22 papers · ranked by Valyu relevance
Bej, Saptarshi, E, Muhammed Sahad + 8 more
We introduce Spike Agreement Dependent Plasticity (SADP), a biologically inspired synaptic learning rule for Spiking Neural Networks (SNNs) that relies on the agreement between pre- and post-synaptic spike trains rather than precise spike-pair timing. SADP generalizes classical Spike-Timing-Dependent Plasticity (STDP)…
Guanchun Li, David W. McLaughlin, Charles S. Peskin
Synaptic plasticity (long term potentiation/depression (LTP/D)), is a cellular mechanism underlying learning. Two distinct types of early LTP/D (E-LTP/D), acting on very different time scales, have been observed experimentally – spike timing dependent plasticity (STDP), on time scales of tens of ms; and behavioral time…
Nimrod Sherf, Maoz Shamir
Rats and mice probe their surroundings by rhythmically moving their whiskers back and forth. The azimuthal position of a whisker can be estimated from the activity of whisking neurons that respond selectively to a preferred phase along the whisking cycle. These preferred phases are widely distributed on the ring.…
Victor Kazantsev, Ivan Tyukin, Yamir Moreno
We consider and analyze the influence of spike-timing dependent plasticity (STDP) on homeostatic states in synaptically coupled neuronal oscillators. In contrast to conventional models of STDP in which spike-timing affects weights of synaptic connections, we consider a model of STDP in which the time lags between pre-…
Yihui Cui, Ilya Prokin, Alexandre Mendes, Hugues Berry + 1 more
In Hebbian plasticity, neural circuits adjust their synaptic weights depending on patterned firing of action potential on either side of the synapse. Spike-timing-dependent plasticity (STDP) is an experimental implementation of Hebb’s postulate that relies on the precise order and the millisecond timing of the paired…
Nicolas Deperrois, Michael Graupner
Synaptic efficacy is subjected to activity-dependent changes on short- and long time scales. While short-term changes decay over minutes, long-term modifications last from hours up to a lifetime and are thought to constitute the basis of learning and memory. Both plasticity mechanisms have been studied extensively but…
Biswadeep Chakraborty, Saibal Mukhopadhyay
A Spiking Neural Network (SNN) is trained with Spike Timing Dependent Plasticity (STDP), which is a neuro-inspired unsupervised learning method for various machine learning applications. This paper studies the generalizability properties of the STDP learning processes using the Hausdorff dimension of the trajectories…
Priyadarshini Panda, Jason M. Allred, Shriram Ramanathan, Kaushik Roy
'Kaushik Roy'] Abstract—A fundamental feature of learning in animals is the "ability to forget" that allows an organism to perceive, model and make decisions from disparate streams of information and adapt to changing environments. Against this backdrop, we present a novel unsupervised learning mechanism ASP (Adaptive…
Amirreza Yousefzadeh, Evangelos Stromatias, Miguel Soto, Teresa Serrano-Gotarredona + 1 more
'Teresa Serrano-Gotarredona' 'Bernabé Linares-Barranco'] In computational neuroscience, synaptic plasticity learning rules are typically studied using the full 64-bit floating point precision computers provide. However, for dedicated hardware implementations, the precision used not only penalizes directly the required…
Nicolas Deperrois, Michael Graupner, Hermann Cuntz
Synaptic efficacy is subjected to activity-dependent changes on short- and long time scales. While short-term changes decay over minutes, long-term modifications last from hours up to a lifetime and are thought to constitute the basis of learning and memory. Both plasticity mechanisms have been studied extensively but…
Claire Piochon, Peter Kruskal, Jason MacLean, Christian Hansel
Spike-timing-dependent plasticity (STDP) provides a cellular implementation of the Hebb postulate, which states that synapses, whose activity repeatedly drives action potential firing in target cells, are potentiated. At glutamatergic synapses onto hippocampal and neocortical pyramidal cells, synaptic activation…
Shashi Kant Gupta
The backpropagation algorithm is often debated for its biological plausibility. However, various learning methods for neural architecture have been proposed in search of more biologically plausible learning. Most of them have tried to solve the "weight transport problem" and try to propagate errors backward in the…
Mostafa Rahimi Azghadi, Said F. Al-Sarawi, Nicolangelo Iannella, Derek Abbott
'Derek Abbott'] Abstract—The Bienenstock-Cooper-Munro (BCM) and Spike Timing-Dependent Plasticity (STDP) rules are two experimentally verified form of synaptic plasticity where the alteration of synaptic weight depends upon the rate and the timing of pre- and postsynaptic firing of action potentials, respectively.…
Vincent Delattre, Daniel Keller, Matthew Perich, Henry Markram + 1 more
'Eilif B. Muller'] Bursts of activity in networks of neurons are thought to convey salient information and drive synaptic plasticity. Here we report that network bursts also exert a profound effect on Spike-Timing-Dependent Plasticity (STDP). In acute slices of juvenile rat somatosensory cortex we paired a network…
Roshan Gopalakrishnan, Arindam Basu
— Synapse plays an important role of learning in a neural network; the learning rules which modify the synapti c strength based on the timing difference between the pre- and postsynaptic spike occurrence is termed as Spike Time Dependent Plasticity (STDP). The most commonly used rule posits weigh t change based on time…
Yanis Inglebert, Dominique Debanne
Since its discovery, spike timing-dependent synaptic plasticity (STDP) has been thought to be a primary mechanism underlying the brain’s ability to learn and to form new memories. However, despite the enormous interest in both the experimental and theoretical neuroscience communities in activity-dependent plasticity…
Sabrina Tazerart, Diana E. Mitchell, Soledad Miranda-Rottmann, Roberto Araya
Spike-timing-dependent plasticity (STDP) has been extensively studied in cortical pyramidal neurons, however, the precise structural organization of excitatory inputs that supports STDP, as well as the structural, molecular and functional properties of dendritic spines during STDP remain unknown. Here we performed a…
Naoki Masuda, Hiroshi Kori
Spike-timing-dependent plasticity (STDP) with asymmetric learning windows is commonly found in the brain and useful for a variety of spike-based computations such as input filtering and associative memory. A natural consequence of STDP is establishment of causality in the sense that a neuron learns to fire with a lag…
Danying Wang, George Parish, Kimron L. Shapiro, Simon Hanslmayr
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 pre- and post-synaptic neurons, known as spike-timing-dependent plasticity (STDP). STDP, together with theta-phase-dependent…
Tjeerd V. olde Scheper, Huibert D. Mansvelder, Arjen van Ooyen, Marcus Kaiser
'Marcus Kaiser'] Short Term Plasticity (STP) has been shown to exist extensively in synapses throughout the brain. Its function is more or less clear in the sense that it alters the probability of synaptic transmission at short time scales. However, it is still unclear what effect STP has on the dynamics of neural…
Janet Barroso-Flores, Marco A. Herrera-Valdez, Violeta Gisselle Lopez-Huerta, Elvira Galarraga + 1 more
'Violeta Gisselle Lopez-Huerta' 'Elvira Galarraga' 'José Bargas'] Most neurons in the striatum are projection neurons (SPNs) which make synapses with each other within distances of approximately 100 µm. About 5% of striatal neurons are GABAergic interneurons whose axons expand hundreds of microns. Short-term synaptic…
Authors not listed
Accurately modeling the dynamics of open quantum systems is critical for advancing quantum technologies, yet traditional methods often struggle with balancing accuracy and efficiency. Machine learning (ML) offers a promising alternative, particularly through recursive models that predict system evolution based on the…