Spike timing-dependent plasticity and memory

Debanne, Dominique; Inglebert, Yanis

doi:10.1016/j.conb.2023.102707

Cited by 30 publications

(24 citation statements)

References 111 publications

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“…One could use parameter U there as a synaptic weight obeyed to STDP. However, as several experiments have shown that inhibitory STDP is strongly dependent on developmental stage of the neural tissue [110], we believe that mathematical modeling of this phenomenon is beyond the scope of this article. Moreover, recent experiments indicate similar developmental-stage dependence even for the “classical” excitatory STDP implied in Eqs.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, recent experiments indicate similar developmental-stage dependence even for the “classical” excitatory STDP implied in Eqs. (29) [110]. We therefore emphasize that our consideration of the STDP impact here is purely speculative.…”

Section: Discussionmentioning

confidence: 99%

“…It means that even a small change in some of the network model parameters can lead to a substantial reorganization of the whole map of n-sites. A potential mechanism for such changes is the Hebbian plasticity, in particular, Spike-Timing–Dependent Plasticity (STDP) [107–110]. The latter can be directly implemented into the model as an additional dynamics of u, the fraction of recovered synaptic resource used to transmit the signal across the synapse, in Eqs.…”

Section: Discussionmentioning

confidence: 99%

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The vitals for steady nucleation maps of spontaneous spiking coherence in autonomous two-dimensional neuronal networks

Zendrikov,

Paraskevov

2024

Preprint

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Thin pancake-like neuronal networks cultured on top of a planar microelectrode array have been extensively tried out in neuroengineering, as a substrate for the mobile robot’s control unit, i.e., as a cyborg’s brain. Most of these attempts failed due to intricate self-organizing dynamics in the neuronal systems. In particular, the networks may exhibit an emergent spatial map of steady nucleation sites (“n-sites”) of spontaneous population spikes. Being unpredictable and independent of the surface electrode locations, the n-sites drastically change local ability of the network to generate spikes. Here, using a spiking neuronal network model with generative spatially-embedded connectome, we systematically show in simulations that the number, location, and relative activity of spontaneously formed n-sites (“the vitals”) crucially depend on the samplings of three distributions: 1) the network distribution of neuronal excitability, 2) the distribution of connections between neurons of the network, and 3) the distribution of maximal amplitudes of a single synaptic current pulse. Moreover, blocking the dynamics of a small fraction (about 4%) of non-pacemaker neurons having the highest excitability was enough to completely suppress the occurrence of population spikes and their n-sites. This key result is explained theoretically. Remarkably, the n-sites occur taking into account only short-term synaptic plasticity, i.e., without a Hebbian-type plasticity. As the spiking network model used in this study is strictly deterministic, all simulation results can be accurately reproduced. The model, which has already demonstrated a very high richness-to-complexity ratio, can also be directly extended into the three-dimensional case, e.g., for targeting peculiarities of spiking dynamics in cerebral (or brain) organoids. We recommend the model as an excellent illustrative tool for teaching network-level computational neuroscience, complementing a few benchmark models.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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The vitals for steady nucleation maps of spontaneous spiking coherence in autonomous two-dimensional neuronal networks

Zendrikov,

Paraskevov

2024

Preprint

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“…In addition, Cm changes will likely alter the velocity and reach of action potentials back-propagating into apical dendrites. In turn, this can affect the modification of distal synapses induced via spike-timing dependent plasticity (STDP), because STDP depends crucially on the coincidence of synaptic activation with the arrival of the action potential (see (Debanne and Inglebert 2023)).…”

Section: Discussionmentioning

confidence: 99%

Daily membrane capacitance changes in mouse neurons

Severín

Shirley

Kirkwood

et al. 2022

Preprint

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Capacitance is a property of biological membranes determined by the properties of the lipid portion of the membrane, as well as morphological features of a cell. In neurons, membrane capacitance is a determining factor of synaptic integration, action potential propagation speed and firing frequency. Besides slow changes associated with increased morphological complexity during postnatal maturation, neuron membrane capacity is largely considered a stable, non-regulated constant magnitude. Here we report that in pyramidal cells of mouse primary visual cortex the membrane capacitance changes nearly two-fold between the start and the end of a daily light cycle. The changes are large, nearly two-fold in magnitude in pyramidal cells, but do not affect cortical parvalbumin-expressing inhibitory interneurons. We discuss possible functional and practical implications as well as potential mechanisms.

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“…Several studies have attributed the formation of long-term memory traces to spike timing dependent plasticity (STDP). [6,[35][36][37][38][39][40][41][42]. STDP governs changes in synaptic weights based on the timing of pre-and postsynaptic action potentials, and may lead to either long-term potentiation (LTP) or depression (LTD) of synaptic strengths [43][44][45][46].…”

Section: Introductionmentioning

confidence: 99%

The role of NMDA receptors in memory and prediction in cultured neural networks

Lamberti,

van Putten,

Marzen

et al. 2024

Preprint

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Memory has been extensively studied at the behavioural as well as the cellular level. Spike timing dependent plasticity (STDP) is associated with N-methyl-D-aspartate (NMDA) receptor activation and is widely accepted to be essential for long-term memory. However, experimental evidence remains sparse, probably due to the required complex combination of cellular and functional readouts. Recent work showed that in-vitro cortical networks memorize and predict inputs. The initial dependency of prediction on short-term memory decreased during the formation of long-term memory traces. Here, we stimulated in-vitro networks to investigate memory and prediction under control conditions, or using the NMDA antagonist APV. APV did not significantly reduce network excitability, but did impede long-term memory trace formation. In APV-treated cultures short-term memory of stimuli persisted, and they were still able to predict. In contrast to control cultures, prediction remained fully dependent on short-term memory. This confirms that NMDA receptor activation is essential for the formation of long-term memory traces and supports the notion that, as control cultures learn to memorize the stimulus, long-term memory starts to contribute to their predictive capability.

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Spike timing-dependent plasticity and memory

Cited by 30 publications

References 111 publications

The vitals for steady nucleation maps of spontaneous spiking coherence in autonomous two-dimensional neuronal networks

The vitals for steady nucleation maps of spontaneous spiking coherence in autonomous two-dimensional neuronal networks

Daily membrane capacitance changes in mouse neurons

The role of NMDA receptors in memory and prediction in cultured neural networks

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