Browse
Submit Data
Databases
API
Help

Dataset Information

13 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Multiple topological representation self-organized by spike-timing-dependent synaptic learning rule.

ABSTRACT: Position-and-scale-free representations of shapes are acquired by neurons in the inferior temporal (IT) cortex. So each neuron receives information from the whole visual field. Familiar shapes are extremely restricted from all the possible shapes on the whole visual field. So they must be clustered in the shape space to have mixed structure of continuity and discreteness. We demonstrate that multiple representation can be acquired in a spike-based model for topological maps based on the spike-timing-dependent synaptic plasticity (STDP), subjected to a set of inputs on multiple rings, which is a simple example of mixed structure. In this representation, the position on each ring is represented by a center of active neurons and the difference of rings is represented by a detailed pattern of active neurons. Neurons in the same region exhibit high activities for an input on the other ring. The result is consistent with the fact observed in IT cortex that neighboring neurons exhibit different preferences while the region of active neurons is continuously shifted for continuous changes of object.

SUBMITTER: Sakai Y

PROVIDER: S-EPMC2645499 | biostudies-other | 2009 Mar

REPOSITORIES: biostudies-other

ACCESS DATA

Json Xml

Similar Datasets

Self-organized noise resistance of oscillatory neural networks with spike timing-dependent plasticity.

Project description:Intuitively one might expect independent noise to be a powerful tool for desynchronizing a population of synchronized neurons. We here show that, intriguingly, for oscillatory neural populations with adaptive synaptic weights governed by spike timing-dependent plasticity (STDP) the opposite is true. We found that the mean synaptic coupling in such systems increases dynamically in response to the increase of the noise intensity, and there is an optimal noise level, where the amount of synaptic coupling gets maximal in a resonance-like manner as found for the stochastic or coherence resonances, although the mechanism in our case is different. This constitutes a noise-induced self-organization of the synaptic connectivity, which effectively counteracts the desynchronizing impact of independent noise over a wide range of the noise intensity. Given the attempts to counteract neural synchrony underlying tinnitus with noisers and maskers, our results may be of clinical relevance.

| S-EPMC4070574 | biostudies-literature

A spike-timing-dependent plasticity rule for dendritic spines.

Project description:The structural organization of excitatory inputs supporting spike-timing-dependent plasticity (STDP) remains unknown. We performed a spine STDP protocol using two-photon (2P) glutamate uncaging (pre) paired with postsynaptic spikes (post) in layer 5 pyramidal neurons from juvenile mice. Here we report that pre-post pairings that trigger timing-dependent LTP (t-LTP) produce shrinkage of the activated spine neck and increase in synaptic strength; and post-pre pairings that trigger timing-dependent LTD (t-LTD) decrease synaptic strength without affecting spine shape. Furthermore, the induction of t-LTP with 2P glutamate uncaging in clustered spines (<5 μm apart) enhances LTP through a NMDA receptor-mediated spine calcium accumulation and actin polymerization-dependent neck shrinkage, whereas t-LTD was dependent on NMDA receptors and disrupted by the activation of clustered spines but recovered when separated by >40 μm. These results indicate that synaptic cooperativity disrupts t-LTD and extends the temporal window for the induction of t-LTP, leading to STDP only encompassing LTP.

| S-EPMC7449969 | biostudies-literature

Adrenergic Gate Release for Spike Timing-Dependent Synaptic Potentiation.

Project description:Spike timing-dependent synaptic plasticity (STDP) serves as a key cellular correlate of associative learning, which is facilitated by elevated attentional and emotional states involving activation of adrenergic signaling. At cellular levels, adrenergic signaling increases dendrite excitability, but the underlying mechanisms remain elusive. Here we show that activation of ?2-adrenoceptors promoted STD long-term synaptic potentiation at mouse hippocampal excitatory synapses by inactivating dendritic Kv1.1-containing potassium channels, which increased dendrite excitability and facilitated dendritic propagation of postsynaptic depolarization, potentially improving coincidental activation of pre- and postsynaptic terminals. We further demonstrate that adrenergic modulation of Kv1.1 was mediated by the signaling scaffold SAP97, which, through direct protein-protein interactions, escorts ?2 signaling to remove Kv1.1 from the dendrite surface. These results reveal a mechanism through which the postsynaptic signaling scaffolds bridge the aroused brain state to promote induction of synaptic plasticity and potentially to enhance spike timing and memory encoding.

| S-EPMC5267933 | biostudies-literature

Memory-Efficient Synaptic Connectivity for Spike-Timing- Dependent Plasticity.

