State-dependent computations: spatiotemporal processing in cortical networks

By Dean V. Buonomano, Wolfgang Maass. 2009

912 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Electrical and Electronic Engineering

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Citations in the corpus

Lifelong learning of human actions with deep neural network self-organization (2017, background): “…using spiking neural networks have been proposed in which spatiotemporal information is encoded as locations of synapses and neurons taking into account the time of their spiking activity (Buonomano & Maass, 2009; Kasabov, 2014; Kasabov, Doborjeh, & Doborjeh, 2017; Zenke, Agnes, & Gerstner, 2015).”
Re-evaluating circuit mechanisms underlying pattern separation (2019, neutral): “, 2015), in analogy with reservoir computing (Yamazaki and Tanaka, 2007; Buonomano and Maass, 2009; Sussillo and Abbott, 2009).”
Network Structure within the Cerebellar Input Layer Enables Lossless Sparse Encoding (2014, background): “Recurrent connections introduce loops that can support attractor states, intrinsic activity, and complex nonlinear dynamics (Buonomano and Maass, 2009) and aid receptive field formation and feature extraction (Somers et al., 1995).”
Temporal context calibrates interval timing (2010, background): “and biophysical models have been developed that suggest that time may be represented through the dynamics of neuronal networ”
Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons (2014, background): “(20) with the matrix G given in equation (12), so that the linear stability analysis in the Poisson network is identical to that in the LIF network.”
Reservoir Computing Trends (2012, background): “Reservoirs also correspond well to how temporal information is spatially encoded in the brain and provides a context for interpreting current inputs [6].”
Towards the neural population doctrine. (2019, methodology): “Artiﬁcial models with a feedforward and recurrent architecture have been used to successfully capture dynamical activity [49,50].”
Neural population coding: combining insights from microscopic and mass signals (2015, background): “Local neural activity not only depends on the current sensory input but also on the current brain state [71,72].”
Information Processing Capacity of Dynamical Systems (2012, background): “Although it has been argued that short-term memory of a neural system is crucial for the brain to perform useful computation on sensory input streams 15,16 , it is our belief that the general paradigm underlying the brain’s computation cannot solely rely on maximizing linear memory.”
Low-dimensional dynamics for working memory and time encoding (2020, background): “In these networks, time decoding should be possible in every brain area regardless of whether or not timing information is relevant for the task (12).”
Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules (2018, background): “For example, memory traces hidden in the rich firing activity patterns of a recurrent network (Maass et al., 2002; Jaeger and Haas, 2004; Buonomano and Maass, 2009; Susillo and Abbott, 2009) or short-term synaptic plasticity in recurrent networks (Mongillo et al.”