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Biomedical subjects

L F Abbott

Publications and source records attributed to L F Abbott.

At least 73 records · Page 4Linked to original sources
PubMed1994

Activity-dependent current distributions in model neurons.

The electrical activity of a neuron can affect its intrinsic physiological characteristics through a wide range of processes. We study a computer-simulated multicompartment model neuron in which channel density depends on local Ca2+ concentrations. This has three interesting consequences for the spatial distribution of conductances and the physiological behavior of the neuron: (i) the model neuron spontaneously develops a realistic, nonuniform distribution of conductances that is linked both to the morphology of the neuron and to the pattern of synaptic input that it receives, (ii) the response to synaptic input reveals a form of intrinsic localized plasticity that balances the synaptic contribution from dendritic regions receiving unequal stimulation, and (iii) intrinsic plasticity establishes a biophysical gain control that restores the neuron to its optimal firing range after synapses are strengthened by "Hebbian" long-term potentiation.

Calcium↗
PubMed1994

Activity-dependent changes in the intrinsic properties of cultured neurons.

Learning and memory arise through activity-dependent modifications of neural circuits. Although the activity dependence of synaptic efficacy has been studied extensively, less is known about how activity shapes the intrinsic electrical properties of neurons. Lobster stomatogastric ganglion neurons fire in bursts when receiving synaptic and modulatory input but fire tonically when pharmacologically isolated. Long-term isolation in culture changed their intrinsic activity from tonic firing to burst firing. Rhythmic stimulation reversed this transition through a mechanism that was mediated by a rise in intracellular calcium concentration. These data suggest that neurons regulate their conductances to maintain stable activity patterns and that the intrinsic properties of a neuron depend on its recent history of activation.

Animals↗
PubMed1994

When inhibition not excitation synchronizes neural firing.

Excitatory and inhibitory synaptic coupling can have counter-intuitive effects on the synchronization of neuronal firing. While it might appear that excitatory coupling would lead to synchronization, we show that frequently inhibition rather than excitation synchronizes firing. We study two identical neurons described by integrate-and-fire models, general phase-coupled models or the Hodgkin-Huxley model with mutual, non-instantaneous excitatory or inhibitory synapses between them. We find that if the rise time of the synapse is longer than the duration of an action potential, inhibition not excitation leads to synchronized firing.

Action Potentials↗
PubMed1994

Vector reconstruction from firing rates.

In a number of systems including wind detection in the cricket, visual motion perception and coding of arm movement direction in the monkey and place cell response to position in the rat hippocampus, firing rates in a population of tuned neurons are correlated with a vector quantity. We examine and compare several methods that allow the coded vector to be reconstructed from measured firing rates. In cases where the neuronal tuning curves resemble cosines, linear reconstruction methods work as well as more complex statistical methods requiring more detailed information about the responses of the coding neurons. We present a new linear method, the optimal linear estimator (OLE), that on average provides the best possible linear reconstruction. This method is compared with the more familiar vector method and shown to produce more accurate reconstructions using far fewer recorded neurons.

Animals↗
PubMed1994

Modeling state-dependent inactivation of membrane currents.

Inactivation of many ion channels occurs through largely voltage-independent transitions to an inactivated state from the open state or from other states in the pathway leading to opening of the channel. Because this form of inactivation is state-dependent rather than voltage-dependent, it cannot be described by the standard Hodgkin-Huxley formalism used in virtually all modeling studies of neuronal behavior. Using two examples, cumulative inactivation of the Kv3 potassium channel and inactivation of the fast sodium channel, we extend the standard formalism for modeling macroscopic membrane currents to account for state-dependent inactivation. Our results provide an accurate description of cumulative inactivation of the Kv3 channel, new insight into inactivation of the sodium channel, and a general framework for modeling macroscopic currents when state-dependent processes are involved. In a model neuron, the macroscopic Kv3 current produces a novel short-term memory effect and firing delays similar to those seen in hippocampal neurons.

Animals↗
PubMed1993

Activity-dependent regulation of conductances in model neurons.

Neurons maintain their electrical activity patterns despite channel turnover, cell growth, and variable extracellular conditions. A model is presented in which maximal conductances of ionic currents depend on the intracellular concentration of calcium ions and so, indirectly, on activity. Model neurons with activity-dependent maximal conductances modify their conductances to maintain a given behavior when perturbed. Moreover, neurons that are described by identical sets of equations can develop different properties in response to different patterns of presynaptic activity.

Animals↗
PubMed1993

The dynamic clamp: artificial conductances in biological neurons.

The dynamic clamp is a novel method that uses computer simulation to introduce conductances into biological neurons. This method can be used to study the role of various conductances in shaping the activity of single neurons, or neurons within networks. The dynamic clamp can also be used to form circuits from previously unconnected neurons. This approach makes computer simulation an interactive experimental tool, and will be useful in many applications where the role of synaptic strengths and intrinsic properties in neuronal and network dynamics is of interest.

Animals↗
PubMed1993

A new computational method for cable theory problems.

We discuss a new computational procedure for solving the linear cable equation on a tree of arbitrary geometry. The method is based on a simple set of diagrammatic rules implemented using an efficient computer algorithm. Unlike most other methods, this technique is particularly useful for determining the short-time behavior of the membrane potential. Examples are presented and the convergence and accuracy of the method are discussed.

