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Dean V. Buonomano
Researcher at University of California, Los Angeles
Publications - 100
Citations - 10511
Dean V. Buonomano is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Recurrent neural network & Population. The author has an hindex of 40, co-authored 91 publications receiving 9535 citations. Previous affiliations of Dean V. Buonomano include Oklahoma State University Center for Health Sciences & University of California, Berkeley.
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CORTICAL PLASTICITY: From Synapses to Maps
TL;DR: The goal of the current paper is to review the fields of both synaptic and cortical map plasticity with an emphasis on the work that attempts to unite both fields, to highlight the gaps in the understanding of synaptic and cellular mechanisms underlying cortical representational plasticity.
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The neural basis of temporal processing
TL;DR: It is suggested that, given the intricate link between temporal and spatial information in most sensory and motor tasks, timing and spatial processing are intrinsic properties of neural function, and specialized timing mechanisms such as delay lines, oscillators, or a spectrum of different time constants are not required.
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State-dependent computations: spatiotemporal processing in cortical networks
Dean V. Buonomano,Wolfgang Maass +1 more
TL;DR: Recent theoretical and experimental work suggests that spatiotemporal processing emerges from the interaction between incoming stimuli and the internal dynamic state of neural networks, including not only their ongoing spiking activity but also their 'hidden' neuronal states, such as short-term synaptic plasticity.
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Timing in the absence of clocks: encoding time in neural network states.
TL;DR: An alternate model in which cortical networks are inherently able to tell time as a result of time-dependent changes in network state is examined, showing that within this framework, there is no linear metric of time, and that a given interval is encoded in the context of preceding events.
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Temporal Information Transformed into a Spatial Code by a Neural Network with Realistic Properties
TL;DR: It is demonstrated that known time-dependent neuronal properties enable a network to transform temporal information into a spatial code in a self-organizing manner, with no need to assume a spectrum of time delays or to custom-design the circuit.