Phase changes in neuronal postsynaptic spiking due to short term plasticity.
Ontology highlight
ABSTRACT:
SUBMITTER: McDonnell MD
PROVIDER: S-EPMC5627952 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Sensory areas of cortex respond more strongly to infrequent stimuli when these violate previously established regularities, a phenomenon known as deviance detection (DD). Previous modeling work has mainly attempted to explain DD on the basis of synaptic plasticity. However, a large fraction of cortical neurons also exhibit firing rate adaptation, an underexplored potential mechanism. Here, we investigate DD in a spiking neuronal network model with two types of short-term plasticity, fast synaptic short-term depression (STD) and slower threshold adaptation (TA). We probe the model with an oddball stimulation paradigm and assess DD by evaluating the network responses. We find that TA is sufficient to elicit DD. It achieves this by habituating neurons near the stimulation site that respond earliest to the frequently presented standard stimulus (local fatigue), which diminishes the response and promotes the recovery (global fatigue) of the wider network. Further, we find a synergy effect between STD and TA, where they interact with each other to achieve greater DD than the sum of their individual effects. We show that this synergy is caused by the local fatigue added by STD, which inhibits the global response to the frequently presented stimulus, allowing greater recovery of TA-mediated global fatigue and making the network more responsive to the deviant stimulus. Finally, we show that the magnitude of DD strongly depends on the timescale of stimulation. We conclude that highly predictable information can be encoded in strong local fatigue, which allows greater global recovery and subsequent heightened sensitivity for DD.
| S-EPMC10599548 | biostudies-literature
Project description: Not available
| S-EPMC10652417 | biostudies-literature
Project description:Postsynaptic release of Ca(2+) from intracellular stores is an important means of cellular signaling that mediates numerous forms of synaptic plasticity. Previous studies have identified a postsynaptic intracellular Ca(2+) requirement for a form of short-term plasticity, post-tetanic potentiation (PTP) at sensory neuron (SN)-motor neuron synapses in Aplysia. Here, we show that postsynaptic IP(3)-mediated Ca(2+) release in response to a presynaptic tetanus in an SN that induces PTP can confer transient plasticity onto a neighboring SN synapse receiving subthreshold activation. This heterosynaptic sharing of plasticity represents a dynamic, short-term synaptic enhancement of synaptic inputs onto a common postsynaptic target. Heterosynaptic sharing is blocked by postsynaptic disruption of Ca(2+)- and IP(3)-mediated signaling, and, conversely, it is mimicked by postsynaptic injection of nonhydrolyzable IP(3), and by photolysis of caged IP(3) in the MN. The molecular mechanism for heterosynaptic sharing involves metabotropic glutamate receptors and Homer-dependent interactions, indicating that Homer can facilitate the integration of Ca(2+)-dependent plasticity at neighboring postsynaptic sites and provides a postsynaptic mechanism for the spread of plasticity induced by presynaptic activation. Our results support a model in which postsynaptic summation of IP(3) signals from suprathreshold and subthreshold inputs results in molecular coincidence detection that gives rise to a novel form of heterosynaptic plasticity.
| S-EPMC2911352 | biostudies-literature
Project description:Working Memory (WM) is a cognitive mechanism that enables temporary holding and manipulation of information in the human brain. This mechanism is mainly characterized by a neuronal activity during which neuron populations are able to maintain an enhanced spiking activity after being triggered by a short external cue. In this study, we implement, using the NEST simulator, a spiking neural network model in which the WM activity is sustained by a mechanism of short-term synaptic facilitation related to presynaptic calcium kinetics. The model, which is characterized by leaky integrate-and-fire neurons with exponential postsynaptic currents, is able to autonomously show an activity regime in which the memory information can be stored in a synaptic form as a result of synaptic facilitation, with spiking activity functional to facilitation maintenance. The network is able to simultaneously keep multiple memories by showing an alternated synchronous activity which preserves the synaptic facilitation within the neuron populations holding memory information. The results shown in this study confirm that a WM mechanism can be sustained by synaptic facilitation.
| S-EPMC9574057 | biostudies-literature
Project description:Bottom-up models of functionally relevant patterns of neural activity provide an explicit link between neuronal dynamics and computation. A prime example of functional activity patterns are propagating bursts of place-cell activities called hippocampal replay, which is critical for memory consolidation. The sudden and repeated occurrences of these burst states during ongoing neural activity suggest metastable neural circuit dynamics. As metastability has been attributed to noise and/or slow fatigue mechanisms, we propose a concise mesoscopic model which accounts for both. Crucially, our model is bottom-up: it is analytically derived from the dynamics of finite-size networks of Linear-Nonlinear Poisson neurons with short-term synaptic depression. As such, noise is explicitly linked to stochastic spiking and network size, and fatigue is explicitly linked to synaptic dynamics. To derive the mesoscopic model, we first consider a homogeneous spiking neural network and follow the temporal coarse-graining approach of Gillespie to obtain a "chemical Langevin equation", which can be naturally interpreted as a stochastic neural mass model. The Langevin equation is computationally inexpensive to simulate and enables a thorough study of metastable dynamics in classical setups (population spikes and Up-Down-states dynamics) by means of phase-plane analysis. An extension of the Langevin equation for small network sizes is also presented. The stochastic neural mass model constitutes the basic component of our mesoscopic model for replay. We show that the mesoscopic model faithfully captures the statistical structure of individual replayed trajectories in microscopic simulations and in previously reported experimental data. Moreover, compared to the deterministic Romani-Tsodyks model of place-cell dynamics, it exhibits a higher level of variability regarding order, direction and timing of replayed trajectories, which seems biologically more plausible and could be functionally desirable. This variability is the product of a new dynamical regime where metastability emerges from a complex interplay between finite-size fluctuations and local fatigue.
