Timing in the absence of clocks: encoding time in neural network states.

By Uma R. Karmarkar, Dean V. Buonomano. Neuron, 2007

548 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Plant Science

View Paper: https://www.semanticscholar.org/paper/8ddc7f9c356c4d7d8035c85f47ff8eaee96fef63

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Citations in the corpus

Timing and time perception: A review of recent behavioral and neuroscience findings and theoretical directions (2010, unknown): “A most representative example of this no-dedicated-system view is the one referred to as a state-dependent network (Buonomano, 2007; Karmarkar & Buonomano, 2007).”
Neural basis of the perception and estimation of time. (2013, unknown): “Of course, these two features are not mutually exclusive when a large neural network is considered (Karmarkar & Buonomano 2007, Matell & Meck 2004).”
Reservoir computing approaches to recurrent neural network training (2009, background): “…of why biological brains can carry out accurate computations with an “inaccurate” and noisy physical substrate [22,23], especially accurate timing [24]; of the way in which visual information is superimposed and processed in primary visual cortex [25,26]; of how cortico-basal pathways support the…”
A model of interval timing by neural integration (2011, background): “…et al., 2004; Shea-Brown et al., 2006; Wackermann and Ehm, 2006); synfire chains (Haß et al., 2008); weighted sums of basis functions (Grossberg and Schmajuk, 1989; Machado, 1997; Ludvig et al., 2008); and repeatable stochastic processes (Karmarkar and Buonomano, 2007; Ahrens and Sahani, 2008).”
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).”
Flexible sensorimotor computations through rapid reconfiguration of cortical dynamics (2018, unknown): “, short-term synaptic plasticity) contribute to both interval (Buonomano and Merzenich, 1995; Karmarkar and Buonomano, 2007; Murray and Escola, 2017) and contextual control (Stokes et al.”
Navigating the Neural Space in Search of the Neural Code. (2017, unknown): “Wewill refer to theminimum set of features that control the behavior as the neural code (Johnson, 2000).”
The bored mind is a guiding mind: toward a regulatory theory of boredom (2018, methodology): “…model is widely used, it is lacking in neurological specificity (Matell & Meck, 2000) but attempts to either complement or propose alternative models are currently available (Cordes et al., 2007; Karmarkar & Buonomano, 2007; Matell & Meck, 2004; Wackermann & Ehm, 2006; Wittmann & Wassenhove, 2009).”
The Neural Basis of Timing: Distributed Mechanisms for Diverse Functions (2018, methodology): “Pharmacological (Rammsayer and Vogel, 1992; Rammsayer, 1999), psychophysical (Karmarkar and Buonomano, 2007; Spencer et al., 2009; Rammsayer et al., 2015), and imaging (Lewis and Miall, 2003) studies suggest that discriminating a short interval (e.g., 50–100 ms) recruits different circuits than the…”
Computation Through Neural Population Dynamics (2020, background): “…et al. 2014), Drosophila (Kim et al. 2017), rodents (Allen et al. 2019, Chaudhuri et al. 2019, Stringer et al. 2019), and Caenorhabditis elegans (Kato et al. 2015, Nichols et al. 2017), as well as high channel count electrophysiology via NeuroPixels (e.g., Jun et al. 2017, Stringer et al. 2019,…”
State-dependent computations: spatiotemporal processing in cortical networks (2009, background): “For example, as mentioned above, sensory cortical areas do not generally exhibit self-maintaining activity, and after a brief stimulus they return to low-level firing rates; in these cases the hidden state can provide a memory that bridges the gap between stimul”
Consensus Paper: Roles of the Cerebellum in Motor Control—The Diversity of Ideas on Cerebellar Involvement in Movement (2011, background): “performed by specific brain structures such as the cerebellum, or is temporal encoding a ubiquitous and generic feature of neural activity [150, 162]? Does timing provide a”
Memory without Feedback in a Neural Network (2009, methodology): “…why previous studies of temporal processing by networks with random connectivities, such as those used to model temporal intervals (Buonomano, 2000; Karmarkar and Buonomano, 2007) and temporal classiﬁcation (Maass et al., 2002), were limited in applicability to time intervals on the order of 1 s…”
Neuroanatomical and Neurochemical Substrates of Timing (2011, background): “…time, for example, the temporal integration of steadily climbing activity throughout the duration (Akkal et al, 2004; Lewis and Miall, 2006; Reutimann et al, 2004) or the detection of spatially distinct, duration-specific patterns of firing (Karmarkar and Buonomano, 2007; Matell and Meck, 2004).”
ROBUST TIMING AND MOTOR PATTERNS BY TAMING CHAOS IN RECURRENT NEURAL NETWORKS (2012, background): “…complex sensory stimuli or generate motor patterns (Buonomano and Laje, 2010; Buonomano and Mauk, 1994; Buonomano and Merzenich, 1995; Haeusler and Maass, 2007; Jaeger, 2001; Jaeger and Haas, 2004; Karmarkar and Buonomano, 2007; Maass et al., 2002; Mauk and Donegan, 1997; Medina et al., 2000).”
Dedicated and intrinsic models of time perception. (2008, background): “ized to mean duration is relatively constant for intervals between 200 ms to 2 s but becomes considerably poorer for intervals shorter than this range [ 7 ,72].”