Information Processing with Frequency-Dependent Synaptic Connections

Markram, Henry; Gupta, A. P.; Uziel, Asher; Wang, Yun; Tsodyks, Misha

doi:10.1006/nlme.1998.3841

Cited by 130 publications

(106 citation statements)

References 62 publications

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“…However, a more complex pattern of responses may emerge with an increasing number of stimuli (Markram et al, 1998). To explore the temporal dynamics of NMDA spike responses with longer activation patterns, we increased the number of synaptic pulses to 10 and varied the ISI between 10 and 1000 ms (n ϭ 6).…”

Section: Temporal Summation With Long Stimulus Trainsmentioning

confidence: 99%

Encoding and Decoding Bursts by NMDA Spikes in Basal Dendrites of Layer 5 Pyramidal Neurons

Polsky

Mel²,

Schiller³

2009

J. Neurosci.

133

155

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Bursts of action potentials are important information-bearing signals in the brain, although the neuronal specializations underlying burst generation and detection are only partially understood. In apical dendrites of neocortical pyramidal neurons, calcium spikes are known to contribute to burst generation, but a comparable understanding of basal dendritic mechanisms is lacking. Here we show that NMDA spikes in basal dendrites mediate both detection and generation of bursts through a postsynaptic mechanism. High-frequency inputs to basal dendrites markedly facilitated NMDA spike initiation compared with low-frequency activation or single inputs. Unlike conventional temporal summation effects based on voltage, however, NMDA spike facilitation depended mainly on residual glutamate bound to NMDA receptors from previous activations. Once triggered by an input burst, we found that NMDA spikes in turn reliably trigger output bursts under in vivo-like stimulus conditions. Through their unique biophysical properties, NMDA spikes are thus ideally suited to promote the propagation of bursts through the cortical network.

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Section: Temporal Summation With Long Stimulus Trainsmentioning

confidence: 99%

Encoding and Decoding Bursts by NMDA Spikes in Basal Dendrites of Layer 5 Pyramidal Neurons

Polsky

Mel²,

Schiller³

2009

J. Neurosci.

133

155

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“…Administration of 20 Hz tetanization, as in our study, was widely used to induce long-term facilitation (LTF) of post-synaptic potentials at crayfish neuromuscular junctions (Wojtowicz andAtwood 1985, Delaney et al 1989), short-term synaptic plasticity in anesthetized fish (Fortune and Rose 2000), long-term potentiation (LTP) in hippocampal slices (Miles and Wong 1987) and modification of synaptic strength in cortical cultures (Jimbo et al 1999). In our simulated networks, tetanization induced both LTP and long-term depression (LTD) of synapses through STDP: firing of a post-synaptic neuron immediately after a pre-synaptic neuron results in LTP of synaptic transmission and the reverse order of firing results in LTD (Levy and Steward 1983, Markram et al 1998, Bi and Poo 1998, Gerstner et al 1996.…”

Section: Simulationmentioning

confidence: 90%

“…Briefly, 1000 leaky-integrateandfire (LIF) model neurons, with a total of 50 000 synapses, were placed randomly in a 3 mm by 3 mm area (see figure 1(c)). All synapses were frequency dependent (Markram et al 1998, Izhikevich et al 2004 to model synaptic depression. 70% of the neurons were excitatory, with spike-timing-dependent plasticity (STDP) (Song et al 2000).…”

Section: Simulationmentioning

confidence: 99%

Region-specific network plasticity in simulated and living cortical networks: comparison of the center of activity trajectory (CAT) with other statistics

Chao

Bakkum²,

Potter³

2007

J. Neural Eng.

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Electrically interfaced cortical networks cultured in vitro can be used as a model for studying the network mechanisms of learning and memory. Lasting changes in functional connectivity have been difficult to detect with extracellular multi-electrode arrays using standard firing rate statistics. We used both simulated and living networks to compare the ability of various statistics to quantify functional plasticity at the network level. Using a simulated integrate-and-fire neural network, we compared five established statistical methods to one of our own design, called center of activity trajectory (CAT). CAT, which depicts dynamics of the location-weighted average of spatiotemporal patterns of action potentials across the physical space of the neuronal circuitry, was the most sensitive statistic for detecting tetanus-induced plasticity in both simulated and living networks. By reducing the dimensionality of multi-unit data while still including spatial information, CAT allows efficient real-time computation of spatiotemporal activity patterns. Thus, CAT will be useful for studies in vivo or in vitro in which the locations of recording sites on multi-electrode probes are important.

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“…Using the TM model, Markram et al (1998b) concluded that the effect of pairing is to selectively regulate low frequency synaptic transmission (Figure 3). Similarly, Markram et al (1998a) reported that pairing results in a selective change in low-frequency synaptic transmission, leaving high-frequency transmission unaffected.…”

Section: Introductionmentioning

confidence: 94%

“…(1998b) slated that The phenomenon produced when Pr changes is readily distinguished fi·om virtually all other types of synaptic changes since changing Pr is a mechanism to selectively regulate low frequency synaptic transmission. 1-lcrc, Pr means probability of release, which is denoted by U in Equations ( 4)-(S);md other types of synaptic changes include changes in the absolute synaptic efficacy, A, or the recovery time constant,''"' Also, Markram et al ( 1998a) noted that ... changes in U ... result in changes in the frequency dependence of transmission, also referred to as redistribution of synaptic efficacy (Markram & Tsodyks, 1996). Specifically, changing U results in a selective change in lowfrequency synaptic transmission, leaving high-frequency transmission unaffected.…”

Section: The Tm Modelmentioning

confidence: 99%

Frequency-dependent synaptic potentiation, depression and spike timing induced by Hebbian pairing in cortical pyramidal neurons

Okatan

Grossberg

2000

Neural Networks

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Information Processing with Frequency-Dependent Synaptic Connections

Cited by 130 publications

References 62 publications

Encoding and Decoding Bursts by NMDA Spikes in Basal Dendrites of Layer 5 Pyramidal Neurons

Encoding and Decoding Bursts by NMDA Spikes in Basal Dendrites of Layer 5 Pyramidal Neurons

Region-specific network plasticity in simulated and living cortical networks: comparison of the center of activity trajectory (CAT) with other statistics

Frequency-dependent synaptic potentiation, depression and spike timing induced by Hebbian pairing in cortical pyramidal neurons

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