11 papers · ranked by Valyu relevance
Felix Weissenberger, Marcelo Matheus Gauy, Johannes Lengler, Florian Meier + 1 more
'Florian Meier' 'Angelika Steger'] In computational neuroscience, synaptic plasticity rules are often formulated in terms of firing rates. The predominant description of in vivo neuronal activity, however, is the instantaneous rate (or spiking probability). In this article we resolve this discrepancy by showing that…
Matthew A. Lawlor, Steven W. Zucker
We develop a biologically-plausible learning rule called Triplet BCM that provably converges to the class means of general mixture models. This rule generalizes the classical BCM neural rule, and provides a novel interpretation of classical BCM as performing a kind of tensor decomposition. It achieves a substantial…
Lawrence C. Udeigwe, Paul W. Munro, G. Bard Ermentrout
The Bienenstock-Cooper-Munro (BCM) learning rule provides a simple setup for synaptic modification that combines a Hebbian product rule with a homeostatic mechanism that keeps the weights bounded. The homeostatic part of the learning rule depends on the time average of the post-synaptic activity and provides a sliding…
Rahma Fourati, Boudour Ammar, Javier Medina, Adel M. Alimi
—In real-world applications such as emotion recognition from recorded brain activity, data are captured from electrodes over time. These signals constitute a multidimensional time series. In this paper, Echo State Network (ESN), a recurrent neural network with a great success in time series prediction and…
Lawrence C Udeigwe, G Bard Ermentrout, Paul W Munro
The BCM learning rule originally arose from experiments intended for measuring the selectivity of neurons in the primary visual cortex, and it dependence on input stimuli. This learning rule incorporates a dynamic LTP threshold, which depends on the time averaged postsynaptic activity. Although the BCM learning rule…
Matthias Kohler, Florian Röhrbein, Alois Knoll, Alin Albu-Schäffer + 1 more
'Henrik Jörntell'] Currently, it is accepted that animal locomotion is controlled by a central pattern generator in the spinal cord. Experiments and models show that rhythm generating neurons and genetically determined network properties could sustain oscillatory output activity suitable for locomotion. However…
Mostafa Rahimi Azghadi, Said F. Al-Sarawi, Derek Abbott, Nicolangelo Iannella
'Nicolangelo Iannella'] Triplet-based Spike Timing Dependent Plasticity (TSTDP) is a powerful synaptic plasticity rule that acts beyond conventional pair-based STDP (PSTDP). Here, the TSTDP is capable of reproducing the outcomes from a variety of biological experiments, while the PSTDP rule fails to reproduce them.…
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.…
Peter Jedlicka, Lubica Benuskova, Wickliffe C. Abraham, Abigail Morrison
'Abigail Morrison'] Long-term potentiation (LTP) and long-term depression (LTD) are widely accepted to be synaptic mechanisms involved in learning and memory. It remains uncertain, however, which particular activity rules are utilized by hippocampal neurons to induce LTP and LTD in behaving animals. Recent experiments…
Wenxuan Pan, Feifei Zhao, Yi Zeng, Bing Han
The architecture design and multi-scale learning principles of the human brain that evolved over hundreds of millions of years are crucial to realizing human-like intelligence. Spiking neural network based Liquid State Machine (LSM) serves as a suitable architecture to study brain-inspired intelligence because of its…
Wenxuan Pan, Feifei Zhao, Yi Zeng, Bing Han
The architecture design and multi-scale learning principles of the human brain that evolved over hundreds of millions of years are crucial to realizing human-like intelligence. Spiking Neural Network (SNN) based Liquid State Machine (LSM) serves as a suitable architecture to study brain-inspired intelligence because of…