The role of single neurons in information processing

By Christof Koch, Idan Segev. Nature Neuroscience, 2000

548 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/ea93daa3ff642201531ca6e2bc1bc3b51815b9fe

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Evolution of Cartesian Genetic Programs for Development of Learning Neural Architecture (2011, background): ““Dendritic trees enhance computational power” and dendrites themselves should not be regarded as passive entities that simply collect and pass synaptic inputs to the soma (Koch and Segev, 2000).”
Dendritic Neuron Model With Effective Learning Algorithms for Classification, Approximation, and Prediction (2019, methodology): “Taking the advantage of the multiplication operation that is essential and important to the information processing in a neuron [20], the computation in synapses is innovatively modeled by using sigmoid functions.”
In search of intelligence: evolving a developmental neuron capable of learning (2014, background): “From the literature it is evident that ‘dendritic trees enhance computational power’ (Koch & Segev, 2000).”
Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties (2011, background): “Such active dendritic properties are suggested to play a key role in information processing [14], nonlinear computations [15–16] and synaptic integration [17–18].”
Active properties of neocortical pyramidal neuron dendrites. (2013, background): “Actually understanding any given neuron requires the right level of abstraction (Branco & Häusser 2010, Häusser & Mel 2003, Herz et al. 2006, Koch et al. 1983, Koch & Segev 2000, Larkum et al. 2001, Mel 1994, Poirazi et al. 2003, Spruston 2008).”
On Having No Head: Cognition throughout Biological Systems (2016, background): “While the dominant model of neural-based cognition relies on the signaling dynamics among networks of neurons, it’s becoming increasingly appreciated that single neurons can execute subtraction, addition, low- and band-pass filtering, normalization, gain control, saturation, amplification, multiplic”
Basis for a neuronal version of Grover's quantum algorithm (2014, background): “Unsurprisingly, given their preeminent status as computational units (cf. Koch and Segev, 2000; Grillner, 2006), single neurons are still favored models for bioinspired smart technologies (e.g., Liu et al., 2013).”
Chronic stress alters dendritic morphology in rat medial prefrontal cortex. (2004, background): “, dendritic branching patterns, distribution, and overall shape) determines many functional properties of neurons (e.g., Rall et al., 1992; Mainen and Sejnowksi, 1996; Koch and Segev, 2000; Lu et al., 2001; Grudt and Perl, 2002), the pronounced stress-induced dendritic changes documented here likely”
Dendritic computation. (2005, background): “…past decade a rapidly increasing number of studies investigating signaling mechanisms in dendrites have appeared, and several recent reviews have discussed these findings in depth (Euler & Denk 2001, Häusser et al. 2000, Koch & Segev 2000, Magee 2000, Segev & London 2000, Williams & Stuart 2003).”
Stochastic resonance and sensory information processing: a tutorial and review of application. (2004, background): “…to be a widespread characteristic of neuronal mechanisms involved in vision (for instance, simple cells in V1 feed forward to complex cells with a threshold nonlinearity according to the canonical model by Hubel and Wiesel) (Maunsell and Van Essen, 1983; Heeger et al., 1996; Koch and Segev, 2000).”