| S-EPMC9822116 | biostudies-literature
Project description:The ability to hold multiple objects in memory is fundamental to intelligent behavior, but its neural basis remains poorly understood. It has been suggested that multiple items may be held in memory by oscillatory activity across neuronal populations, but yet there is little direct evidence. Here, we show that neuronal information about two objects held in short-term memory is enhanced at specific phases of underlying oscillatory population activity. We recorded neuronal activity from the prefrontal cortices of monkeys remembering two visual objects over a brief interval. We found that during this memory interval prefrontal population activity was rhythmically synchronized at frequencies around 32 and 3 Hz and that spikes carried the most information about the memorized objects at specific phases. Further, according to their order of presentation, optimal encoding of the first presented object was significantly earlier in the 32 Hz cycle than that for the second object. Our results suggest that oscillatory neuronal synchronization mediates a phase-dependent coding of memorized objects in the prefrontal cortex. Encoding at distinct phases may play a role for disambiguating information about multiple objects in short-term memory.
| S-EPMC2779828 | biostudies-literature
Project description:Disrupting the pathological synchronous firing patterns of neurons with high frequency stimulation is a common treatment for Parkinsonian symptoms and epileptic seizures when pharmaceutical drugs fail. In this paper, our goal is to design a desynchronization strategy for large networks of spiking neurons such that the neuronal activity of the network remains in the desynchronized regime for a long period of time after the removal of the stimulation. We develop a novel "Forced Temporal-Spike Time Stimulation (FTSTS)" strategy that harnesses the spike-timing dependent plasticity to control the synchronization of neural activity in the network by forcing the neurons in the network to artificially fire in a specific temporal pattern. Our strategy modulates the synaptic strengths of selective synapses to achieve a desired synchrony of neural activity in the network. Our simulation results show that the FTSTS strategy can effectively synchronize or desynchronize neural activity in large spiking neuron networks and keep them in the desired state for a long period of time after the removal of the external stimulation. Using simulations, we demonstrate the robustness of our strategy in desynchronizing neural activity of networks against uncertainties in the designed stimulation pulses and network parameters. Additionally, we show in simulation, how our strategy could be incorporated within the existing desynchronization strategies to improve their overall efficacy in desynchronizing large networks. Our proposed strategy provides complete control over the synchronization of neurons in large networks and can be used to either synchronize or desynchronize neural activity based on specific applications. Moreover, it can be incorporated within other desynchronization strategies to improve the efficacy of existing therapies for numerous neurological and psychiatric disorders associated with pathological synchronization.
| S-EPMC6737503 | biostudies-literature
Project description:Ca2+/calmodulin-dependent kinase IIα (CaMKIIα) is essential for synaptic plasticity and learning by decoding synaptic Ca2+ oscillations. Despite decades of extensive research, new mechanisms underlying CaMKIIα's function in synapses are still being discovered. Here, we discover that Shank3 is a specific binding partner for autoinhibited CaMKIIα. We demonstrate that Shank3 and GluN2B, via combined actions of Ca2+ and phosphatases, reciprocally bind to CaMKIIα. Under basal condition, CaMKIIα is recruited to the Shank3 subcompartment of postsynaptic density (PSD) via phase separation. Rise of Ca2+ concentration induces GluN2B-mediated recruitment of active CaMKIIα and formation of the CaMKIIα/GluN2B/PSD-95 condensates, which are autonomously dispersed upon Ca2+ removal. Protein phosphatases control the Ca2+-dependent shuttling of CaMKIIα between the two PSD subcompartments and PSD condensate formation. Activation of CaMKIIα further enlarges the PSD assembly and induces structural LTP. Thus, Ca2+-induced and phosphatase-checked shuttling of CaMKIIα between distinct PSD nano-domains can regulate phase separation-mediated PSD assembly and synaptic plasticity.
| S-EPMC7852677 | biostudies-literature
Project description:Although it is well known that long-term synaptic plasticity can be expressed both pre- and postsynaptically, the functional consequences of this arrangement have remained elusive. We show that spike-timing-dependent plasticity with both pre- and postsynaptic expression develops receptive fields with reduced variability and improved discriminability compared to postsynaptic plasticity alone. These long-term modifications in receptive field statistics match recent sensory perception experiments. Moreover, learning with this form of plasticity leaves a hidden postsynaptic memory trace that enables fast relearning of previously stored information, providing a cellular substrate for memory savings. Our results reveal essential roles for presynaptic plasticity that are missed when only postsynaptic expression of long-term plasticity is considered, and suggest an experience-dependent distribution of pre- and postsynaptic strength changes.
| S-EPMC4584257 | biostudies-literature
Project description:Dynamics of an interconnected population of excitatory and inhibitory spiking neurons wandering around a Bogdanov-Takens (BT) bifurcation point can generate the observed scale-free avalanches at the population level and the highly variable spike patterns of individual neurons. These characteristics match experimental findings for spontaneous intrinsic activity in the brain. In this paper, we address the mechanisms causing the system to get and remain near this BT point. We propose an effective stochastic neural field model which captures the dynamics of the mean-field model. We show how the network tunes itself through local long-term synaptic plasticity by STDP and short-term synaptic depression to be close to this bifurcation point. The mesoscopic model that we derive matches the directed percolation model at the absorbing state phase transition.
| S-EPMC9588946 | biostudies-literature