Project description:Spike-Timing-Dependent Plasticity (STDP) is a bio-inspired local incremental weight update rule commonly used for online learning in spike-based neuromorphic systems. In STDP, the intensity of long-term potentiation and depression in synaptic efficacy (weight) between neurons is expressed as a function of the relative timing between pre- and post-synaptic action potentials (spikes), while the polarity of change is dependent on the order (causality) of the spikes. Online STDP weight updates for causal and acausal relative spike times are activated at the onset of post- and pre-synaptic spike events, respectively, implying access to synaptic connectivity both in forward (pre-to-post) and reverse (post-to-pre) directions. Here we study the impact of different arrangements of synaptic connectivity tables on weight storage and STDP updates for large-scale neuromorphic systems. We analyze the memory efficiency for varying degrees of density in synaptic connectivity, ranging from crossbar arrays for full connectivity to pointer-based lookup for sparse connectivity. The study includes comparison of storage and access costs and efficiencies for each memory arrangement, along with a trade-off analysis of the benefits of each data structure depending on application requirements and budget. Finally, we present an alternative formulation of STDP via a delayed causal update mechanism that permits efficient weight access, requiring no more than forward connectivity lookup. We show functional equivalence of the delayed causal updates to the original STDP formulation, with substantial savings in storage and access costs and efficiencies for networks with sparse synaptic connectivity as typically encountered in large-scale models in computational neuroscience.

| S-EPMC6499189 | biostudies-literature

Perceptron learning rule derived from spike-frequency adaptation and spike-time-dependent plasticity.

Project description:It is widely believed that sensory and motor processing in the brain is based on simple computational primitives rooted in cellular and synaptic physiology. However, many gaps remain in our understanding of the connections between neural computations and biophysical properties of neurons. Here, we show that synaptic spike-time-dependent plasticity (STDP) combined with spike-frequency adaptation (SFA) in a single neuron together approximate the well-known perceptron learning rule. Our calculations and integrate-and-fire simulations reveal that delayed inputs to a neuron endowed with STDP and SFA precisely instruct neural responses to earlier arriving inputs. We demonstrate this mechanism on a developmental example of auditory map formation guided by visual inputs, as observed in the external nucleus of the inferior colliculus (ICX) of barn owls. The interplay of SFA and STDP in model ICX neurons precisely transfers the tuning curve from the visual modality onto the auditory modality, demonstrating a useful computation for multimodal and sensory-guided processing.

| S-EPMC2842046 | biostudies-literature

The Interplay of Synaptic Plasticity and Scaling Enables Self-Organized Formation and Allocation of Multiple Memory Representations.

Project description:It is commonly assumed that memories about experienced stimuli are represented by groups of highly interconnected neurons called cell assemblies. This requires allocating and storing information in the neural circuitry, which happens through synaptic weight adaptations at different types of synapses. In general, memory allocation is associated with synaptic changes at feed-forward synapses while memory storage is linked with adaptation of recurrent connections. It remains, however, largely unknown how memory allocation and storage can be achieved and the adaption of the different synapses involved be coordinated to allow for a faithful representation of multiple memories without disruptive interference between them. In this theoretical study, by using network simulations and phase space analyses, we show that the interplay between long-term synaptic plasticity and homeostatic synaptic scaling organizes simultaneously the adaptations of feed-forward and recurrent synapses such that a new stimulus forms a new memory and where different stimuli are assigned to distinct cell assemblies. The resulting dynamics can reproduce experimental in-vivo data, focusing on how diverse factors, such as neuronal excitability and network connectivity, influence memory formation. Thus, the here presented model suggests that a few fundamental synaptic mechanisms may suffice to implement memory allocation and storage in neural circuitry.

| S-EPMC7575689 | biostudies-literature

Neuromodulators control the polarity of spike-timing-dependent synaptic plasticity.

