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Bardia F. Behabadi

dblp:85/3480 · DBLP profile ↗
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3ranked-venue papers
2as first author
0since 2021 · last 2014
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2 · 1 first-authorArtificial intelligence and machine learning · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Representation and self-supervised learning · 50% Deep learning architectures and training · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Representation and self-supervised learning
computational neuroscience
0.212014
An Augmented Two-Layer Model Captures Nonlinear Analog Spatial Integration Effects in Pyramidal Neuron Dendrites · Proc. IEEE 2014
Machine learning › Deep learning architectures and training › biologically inspired neural network
dendritic integration
0.212014
An Augmented Two-Layer Model Captures Nonlinear Analog Spatial Integration Effects in Pyramidal Neuron Dendrites · Proc. IEEE 2014

Methods — techniques the papers use, named apart from their topics

sigmoidal function approximation · 0.2neurophysiological modeling · 0.2
YearPublicationVenuePosition
2014 An Augmented Two-Layer Model Captures Nonlinear Analog Spatial Integration Effects in Pyramidal Neuron Dendrites
abstract
In pursuit of the goal to understand and eventually reproduce the diverse functions of the brain, a key challenge lies in reverse engineering the peculiar biology-based "technology" that underlies the brain's remarkable ability to process and store information. The basic building block of the nervous system is the nerve cell, or "neuron," yet after more than 100 years of neurophysiological study and 60 years of modeling, the information processing functions of individual neurons, and the parameters that allow them to engage in so many different types of computation (sensory, motor, mnemonic, executive, etc.) remain poorly understood. In this paper, we review both historical and recent findings that have led to our current understanding of the analog spatial processing capabilities of dendrites, the major input structures of neurons, with a focus on the principal cell type of the neocortex and hippocampus, the pyramidal neuron (PN). We encapsulate our current understanding of PN dendritic integration in an abstract layered model whose spatially sensitive branch-subunits compute multidimensional sigmoidal functions. Unlike the 1-D sigmoids found in conventional neural network models, multidimensional sigmoids allow the cell to implement a rich spectrum of nonlinear modulation effects directly within their dendritic trees.
Monika Jadi, Bardia F. Behabadi, Alon Poleg-Polsky, Jackie Schiller, Bartlett W. Mel
Proc. IEEE2
2012 Location-Dependent Excitatory Synaptic Interactions in Pyramidal Neuron Dendrites
abstract
Neocortical pyramidal neurons (PNs) receive thousands of excitatory synaptic contacts on their basal dendrites. Some act as classical driver inputs while others are thought to modulate PN responses based on sensory or behavioral context, but the biophysical mechanisms that mediate classical-contextual interactions in these dendrites remain poorly understood. We hypothesized that if two excitatory pathways bias their synaptic projections towards proximal vs. distal ends of the basal branches, the very different local spike thresholds and attenuation factors for inputs near and far from the soma might provide the basis for a classical-contextual functional asymmetry. Supporting this possibility, we found both in compartmental models and electrophysiological recordings in brain slices that the responses of basal dendrites to spatially separated inputs are indeed strongly asymmetric. Distal excitation lowers the local spike threshold for more proximal inputs, while having little effect on peak responses at the soma. In contrast, proximal excitation lowers the threshold, but also substantially increases the gain of distally-driven responses. Our findings support the view that PN basal dendrites possess significant analog computing capabilities, and suggest that the diverse forms of nonlinear response modulation seen in the neocortex, including uni-modal, cross-modal, and attentional effects, could depend in part on pathway-specific biases in the spatial distribution of excitatory synaptic contacts onto PN basal dendritic arbors.
Bardia F. Behabadi, Alon Poleg-Polsky, Monika Jadi, Jackie Schiller, Bartlett W. Mel
PLoS Comput. Biol.1
2007 J4 at Sweet 16: A New Wrinkle?
abstract
Compartmental models provide a major source of insight into the information processing functions of single neurons. Over the past 15 years, one of the most widely used neuronal morphologies has been the cell called "j4," a layer 5 pyramidal cell from cat visual cortex originally described in Douglas, Martin, and Whitteridge (1991). The cell has since appeared in at least 28 published compartmental modeling studies, including several in this journal. In recently examining why we could not reproduce certain in vitro data involving the attenuation of signals originating in distal basal dendrites, we discovered that pronounced fluctuations in the diameter measurements of j4 lead to a bottlenecking effect that increases distal input resistances and significantly reduces voltage transfer between distal sites and the cell body. Upon smoothing these diameter fluctuations, bringing j4 more in line with other reconstructions of layer 5 pyramidal neurons, we found that the attenuation of steady-state voltage signals traveling to the cell body V(distal)/V(soma) was reduced by 60% at some locations in some branches (corresponding to a 2.5-fold increase in the voltage response at the soma for the same distal depolarization) and by 30% on average (corresponding to a 45% increase in somatic response). Changes of this magnitude could lead to different outcomes in some types of compartmental modeling studies. A smoothed version of the j4 morphology is available online at http://lnc.usc.edu/j4-smooth/ .
Bardia F. Behabadi, Bartlett W. Mel
Neural Comput.1