Biology subjects

Golden, R.

Publications and source records attributed to Golden, R..

5 recordsLinked to original sources

bioRxiv2026

Sleep to forget: active control of consolidation and forgetting by slow-wave sleep dynamics

Sleep supports both the consolidation of new memories and the forgetting of others, but how the cortex flexibly controls these outcomes remains poorly understood. Recent work has shown that two types of Up states may play distinct, competing roles during slow-wave sleep (SWS): slow waves actively consolidate memory traces, whereas delta waves promote their weakening. Here we use a biophysical thalamocortical network model equipped with spike-timing-dependent plasticity to investigate the synaptic mechanisms underlying this dissociation. By manipulating the intrinsic Ca2+ dynamics of cortical pyramidal cells, we generate both slow and delta wave Up states within a single network. Using a sequence-learning task paradigm we recapitulate the optogenetic dissociation: removing plasticity during slow waves degrades the memory, while removing it during delta waves enhances consolidation. Mechanistically, the model reveals the longer slow wave Up state affords a spontaneous reactivation phase, occurring after the interfering input, during which the trained memory is selectively reactivated and protected, a phase the truncated delta Up state cannot support. We further find that delta waves sparsen the synaptic representation more than slow waves and predict that the balance between consolidation and forgetting can be flexibly tuned by the ratio of slow to delta waves during SWS.

neuroscience↗

bioRxiv2025

Hippocampal indexing alters the stability landscape of synaptic weight space allowing life-long learning

Sleep replay - the reactivation of memory traces during slow-wave sleep - is widely held to stabilize memories and reduce interference, yet exactly how replay reorganizes synaptic-weight space to preserve existing memories while incorporating new ones remains unclear. Here, we use a biophysically realistic network model to probe the synaptic-weight dynamics underlying this process. We find that replay drives synaptic weights toward stable configurations - synaptic attractors - that jointly support both old and new memories. Hippocampus-driven interactions between sharp-wave ripples and cortical slow waves guide this reorganization, allowing recently acquired memories to be incorporated without degrading prior ones. These results reveal a mechanistic and geometric framework for memory consolidation: sleep does not passively protect memories, but actively sculpts synaptic-weight space into attractor configurations from which forgetting requires escaping a stability basin. SIGNIFICANCE STATEMENTStoring, processing, and retrieving information underpins intelligent behavior. Sleep extracts invariant features from prior experience, promoting the emergence of explicit knowledge and insight. Yet despite abundant empirical findings, our understanding of how sleep reshapes memory representations at the level of synaptic organization remains limited. Here we present a novel framework that describes how memories are encoded in synaptic-weight space and how sleep dynamics reorganize synaptic landscape. These results advance our understanding of how the brain solves core problems of lifelong learning.

neuroscience↗

bioRxiv2025

Interleaved Replay of Novel and Familiar Memory Traces During Slow-Wave Sleep Prevents Catastrophic Forgetting

Humans and animals can learn continuously, acquiring new knowledge while integrating it into a lifelong memory pool. In contrast, artificial neural networks (ANNs) suffer from catastrophic forgetting, where new training disrupts existing memories. This issue can be alleviated in ANNs by interleaving training on new tasks with past data; however, whether the brain uses a similar strategy is unknown. In this work, we show that slow-wave sleep interleaves replay of familiar and novel (i.e. hippocampal-dependent) memory traces within individual slow waves, allowing new memories to integrate into the existing cortical pool without interference. This study presents a novel theory for how memory traces acquired across an animals life are organized within the cortical-hippocampal system to support continual learning and suggests novel principles for a broad range of continual learning AI.

neuroscience↗

bioRxiv2020

Multielectrode Cortical Stimulation Selectively Induces Unidirectional Wave Propagation in Biophysical/Neural Model

Cortical stimulation is emerging as an experimental tool in basic research and a promising therapy for a range of neuropsychiatric conditions. As multielectrode arrays enter clinical practice, the possibility of using spatiotemporal patterns of electrical stimulation to induce desired physiological patterns has become theoretically possible, but in practice can only be implemented by trial-and-error because of a lack of predictive models. Experimental evidence increasingly establishes travelling waves as fundamental to cortical information-processing, but we lack understanding how to control wave properties despite rapidly improving technologies. This study uses a hybrid biophysical-anatomical and neural-computational model to predict and understand how a simple pattern of cortical surface stimulation could induce directional traveling waves via asymmetric activation of inhibitory interneurons. It reveals local circuit mechanisms to control spatiotemporal cortical dynamics and predicts interventions that can be developed to treat a broad range of cognitive disorders.

neuroscience↗

bioRxiv2019

Interleaved training prevents catastrophic forgetting in spiking neural networks

Artificial neural networks overwrite previously learned tasks when trained sequentially, a phenomenon known as catastrophic forgetting. In contrast, the brain learns continuously, and typically learns best when new learning is interleaved with periods of sleep for memory consolidation. In this study, we used spiking network to study mechanisms behind catastrophic forgetting and the role of sleep in preventing it. The network could be trained to learn a complex foraging task but exhibited catastrophic forgetting when trained sequentially on multiple tasks. New task training moved the synaptic weight configuration away from the manifold representing old tasks leading to forgetting. Interleaving new task training with periods of off-line reactivation, mimicking biological sleep, mitigated catastrophic forgetting by pushing the synaptic weight configuration towards the intersection of the solution manifolds representing multiple tasks. The study reveals a possible strategy of synaptic weights dynamics the brain applies during sleep to prevent forgetting and optimize learning.

neuroscience↗