Analysis of Neuron Models with Dynamically Regulated Conductances

Abbott, L. F.; Lemasson, Gwendal

doi:10.1162/neco.1993.5.6.823

Cited by 74 publications

(65 citation statements)

References 17 publications

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“…Following the pioneering work of Abbott and LeMasson (1993) and LeMasson et al (1993), we link changes in the intracellular calcium concentration to the densities with which various ionic channels are expressed across the somatic membrane. Such a pathway is likely to be exceedingly complex and will involve calcium-sensitive genes critical for slow neuronal adaptive responses (Koutalos & Yau, 1996;Ginty, 1997).…”

Section: Discussionmentioning

confidence: 99%

Adaptive Neural Coding Dependent on the Time-Varying Statistics of the Somatic Input Current

1999

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It is generally assumed that nerve cells optimize their performance to reflect the statistics of their input. Electronic circuit analogs of neurons require similar methods of self-optimization for stable and autonomous operation. We here describe and demonstrate a biologically plausible adaptive algorithm that enables a neuron to adapt the current threshold and the slope (or gain) of its current-frequency relationship to match the mean (or dc offset) and variance (or dynamic range or contrast) of the time-varying somatic input current. The adaptation algorithm estimates the somatic current signal from the spike train by way of the intracellular somatic calcium concentration, thereby continuously adjusting the neurons' firing dynamics. This principle is shown to work in an analog VLSI-designed silicon neuron.

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Section: Discussionmentioning

confidence: 99%

Adaptive Neural Coding Dependent on the Time-Varying Statistics of the Somatic Input Current

1999

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“…The underlying premise in this class of models is that, when the activity level drifts away from an equilibrium state, intracellular sensors detect these changed activity levels, and trigger changes in the number and/or distribution of ion channels. (3)(4)(5) In the early generation of these models, (2,3) the activity sensor was a simple measure of the bulk intracellular Ca 2þ as a great deal of experimental data indicates that intracellular Ca 2þ concentrations fluctuate as a function of activity. (28,29) In these models, the stipulation was that excess activity, as detected by the sensor, would trigger a slow decrease in the inward currents and a slow increase in the outward currents, according to a simple negative feedback rule of the form concentration with a different midpoint and slope for each conductance and the regulation time constant t can also be different for different conductances (Fig.…”

Section: Boxmentioning

confidence: 99%

“…We will then discuss a class of self-tuning models (2)(3)(4)(5)(6)(7)(8)(9) in which activity is used as a feedback signal to allow neurons and networks to maintain optimal activity patterns. We will conclude with a brief discussion of recent work that suggests that synaptic strength is also homeostatically controlled.…”

Section: Introductionmentioning

confidence: 99%

Modeling stability in neuron and network function: the role of activity in homeostasis

2002

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SummaryIndividual neurons display characteristic firing patterns determined by the number and kind of ion channels in their membranes. We describe experimental and computational studies that suggest that neurons use activity sensors to regulate the number and kind of ion channels and receptors in their membrane to maintain a stable pattern of activity and to compensate for ongoing processes of degradation, synthesis and insertion of ion channels and receptors. We show that similar neuronal and network outputs can be produced by a number of different combinations of ion channels and synapse strengths. This suggests that individual neurons of the same class may each have found an acceptable solution to a genetically determined pattern of activity, and that networks of neurons in different animals may produce similar output patterns by somewhat variable underlying mechanisms.

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“…In this section, we will discuss both theoretical and experimental work that suggests that activity may also play a critical role in determining the intrinsic properties of neurons (12,(31)(32)(33)(34).…”

Section: Long-term Control Of Intrinsic Conductances: the Role Of Actmentioning

confidence: 99%

Memory from the dynamics of intrinsic membrane currents

Marder

Abbott

Turrigiano

et al. 1996

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

268

199

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Almost all theoretical and experimental studies of the mechanisms underlying learning and memory focus on synaptic efficacy and make the implicit assumption that changes in synaptic efficacy are both necessary and sufficient to account for learning and memory. However, network dynamics depends on the complex interaction between intrinsic membrane properties and synaptic strengths and time courses. Furthermore, neuronal activity itself modifies not only synaptic efficacy but also the intrinsic membrane properties of neurons. This paper presents examples demonstrating that neurons with complex temporal dynamics can provide short-term ''memory'' mechanisms that rely solely on intrinsic neuronal properties. Additionally, we discuss the potential role that activity may play in long-term modification of intrinsic neuronal properties. While not replacing synaptic plasticity as a powerful learning mechanism, these examples suggest that memory in networks results from an ongoing interplay between changes in synaptic efficacy and intrinsic membrane properties.The behavior of rhythmic networks depends on the complex interaction between the dynamics of the individual neurons (their intrinsic properties) and the strengths and time courses of the synapses among them (1-3). Years of research on networks as diverse as central pattern generators in invertebrates and vertebrates (3), the thalamus (4-6), and the cerebellum (7) have clearly shown that complex neuronal characteristics, such as oscillatory and plateau properties, play crucial roles in shaping neural network output. Recent work has expanded the brain regions that show neuronal oscillations to the cortex (8), suggesting that complex intrinsic properties are likely to shape the computational dynamics of many, if not all, brain regions.It is commonly assumed that short and long-lasting changes in synaptic efficacy are both necessary and sufficient to account for the stable changes in network dynamics that we call ''memory''. The remarkable success of early neural network models, in which only synaptic strengths were modified but memories were stored, showed that changes in network output could result solely from changes in synaptic strength (9). An attractive feature of synaptic modification is that it can be restricted to a subset of the synaptic connections made by a neuron, or made onto a neuron. And last, but not least, the experimental work on the Aplysia gill withdrawal reflex and hippocampal slices, in which long-lasting changes in synaptic strength could be produced, and the mechanisms underlying those changes studied (10, 11) provided strong impetus to look primarily at synaptic plasticity as the mechanism underlying memory in intact animals. However, in this paper, we show that intrinsic neuronal currents can also play a role in memory phenomena in at least two different ways. First, a variety of ''short-term'' memory mechanisms can result from slowly activating and inactivating membrane conductances that make the response of a cell depend on its recent...

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Analysis of Neuron Models with Dynamically Regulated Conductances

Cited by 74 publications

References 17 publications

Adaptive Neural Coding Dependent on the Time-Varying Statistics of the Somatic Input Current

Adaptive Neural Coding Dependent on the Time-Varying Statistics of the Somatic Input Current

Modeling stability in neuron and network function: the role of activity in homeostasis

Memory from the dynamics of intrinsic membrane currents

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