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Bartlett W Mel

Publications and source records attributed to Bartlett W Mel.

6 recordsLinked to original sources
PubMed2004

On the fight between excitation and inhibition: location is everything.

In the intact brain, neurons are constantly subjected to both excitatory and inhibitory inputs to their dendritic trees. Although it is accepted that the overall response of a neuron--its train of output spikes--depends on the balance of excitation and inhibition, we continue to lack specific knowledge of the rules that govern how excitatory and inhibitory inputs interact in space and time within the confines of individual neurons. In a recent paper, Liu starts by providing evidence that the relative locations and numbers of excitatory and inhibitory synapses are tightly regulated in cultured neurons from the hippocampus. This is consistent with findings in other labs that suggest neurons work hard, and in a variety of different ways, to maintain their inputs in proper balance and their outputs within appropriate ranges. On this backdrop, Liu's most important finding of a functional nature is that inhibition appears to act quite locally; that is, an inhibitory synapse effectively opposes an excitatory synapse only when it is very close by within the same dendritic branch (Fig. 1). This finding provides further support for the view--anticipated by neural theorists more than 20 years ago--that the brain's principal neurons contain a potentially large number of separate computational subunits.

Animals↗
PubMed2004

Computational subunits in thin dendrites of pyramidal cells.

The thin basal and oblique dendrites of cortical pyramidal neurons receive most of the synaptic inputs from other cells, but their integrative properties remain uncertain. Previous studies have most often reported global linear or sublinear summation. An alternative view, supported by biophysical modeling studies, holds that thin dendrites provide a layer of independent computational 'subunits' that sigmoidally modulate their inputs prior to global summation. To distinguish these possibilities, we combined confocal imaging and dual-site focal synaptic stimulation of identified thin dendrites in rat neocortical pyramidal neurons. We found that nearby inputs on the same branch summed sigmoidally, whereas widely separated inputs or inputs to different branches summed linearly. This strong spatial compartmentalization effect is incompatible with a global summation rule and provides the first experimental support for a two-layer 'neural network' model of pyramidal neuron thin-branch integration. Our findings could have important implications for the computing and memory-related functions of cortical tissue.

Animals↗
PubMed2003

Arithmetic of subthreshold synaptic summation in a model CA1 pyramidal cell.

The rules of synaptic integration in pyramidal cells remain obscure, in part due to conflicting interpretations of existing experimental data. To clarify issues, we developed a CA1 pyramidal cell model calibrated with a broad spectrum of in vitro data. Using simultaneous dendritic and somatic recordings and combining results for two different response measures (peak versus mean EPSP), two different stimulus formats (single shock versus 50 Hz trains), and two different spatial integration conditions (within versus between-branch summation), we found that the cell's subthreshold responses to paired inputs are best described as a sum of nonlinear subunit responses, where the subunits correspond to different dendritic branches. In addition to suggesting a new type of experiment and providing testable predictions, our model shows how conclusions regarding synaptic arithmetic can be influenced by an array of seemingly innocuous experimental design choices.

Dendrites↗
PubMed2003

Pyramidal neuron as two-layer neural network.

The pyramidal neuron is the principal cell type in the mammalian forebrain, but its function remains poorly understood. Using a detailed compartmental model of a hippocampal CA1 pyramidal cell, we recorded responses to complex stimuli consisting of dozens of high-frequency activated synapses distributed throughout the apical dendrites. We found the cell's firing rate could be predicted by a simple formula that maps the physical components of the cell onto those of an abstract two-layer "neural network." In the first layer, synaptic inputs drive independent sigmoidal subunits corresponding to the cell's several dozen long, thin terminal dendrites. The subunit outputs are then summed within the main trunk and cell body prior to final thresholding. We conclude that insofar as the neural code is mediated by average firing rate, a two-layer neural network may provide a useful abstraction for the computing function of the individual pyramidal neuron.

Biophysical Phenomena↗
PubMed2002

Have we been hebbing down the wrong path?

In this issue of Neuron, Stepanyants, Hof, and Chklovskii derive a simple mathematical formula to calculate, from measurable anatomical parameters, the capacity for neuronal wiring plasticity involving local synaptic rearrangements. Their work provides a deeper understanding of the potential contribution of structural plasticity to learning and memory.

Animals↗