Project description:Near coincidental pre- and postsynaptic action potentials induce associative long-term potentiation (LTP) or long-term depression (LTD), depending on the order of their timing. Here, we show that in visual cortex the rules of this spike-timing-dependent plasticity are not rigid, but shaped by neuromodulator receptors coupled to adenylyl cyclase (AC) and phospholipase C (PLC) signaling cascades. Activation of the AC and PLC cascades results in phosphorylation of postsynaptic glutamate receptors at sites that serve as specific "tags" for LTP and LTD. As a consequence, the outcome (i.e., whether LTP or LTD) of a given pattern of pre- and postsynaptic firing depends not only on the order of the timing, but also on the relative activation of neuromodulator receptors coupled to AC and PLC. These findings indicate that cholinergic and adrenergic neuromodulation associated with the behavioral state of the animal can control the gating and the polarity of cortical plasticity.

| S-EPMC2756178 | biostudies-literature

A synaptic learning rule for exploiting nonlinear dendritic computation.

Project description:Information processing in the brain depends on the integration of synaptic input distributed throughout neuronal dendrites. Dendritic integration is a hierarchical process, proposed to be equivalent to integration by a multilayer network, potentially endowing single neurons with substantial computational power. However, whether neurons can learn to harness dendritic properties to realize this potential is unknown. Here, we develop a learning rule from dendritic cable theory and use it to investigate the processing capacity of a detailed pyramidal neuron model. We show that computations using spatial or temporal features of synaptic input patterns can be learned, and even synergistically combined, to solve a canonical nonlinear feature-binding problem. The voltage dependence of the learning rule drives coactive synapses to engage dendritic nonlinearities, whereas spike-timing dependence shapes the time course of subthreshold potentials. Dendritic input-output relationships can therefore be flexibly tuned through synaptic plasticity, allowing optimal implementation of nonlinear functions by single neurons.

| S-EPMC8691952 | biostudies-literature

Self-organized reactivation maintains and reinforces memories despite synaptic turnover.

Project description:Long-term memories are believed to be stored in the synapses of cortical neuronal networks. However, recent experiments report continuous creation and removal of cortical synapses, which raises the question how memories can survive on such a variable substrate. Here, we study the formation and retention of associative memory in a computational model based on Hebbian cell assemblies in the presence of both synaptic and structural plasticity. During rest periods, such as may occur during sleep, the assemblies reactivate spontaneously, reinforcing memories against ongoing synapse removal and replacement. Brief daily reactivations during rest-periods suffice to not only maintain the assemblies, but even strengthen them, and improve pattern completion, consistent with offline memory gains observed experimentally. While the connectivity inside memory representations is strengthened during rest phases, connections in the rest of the network decay and vanish thus reconciling apparently conflicting hypotheses of the influence of sleep on cortical connectivity.

| S-EPMC6546393 | biostudies-literature

Spike-shape dependence of the spike-timing dependent synaptic plasticity in ferroelectric-tunnel-junction synapses.

Project description:Resistive switching (RS) devices have attracted increasing attention for artificial synapse applications in neural networks because of their nonvolatile and analogue resistance changes. Among the neural networks, a spiking neural network (SNN) based on spike-timing-dependent plasticity (STDP) is highly energy efficient. To implement STDP in resistive switching devices, several types of voltage spikes have been proposed to date, but there have been few reports on the relationship between the STDP characteristics and spike types. Here, we report the STDP characteristics implemented in ferroelectric tunnel junctions (FTJs) by several types of spikes. Based on simulated time evolutions of superimposed spikes and taking the nonlinear current-voltage (I-V) characteristics of FTJs into account, we propose equations for simulating the STDP curve parameters of a magnitude of the conductance change (?Gmax) and a time window (?C) from the spike parameters of a peak amplitude (Vpeak) and time durations (tp and td) for three spike types: triangle-triangle, rectangular-triangle, and rectangular-rectangular. The power consumption experiments of the STDP revealed that the power consumption under the inactive-synapse condition (spike timing |?t|?>??C) was as large as 50-82% of that under the active-synapse condition (|?t|?<??C). This finding indicates that the power consumption under the inactive-synapse condition should be reduced to minimize the total power consumption of an SNN implemented by using FTJs as synapses.

| S-EPMC6882828 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data