Simplified Dynamics of Human and Mammalian Neocortical Neurons

Wilson, Hugh R.

doi:10.1006/jtbi.1999.1002

Cited by 178 publications

(112 citation statements)

References 33 publications

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“…Simplified conductance-based equations for human neocortical neurons are described by Eq. 2 (19). In response to traditional rivalry stimulation, this spiking model produced rivalry alternations of variable duration as shown in Fig.…”

Section: Resultsmentioning

confidence: 97%

“…1, simulations were repeated by using an expanded network incorporating conductance-based neurons. The simplified conductance-based equations used here have been described and shown to produce accurate spike shapes, firing rates, and spike-frequency adaptation for human neocortical neurons (19). The four equations for each neuron describe the membrane potential V, recovery variable R (K ϩ inactivation current), inward Ca 2ϩ current conductance T, and slow Ca 2ϩ mediated K ϩ hyperpolarizing conductance H.…”

Section: [1]mentioning

confidence: 99%

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Computational evidence for a rivalry hierarchy in vision

Wilson

2003

Proc. Natl. Acad. Sci. U.S.A.

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371

427

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Cortical-form vision comprises multiple, hierarchically arranged areas with feedforward and feedback interconnections. This complex architecture poses difficulties for attempts to link perceptual phenomena to activity at a particular level of the system. This difficulty has been especially salient in studies of binocular rivalry alternations, where there is seemingly conflicting evidence for a locus in primary visual cortex or alternatively in higher cortical areas devoted to object perception. Here, I use a competitive neural model to demonstrate that the data require at least two hierarchic rivalry stages for their explanation. This model demonstrates that competitive inhibition in the first rivalry stage can be eliminated by using suitable stimulus dynamics, thereby revealing properties of a later stage, a result obtained with both spike-rate and conductance-based model neurons. This result provides a synthesis of competing rivalry theories and suggests that neural competition may be a general characteristic throughout the form-vision hierarchy.W hen an observer views orthogonal gratings, e.g., vertical and horizontal, one in each eye, the percept is not stable but rather fluctuates among horizontal, vertical, and patchwork mixtures of the two. The traditional interpretation of this rivalry is that it reflects competition between monocularly driven neurons in primary visual cortex (V1), termed interocular rivalry here (1). Two functional MRI studies have tested this idea and provided evidence for the presence of rivalry-correlated alternations in the activation of human V1 (2, 3). Furthermore, recent psychophysical evidence has shown that rivalry transitions generally occur as waves that propagate at constant speed when mapped onto V1 (4). However, primate physiological evidence has suggested that rivalry is more prominent in higher cortical areas such as V4 than in V1 (5). In addition, recent psychophysics has shown that 18.0-Hz on-off flicker of orthogonal monocular gratings coupled with swapping gratings between eyes at 1.5 Hz (3 swaps per sec) leads to perceptual dominance durations of Ϸ2.0 sec (6). This indicates that observers perceive a single pattern while it switches back and forth between eyes several times, a result clearly incompatible with a monocular basis for rivalry under these conditions. Finally, experiments in which the rivaling patterns are objects such as faces and houses are compatible with a higher cortical locus for rivalry (7,8).How can these prima facie conflicting experimental results be reconciled? Certainly one of the most definitive arguments against interocular rivalry is the 18.0-Hz flicker combined with 1.5-Hz eye-switching (F&S) procedure described above (6). The persistence of dominance across several eye swaps, however, has been shown to depend critically on the temporal parameters of the F&S procedure (9), which suggests the hypothesis that F&S stimulus dynamics might bypass or defeat an early interocular stage of rivalry competition, thereby revealing higher levels of bin...

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Section: Resultsmentioning

confidence: 97%

Section: [1]mentioning

confidence: 99%

Computational evidence for a rivalry hierarchy in vision

Wilson

2003

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

371

427

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“…Mathematically, the difference of behavior between these two classes is attributed to that of generation mechanism of action potentials: saddle-node bifurcations (class I), and subcritical Hopf bifurcations (class II). Although the concept of "class" of neurons does not depend on the number of variables, we restrict our arguments for a moment to a reduced form of two-variables, where the first variable, say V, may represent the membrane potential, and the second one, R, an activation state of, e.g., some potassium channels in a generalized sense [10], [12], [19]. With the injected current strength being denoted by I, the single cell equation may be written as: …”

Section: Emergent Chaos In Class I* Neuronsmentioning

confidence: 99%

Neocortical gap junction-coupled interneuron systems may induce chaotic behavior itinerant among quasi-attractors exhibiting transient synchrony

FUJII

2004

Neurocomputing

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Recent discovery of the massive presence of gap junction couplings among neocortical FS (and LTS) interneurons poses serious questions about their collective dynamical behavior, and their possible cognitive roles. We present here the theoretical possibility that a class of neurons coupled by gap junctions may emerge spatio-temporal chaos itinerant among attractors in Milnor's sense, which in turn organizes synchronous cell groups transiently. Some physiological observations from the neocortex, e.g., local field potential (LFP) data exhibiting transient synchrony may provide evidence. We suggest also possible role in the so-called binding problem.

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“…Generally, one approach in computational neuroscience involves creating biologically realistic models, where information about the biological details of neurons including their electrochemistry, biochemistry, and detailed morphology and connectivity are also included [2], such as Hodgkin-Huxley [3] and compartment models [4]. Another approach involves building qualitative models to capture the spiking nature and the essential elements of the behavior with simplified complexity, for example, leaky integrate-and-fire [5], FitzHugh-Nagumo [6], Morris-Lecar [7], Hindmarsh-Rose [8], Wilson [9], Resonate-andFire [10] and Izhikevich [11] neuron models.…”

Section: Introductionmentioning

confidence: 99%

Modeling Neuromorphic Persistent Firing Networks

Ning¹,

Li²,

He³

et al. 2015

IJIS

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Neurons are believed to be the brain computational engines of the brain. A recent discovery in neurophysiology reveals that interneurons can slowly integrate spiking, share the output across a coupled network of axons and respond with persistent firing even in the absence of input to the soma or dendrites, which has not been understood and could be very important for exploring the mechanism of human cognition. The conventional models are incapable of simulating the important newly-discovered phenomenon of persistent firing induced by axonal slow integration. In this paper, we propose a computationally efficient model of neurons through modeling the axon as a slow leaky integrator, which captures almost all-known neural behaviors. The model controls the switching of axonal firing dynamics between passive conduction mode and persistent firing mode. The interplay between the axonal integrated potential and its multiple thresholds in axon precisely determines the persistent firing dynamics of neurons. We also present a persistent firing polychronous spiking network which exhibits asynchronous dynamics indicating that this computationally efficient model is not only bio-plausible, but also suitable for large scale spiking network simulations. The implications of this network and the analog circuit design for exploring the relationship between working memory and persistent firing enable developing a spiking networkbased memory and bio-inspired computer systems.

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Simplified Dynamics of Human and Mammalian Neocortical Neurons

Cited by 178 publications

References 33 publications

Computational evidence for a rivalry hierarchy in vision

Computational evidence for a rivalry hierarchy in vision

Neocortical gap junction-coupled interneuron systems may induce chaotic behavior itinerant among quasi-attractors exhibiting transient synchrony

Modeling Neuromorphic Persistent Firing Networks

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