The log-dynamic brain: how skewed distributions affect network operations

By György Buzsáki, Kenji Mizuseki. 2014

978 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

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Citations in the corpus

Dropout as an Implicit Gating Mechanism For Continual Learning (2020, background): “[3] showed the neural activity of several biological brains, which is in line with the neural activity in dropout networks, as shown by [2] and our experiments.”
Biologically inspired protection of deep networks from adversarial attacks (2017, background): “In-Biologically deed, high kurtosis is a property shared by weight distributions in biological networks (Buzs ´ aki & Mizuseki, 2014), raising the question of whether or not it plays a functional role in protection against adversarial examples.”
Layer-Specific Organization of Local Excitatory and Inhibitory Synaptic Connectivity in the Rat Presubiculum (2017, background): “These findings support the concept of log-normal processes being a general principle in the brain across different scales (Buzsáki and Mizuseki 2014).”
On the methods for reactivation and replay analysis (2020, background): “, 2007), and only a small fraction of neurons display extremely high firing rates (Buzsáki and Mizuseki, 2014), these analyses can be particularly susceptible to bias from a small fraction of the population.”
The spectral exponent of the resting EEG indexes the presence of consciousness during unresponsiveness induced by propofol, xenon, and ketamine (2019, neutral): “…spatial-scales (He, 2014), e.g. in membrane potential fluctuations (Destexhe et al., 2003), in local field potentials (Milstein et al., 2009; Buzs aki and Mizuseki, 2014), in electrocorticography (ECoG) (He et al., 2010; Miller et al., 2009; Manning et al., 2009; Freeman and Zhai, 2009), in…”
Spectrally resolved fast transient brain states in electrophysiological data (2016, neutral): “, 2015), a feature that is common in the neural system at multiple levels, both anatomical and functional (Buzsaki and Mizuseki, 2014).”
Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems (2016, result): “Indeed, spatial information in hippocampal neurons seems to be concentrated in a small set of neurons [22], an experimental observation that seemed to run counter to the information-theoretic view that whitened representations are most efﬁcient.”
Review of Multimodal Data Acquisition Approaches for Brain–Computer Interfaces (2024, unknown): “This low SNR complicates the isolation of individual neuronal spikes, which is crucial for accurate neural decoding [10].”
An intracranial dissection of human escape circuits (2024, unknown): “Speci ﬁ cally, the power at each time point was normalized by dividing it by the mean power during the baseline period ( – 0.2 to − 0.05s), taking the log and dividing by the standard deviation of the logarithmic baseline values (zlogratio) 106 .”
Mechanisms and Controversies of tACS (Transcranial Alternating Current Stimulation) (2024, background): “The intrinsic firing rates of neurons range from below 0.1 Hz to more than 10 Hz follow a log-normal distribution (Buzsáki & Mizuseki, 2014).”