Rodent studies suggest that spike timing relative to hippocampal theta activity determines whether potentiation or depression of synapses arise. Such changes also depend on spike timing between presynaptic and postsynaptic neurons, known as spike timing-dependent plasticity (STDP). STDP, together with theta phase-dependent learning, has inspired several computational models of learning and memory. However, evidence to elucidate how these mechanisms directly link to human episodic memory is lacking. In a computational model, we modulate long-term potentiation (LTP) and long-term depression (LTD) of STDP, by opposing phases of a simulated theta rhythm. We fit parameters to a hippocampal cell culture study in which LTP and LTD were observed to occur in opposing phases of a theta rhythm. Further, we modulated two inputs by cosine waves with 0° and asynchronous phase offsets and replicate key findings in human episodic memory. Learning advantage was found for the in-phase condition, compared with the out-of-phase conditions, and was specific to theta-modulated inputs. Importantly, simulations with and without each mechanism suggest that both STDP and theta phase-dependent plasticity are necessary to replicate the findings. Together, the results indicate a role for circuit-level mechanisms, which bridge the gap between slice preparation studies and human memory.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10012328 | PMC |
http://dx.doi.org/10.1523/ENEURO.0333-22.2023 | DOI Listing |
BMC Neurol
December 2024
Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA, 02115, USA.
Parkinson's disease (PD) is a neurodegenerative disease affecting millions of people around the world. Conventional PD detection algorithms are generally based on first and second-generation artificial neural network (ANN) models which consume high energy and have complex architecture. Considering these limitations, a time-varying synaptic efficacy function based leaky-integrate and fire neuron model, called SEFRON is used for the detection of PD.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, 55905, USA.
Alcohol use disorder (AUD) is a chronic relapsing brain disorder characterized by an impaired ability to stop or control alcohol consumption despite adverse social, occupational, or health consequences. AUD affects nearly one-third of adults at some point during their lives, with an associated cost of approximately $249 billion annually in the U.S.
View Article and Find Full Text PDFJ Comput Neurosci
December 2024
Department of Physics, Drexel University, 3141 Chestnut Street, Philadelphia, 19104, PA, USA.
Traveling waves of neuronal spiking activity are commonly observed across the brain, but their intrinsic function is still a matter of investigation. Experiments suggest that they may be valuable in the consolidation of memory or learning, indicating that consideration of traveling waves in the presence of plasticity might be important. A possible outcome of this consideration is that the synaptic pathways, necessary for the propagation of these waves, will be modified by the waves themselves.
View Article and Find Full Text PDFNanoscale
December 2024
Electronic Materials Laboratory, K. N. Toosi University of Technology, Tehran 1631714191, Iran.
Multibit/analog artificial synapses are in demand for neuromorphic computing systems. A problem hindering the utilization of memristive artificial synapses in commercial neuromorphic systems is the rigidity of their functional parameters, plasticity in particular. Here, we report fabricating polycrystalline rutile-based memristive memory segments with Ti/poly-TiO/Ti structures featuring multibit/analog storage and the first use of a tunable DC-biasing for synaptic plasticity adjustment from short- to long-term.
View Article and Find Full Text PDFNeural Netw
December 2024
Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany. Electronic address:
Cortical networks are capable of unsupervised learning and spontaneous replay of complex temporal sequences. Endowing artificial spiking neural networks with similar learning abilities remains a challenge. In particular, it is unresolved how different plasticity rules can contribute to both learning and the maintenance of network stability during learning.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!