Algorithms↗
PubMed1993

Dynamic clamp: computer-generated conductances in real neurons.

1. We describe a new method, the dynamic clamp, that uses a computer as an interactive tool to introduce simulated voltage and ligand mediated conductances into real neurons. 2. We simulate a gamma-aminobutyric acid (GABA) response of a cultured stomatogastric ganglion neuron to illustrate that the dynamic clamp effectively introduces a conductance into the target neuron. 3. To demonstrate an artificial voltage-dependent conductance, we simulate the action of a voltage-dependent proctolin response on a neuron in the intact stomatogastric ganglion. We show that shifts in the activation curve and the maximal conductance of the response produce different effects on the target neuron. 4. The dynamic clamp is used to construct reciprocal inhibitory synapses between two stomatogastric ganglion neurons that are not coupled naturally, illustrating that this method can be used to form new networks at will.

Animals↗
PubMed1992

Reduction of conductance-based neuron models.

We present a scheme for systematically reducing the number of differential equations required for biophysically realistic neuron models. The techniques are general, are designed to be applicable to a large set of such models and retain in the reduced system as high a degree of fidelity to the original system as possible. As examples, we provide reductions of the Hodgkin-Huxley system and the A-current model of Connor et al. (1977).

Mathematics↗
PubMed1992

Artificial electrical synapses in oscillatory networks.

1. We use an electronic circuit to artificially electrically couple neurons. 2. Strengthening the coupling between an oscillating neuron and a hyperpolarized, passive neuron can either increase or decrease the frequency of the oscillator depending on the properties of the oscillator. 3. The result of electrically coupling two neuronal oscillators depends on the membrane potentials, intrinsic properties of the neurons, and the coupling strength. 4. The interplay between chemical inhibitory synapses and electrical synapses can be studied by creating both chemical and electrical synapses between two cultured neurons and by artificially strengthening the electrical synapse between the ventricular dilator and one pyloric dilator neuron of the stomatogastric ganglion.

Animals↗
PubMed1991

The path integral for dendritic trees.

We construct the path integral for determining the potential on any dendritic tree described by a linear cable equation. This is done by generalizing Brownian motion from a line to a tree. We also construct the path integral for dendritic structures with spatially-varying and/or time-dependent membrane conductivities due, for example, to synaptic inputs. The path integral allows novel computational techniques to be applied to cable problems. Our analysis leads ultimately to an exact expression for the Green's function on a dendritic tree of arbitrary geometry expressed in terms of a set of simple diagrammatic rules. These rules providing a fast and efficient method for solving complex cable problems.

Animals↗
PubMed1990

The effect of electrical coupling on the frequency of model neuronal oscillators.

Neurons with oscillatory properties are a common feature of the nervous system, but little is known about how neural oscillators shape the behavior of neuronal networks or how network interactions influence the properties of neural oscillators. Mathematical models are used to examine the effect of electrically coupling an oscillatory neuron to a second neuron that is either silent or tonically firing. Models of oscillatory neurons with varying degrees of complexity show that this coupling can either increase or decrease the frequency of an oscillator, depending on its membrane potential wave form, the state of the neuron to which it is coupled, and the strength of the coupling. Thus, electrical coupling provides a flexible mechanism for modifying the behavior of an oscillatory neural network.

Action Potentials↗
PubMed1990

Modulation of function and gated learning in a network memory.

Memory and learning are studied in a model neural network made from component cells with a variety of realistic intrinsic dynamic behaviors. Modulation of intrinsic cellular characteristics causes a network to switch between two entirely different modes of operation. In one mode the network acts as a selective, long-term associative memory, whereas in the other it is a nonselective, short-term latching memory. Such functional modulation can be used as a mechanism for initiating and terminating learning in a network associative memory.

Animals↗
PubMed1988

A model of autocatalytic replication.

The catalytic effects that existing polymer chains have on the formation of new chains are modeled using ideas from spin glasses and neural networks. Computer simulation shows that isolated groups of chains in this model are capable of accurately replicating a wide variety of complex structures without templating. Replication in the model arises spontaneously and rapidly, leading to an extremely simple realization of a system exhibiting Darwinian evolution.

Animals↗
PubMedDate not supplied

Functional significance of long-term potentiation for sequence learning and prediction.

Population coding, where neurons with broad and overlapping firing rate tuning curves collectively encode information about a stimulus, is a common feature of sensory systems. We use decoding methods and measured properties of NMDA-mediated LTP induction to study the impact of long-term potentiation of synapses between the neurons of such a coding array. We find that, due to a temporal asymmetry in the induction of NMDA-mediated LTP, firing patterns in a neuronal array that initially represent the current value of a sensory input will, after training, provide an experienced-based prediction of that input instead. We compute how this prediction arises from and depends on the training experience. We also show how the encoded prediction can be used to generate learned motor sequences, such as the movement of a limb. This involves a novel form of memory recall that is driven by the motor response so that it automatically generates new information at a rate approximate for the task being performed.

Excitatory Amino Acid Agonists↗