The neural basis of temporal processing.
By M. Mauk, Dean V. Buonomano. Annual Review of Neuroscience, 2004
956 citations recorded by the source corpus.
Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology
View Paper: https://www.semanticscholar.org/paper/95a11e0c0763268c891e9450f1f09397b6e3ad00
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
- Michael J Frank
- Mehrdad Jazayeri
- Xiao-Jing Wang
- Dean V. Buonomano
- Randall C. O’Reilly
- David W. Tank
- Michael J Frank
- Christopher D. Harvey
In Experimental and Cognitive Psychology
- Simon L. Grondin
- Robert J. Zatorre
- Joyce L. Chen
- Mehrdad Jazayeri
- Virginia B. Penhune
- Rodrigo Laje
- Michael N. Shadlen
- Dean V. Buonomano
Citations in the corpus
- Supervised Learning in Spiking Neural Networks with ReSuMe: Sequence Learning, Classification, and Spike Shifting (2010, background): “Second, it also tests the temporal integration ability of the neural circuits required for the discrimination of inputs on the timescale of hundreds of milliseconds, that is, on the timescale suitable for many cognitive and sensory-motor processes (Mauk & Buonomano, 2004).”
- Decision making, impulsivity and time perception. (2008, background): “Specifically, neural populations within each region could encode duration as a result of specific time-dependent neural changes, such as short-term synaptic plasticity [ 63 ].”
- Timing in the absence of clocks: encoding time in neural network states. (2007, background): “The nervous system processes temporal information over a wide range, from microseconds to circadian rhythms (Carr, 1993; Mauk and Buonomano, 2004; Buhusi and Meck, 2005).”
- ROBUST TIMING AND MOTOR PATTERNS BY TAMING CHAOS IN RECURRENT NEURAL NETWORKS (2012, methodology): “Train the recurrent plastic weights with the innate algorithm as defined by equations (3–5) (the network evolves according to equation (1)) in the presence of noise and with random initial conditions for a number of training loops (training generally converges in between a few loops and a few dozen ”
- State-dependent computations: spatiotemporal processing in cortical networks (2009, background): “However, it is perhaps in the auditory system that temporal processing is most prominent; for example, both the spatial (spectral) and the temporal structure of animal vocalizations — from frog calls to bird song to monkey calls — encode informatio”
- The tempotron: a neuron that learns spike timing–based decisions (2006, background): “However, the tempotron is expected to perform poorly on classification tasks that require it to discriminate between spike patterns on the basis of temporal features that extend beyond a single integration tim”
- When the brain plays music: auditory–motor interactions in music perception and production (2007, background): “, but it has also been hypothesized to be an emergent property of the kinematics of movement itsel”
- Timing and time perception: A review of recent behavioral and neuroscience findings and theoretical directions (2010, positive): “The human organism comprises multiple levels of cyclic activities or sorts of temporal rates, extending from very large scales (e.g., menstrual cycles, circadian rhythm) to much shorter ones (e.g., the firing rate of cortical cells) (see Buonomano, 2007; Mauk & Buonomano, 2004; Wackermann, 2007).”
- The image of time: A voxel-wise meta-analysis (2010, background): “Additionally, Mauk and Buonomano (2004) suggest that the timing of sub-second stimuli may rely on sensory processes, wherein statedependent networks may encode changes in duration (Karmarkar and Buonomano, 2007).”
- Banishing the homunculus: making working memory work. (2006, background): “…the form of the store/ignore/recall (SIR) layer, which can be used to provide explicit working memory update signals that are encoded in a variety of different ways in different tasks, and may also be present via implicit timing signals via the cerebellum (e.g., Mauk & Buonomano, 2004; Ivry, 1996).”
- Movement Timing and Invariance Arise from Several Geometries (2009, neutral): “Mauk and Buanomano [56], stating that ‘‘.”
- Towards an executive without a homunculus: computational models of the prefrontal cortex/basal ganglia system (2007, background): “…in the form of the store/ignore/recall (SIR) layer, which can be used to provide explicit working memory update signals that are encoded in a variety of different ways in different tasks, and may also be present via implicit timing signals via the cerebellum (e.g. Ivry 1996; Mauk & Buonomano 2004).”