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Journal ArticleDOI

Neuronal circuits of the neocortex

Rodney J. Douglas, +1 more
- 24 Jun 2004 - 
- Vol. 27, Iss: 1, pp 419-451
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TLDR
It is found that, as has long been suspected by cortical neuroanatomists, the same basic laminar and tangential organization of the excitatory neurons of the neocortex is evident wherever it has been sought.
Abstract
We explore the extent to which neocortical circuits generalize, i.e., to what extent can neocortical neurons and the circuits they form be considered as canonical? We find that, as has long been suspected by cortical neuroanatomists, the same basic laminar and tangential organization of the excitatory neurons of the neocortex is evident wherever it has been sought. Similarly, the inhibitory neurons show characteristic morphology and patterns of connections throughout the neocortex. We offer a simple model of cortical processing that is consistent with the major features of cortical circuits: The superficial layer neurons within local patches of cortex, and within areas, cooperate to explore all possible interpretations of different cortical input and cooperatively select an interpretation consistent with their various cortical and subcortical inputs.

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Citations
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TL;DR: In this article, the authors present neuromorphic processing devices that emulate the biological style of processing by using parallel instances of mixed-signal analog/digital circuits that operate in real-time.
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Synaptic connections formed by patchy projections of pyramidal cells in the superficial layers of cat visual cortex.

TL;DR: Heterogeneity in the synaptic targets of single cells within the superficial layers of cat V1 is a network feature well-suited for context-dependent processing.
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In vivo two-photon microscopic observation and ablation in deeper brain regions realized by modifications of excitation beam diameter and immersion liquid.

TL;DR: To increase the two-photon excitation efficiency, a method was developed for evaluating the focal volume in living mouse brains and modified the beam diameter of the excitation laser light and the value of the refractive index in the immersion liquid to maximize theexcitation photon density at the focal position.
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Feedforward Inhibition Allows Input Summation to Vary in Recurrent Cortical Networks.

TL;DR: To understand input-output transformations in cortical networks, spiking responses from visual cortex of awake mice of either sex are recorded while pairing sensory stimuli with optogenetic perturbation of excitatory and parvalbumin-positive inhibitory neurons.
Proceedings ArticleDOI

Real-time inference in a VLSI spiking neural network

TL;DR: This work reports a step towards constructing electronic systems with analogous performance to the brain by combining in real-time three distinct sources of input data and implementing more complex probabilistic models such as Bayesian networks in neuromorphic electronic systems.
References
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Journal ArticleDOI

Receptive fields, binocular interaction and functional architecture in the cat's visual cortex

TL;DR: This method is used to examine receptive fields of a more complex type and to make additional observations on binocular interaction and this approach is necessary in order to understand the behaviour of individual cells, but it fails to deal with the problem of the relationship of one cell to its neighbours.
Journal ArticleDOI

Distributed Hierarchical Processing in the Primate Cerebral Cortex

TL;DR: A summary of the layout of cortical areas associated with vision and with other modalities, a computerized database for storing and representing large amounts of information on connectivity patterns, and the application of these data to the analysis of hierarchical organization of the cerebral cortex are reported on.
Journal ArticleDOI

Hierarchical models of object recognition in cortex

TL;DR: A new hierarchical model consistent with physiological data from inferotemporal cortex that accounts for this complex visual task and makes testable predictions is described.
Journal ArticleDOI

Neuromorphic electronic systems

TL;DR: It is shown that for many problems, particularly those in which the input data are ill-conditioned and the computation can be specified in a relative manner, biological solutions are many orders of magnitude more effective than those using digital methods.
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