, yet its effects vary depending on polarity, intensity, electrode montage, and timing. Traditional models based on Hebbian or homeostatic plasticity only partially explain these inconsistencies. We propose that homeostatic structural plasticity (HSP) can offer a more comprehensive...
