495 research outputs found
Stable Hebbian learning from spike timing-dependent plasticity
We explore a synaptic plasticity model that incorporates recent findings that potentiation and depression can be induced by precisely timed pairs of synaptic events and postsynaptic spikes. In addition we include the observation that strong synapses undergo relatively less potentiation than weak synapses, whereas depression is independent of synaptic strength. After random stimulation, the synaptic weights reach an equilibrium distribution which is stable, unimodal, and has positive skew. This weight distribution compares favorably to the distributions of quantal amplitudes and of receptor number observed experimentally in central neurons and contrasts to the distribution found in plasticity models without size-dependent potentiation. Also in contrast to those models, which show strong competition Changes in the synaptic connections between neurons are widely believed to contribute to memory storage, and the activitydependen
Activity Deprivation Reduces Miniature IPSC Amplitude by Decreasing the Number of Postsynaptic GABAA Receptors Clustered at Neocortical Synapses
Synaptic Scaling Balances Learning in a Spiking Model of Neocortex
Learning in the brain requires complementary mechanisms: potentiation and
activity-dependent homeostatic scaling. We introduce synaptic scaling to a
biologically-realistic spiking model of neocortex which can learn changes in
oscillatory rhythms using STDP, and show that scaling is necessary to balance
both positive and negative changes in input from potentiation and atrophy. We
discuss some of the issues that arise when considering synaptic scaling in such
a model, and show that scaling regulates activity whilst allowing learning to
remain unaltered.Comment: 10 page
Attractor Metadynamics in Adapting Neural Networks
Slow adaption processes, like synaptic and intrinsic plasticity, abound in
the brain and shape the landscape for the neural dynamics occurring on
substantially faster timescales. At any given time the network is characterized
by a set of internal parameters, which are adapting continuously, albeit
slowly. This set of parameters defines the number and the location of the
respective adiabatic attractors. The slow evolution of network parameters hence
induces an evolving attractor landscape, a process which we term attractor
metadynamics. We study the nature of the metadynamics of the attractor
landscape for several continuous-time autonomous model networks. We find both
first- and second-order changes in the location of adiabatic attractors and
argue that the study of the continuously evolving attractor landscape
constitutes a powerful tool for understanding the overall development of the
neural dynamics
An Improved Test for Detecting Multiplicative Homeostatic Synaptic Scaling
Homeostatic scaling of synaptic strengths is essential for maintenance of network “gain”, but also poses a risk of losing the distinctions among relative synaptic weights, which are possibly cellular correlates of memory storage. Multiplicative scaling of all synapses has been proposed as a mechanism that would preserve the relative weights among them, because they would all be proportionately adjusted. It is crucial for this hypothesis that all synapses be affected identically, but whether or not this actually occurs is difficult to determine directly. Mathematical tests for multiplicative synaptic scaling are presently carried out on distributions of miniature synaptic current amplitudes, but the accuracy of the test procedure has not been fully validated. We now show that the existence of an amplitude threshold for empirical detection of miniature synaptic currents limits the use of the most common method for detecting multiplicative changes. Our new method circumvents the problem by discarding the potentially distorting subthreshold values after computational scaling. This new method should be useful in assessing the underlying neurophysiological nature of a homeostatic synaptic scaling transformation, and therefore in evaluating its functional significance
Summation of connectivity strengths in the visual cortex reveals stability of neuronal microcircuits after plasticity
Abstract : Background: Within sensory systems, neurons are continuously affected by environmental stimulation. Recently, we showed that, on cell-pair basis, visual adaptation modulates the connectivity strength between similarly tuned neurons to orientation and we suggested that, on a larger scale, the connectivity strength between neurons forming sub-networks could be maintained after adaptation-induced-plasticity. In the present paper, based on the summation of the connectivity strengths, we sought to examine how, within cell-assemblies, functional connectivity is regulated during an exposure-based adaptation. Results: Using intrinsic optical imaging combined with electrophysiological recordings following the reconfiguration of the maps of the primary visual cortex by long stimulus exposure, we found that within functionally connected cells, the summed connectivity strengths remain almost equal although connections among individual pairs are modified. Neuronal selectivity appears to be strongly associated with neuronal connectivity in a “homeodynamic” manner which maintains the stability of cortical functional relationships after experience-dependent plasticity. Conclusions: Our results support the “homeostatic plasticity concept” giving new perspectives on how the summation in visual cortex leads to the stability within labile neuronal ensembles, depending on the newly acquired properties by neurons
Homeostatic Plasticity Studied Using In Vivo Hippocampal Activity-Blockade: Synaptic Scaling, Intrinsic Plasticity and Age-Dependence
Homeostatic plasticity is thought to be important in preventing neuronal circuits from becoming hyper- or hypoactive. However, there is little information concerning homeostatic mechanisms following in vivo manipulations of activity levels. We investigated synaptic scaling and intrinsic plasticity in CA1 pyramidal cells following 2 days of activity-blockade in vivo in adult (postnatal day 30; P30) and juvenile (P15) rats. Chronic activity-blockade in vivo was achieved using the sustained release of the sodium channel blocker tetrodotoxin (TTX) from the plastic polymer Elvax 40W implanted directly above the hippocampus, followed by electrophysiological assessment in slices in vitro. Three sets of results were in general agreement with previous studies on homeostatic responses to in vitro manipulations of activity. First, Schaffer collateral stimulation-evoked field responses were enhanced after 2 days of in vivo TTX application. Second, miniature excitatory postsynaptic current (mEPSC) amplitudes were potentiated. However, the increase in mEPSC amplitudes occurred only in juveniles, and not in adults, indicating age-dependent effects. Third, intrinsic neuronal excitability increased. In contrast, three sets of results sharply differed from previous reports on homeostatic responses to in vitro manipulations of activity. First, miniature inhibitory postsynaptic current (mIPSC) amplitudes were invariably enhanced. Second, multiplicative scaling of mEPSC and mIPSC amplitudes was absent. Third, the frequencies of adult and juvenile mEPSCs and adult mIPSCs were increased, indicating presynaptic alterations. These results provide new insights into in vivo homeostatic plasticity mechanisms with relevance to memory storage, activity-dependent development and neurological diseases
Stable Propagation of a Burst Through a One-Dimensional Homogeneous Excitatory Chain Model of Songbird Nucleus HVC
We demonstrate numerically that a brief burst consisting of two to six spikes
can propagate in a stable manner through a one-dimensional homogeneous
feedforward chain of non-bursting neurons with excitatory synaptic connections.
Our results are obtained for two kinds of neuronal models, leaky
integrate-and-fire (LIF) neurons and Hodgkin-Huxley (HH) neurons with five
conductances. Over a range of parameters such as the maximum synaptic
conductance, both kinds of chains are found to have multiple attractors of
propagating bursts, with each attractor being distinguished by the number of
spikes and total duration of the propagating burst. These results make
plausible the hypothesis that sparse precisely-timed sequential bursts observed
in projection neurons of nucleus HVC of a singing zebra finch are intrinsic and
causally related.Comment: 13 pages, 6 figure
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