245,432 research outputs found
Neural correlates of intentional and stimulus-driven inhibition: a comparison
People can inhibit an action because of an instruction by an external stimulus, or because of their own internal decision. The similarities and differences between these two forms of inhibition are not well understood. Therefore, in the present study the neural correlates of intentional and stimulus-driven inhibition were tested in the same subjects. Participants performed two inhibition tasks while lying in the scanner: the marble task in which they had to choose for themselves between intentionally acting on, or inhibiting a prepotent response to measure intentional inhibition, and the classical stop signal task in which an external signal triggered the inhibition process. Results showed that intentional inhibition decision processes rely on a neural network that has been documented extensively for stimulus-driven inhibition, including bilateral parietal and lateral prefrontal cortex and pre-supplementary motor area. We also found activation in dorsal frontomedian cortex and left inferior frontal gyrus during intentional inhibition that depended on the history of previous choices. Together, these results indicate that intentional inhibition and stimulus-driven inhibition engage a common inhibition network, but intentional inhibition is also characterized by additional context-dependent neural activation in medial prefrontal cortex
Neural crest induction in Xenopus: evidence for a two-signal model
We have investigated the molecular interactions underlying neural crest formation in Xenopus. Using chordin overexpression to antagonize endogenous BMP signaling in whole embryos and explants, we demonstrate that such inhibition alone is insufficient to account for neural crest induction in vivo. We find, however, that chordin-induced neural plate tissue can be induced to adopt neural crest fates by members of the FGF and Wnt families, growth factors that have previously been shown to posteriorize induced neural tissue. Overexpression of a dominant negative XWnt-8 inhibits the expression of neural crest markers, demonstrating the necessity for a Wnt signal during neural crest induction in vivo. The requirement for Wnt signaling during neural crest induction is shown to be direct, whereas FGF-mediated neural crest induction may be mediated by Wnt signals. Overexpression of the zinc finger transcription factor Slug, one of the earliest markers of neural crest formation, is insufficient for neural crest induction. Slug-expressing ectoderm will generate neural crest in the presence of Wnt or FGF-like signals, however, bypassing the need for BMP inhibition in this process. A two-step model for neural crest induction is proposed
Pre-integration lateral inhibition enhances unsupervised learning
A large and influential class of neural network architectures use
post-integration lateral inhibition as a mechanism for competition. We argue
that these algorithms are computationally deficient in that they fail to
generate, or learn, appropriate perceptual representations under certain
circumstances. An alternative neural network architecture is presented in which
nodes compete for the right to receive inputs rather than for the right to
generate outputs. This form of competition, implemented through pre-integration
lateral inhibition, does provide appropriate coding properties and can be used
to efficiently learn such representations. Furthermore, this architecture is
consistent with both neuro-anatomical and neuro-physiological data. We thus
argue that pre-integration lateral inhibition has computational advantages over
conventional neural network architectures while remaining equally biologically
plausible
Neural Sampling by Irregular Gating Inhibition of Spiking Neurons and Attractor Networks
A long tradition in theoretical neuroscience casts sensory processing in the
brain as the process of inferring the maximally consistent interpretations of
imperfect sensory input. Recently it has been shown that Gamma-band inhibition
can enable neural attractor networks to approximately carry out such a sampling
mechanism. In this paper we propose a novel neural network model based on
irregular gating inhibition, show analytically how it implements a Monte-Carlo
Markov Chain (MCMC) sampler, and describe how it can be used to model networks
of both neural attractors as well as of single spiking neurons. Finally we show
how this model applied to spiking neurons gives rise to a new putative
mechanism that could be used to implement stochastic synaptic weights in
biological neural networks and in neuromorphic hardware
Gain control with A-type potassium current: IA as a switch between divisive and subtractive inhibition
Neurons process information by transforming barrages of synaptic inputs into
spiking activity. Synaptic inhibition suppresses the output firing activity of
a neuron, and is commonly classified as having a subtractive or divisive effect
on a neuron's output firing activity. Subtractive inhibition can narrow the
range of inputs that evoke spiking activity by eliminating responses to
non-preferred inputs. Divisive inhibition is a form of gain control: it
modifies firing rates while preserving the range of inputs that evoke firing
activity. Since these two "modes" of inhibition have distinct impacts on neural
coding, it is important to understand the biophysical mechanisms that
distinguish these response profiles.
We use simulations and mathematical analysis of a neuron model to find the
specific conditions for which inhibitory inputs have subtractive or divisive
effects. We identify a novel role for the A-type Potassium current (IA). In our
model, this fast-activating, slowly- inactivating outward current acts as a
switch between subtractive and divisive inhibition. If IA is strong (large
maximal conductance) and fast (activates on a time-scale similar to spike
initiation), then inhibition has a subtractive effect on neural firing. In
contrast, if IA is weak or insufficiently fast-activating, then inhibition has
a divisive effect on neural firing. We explain these findings using dynamical
systems methods to define how a spike threshold condition depends on synaptic
inputs and IA.
Our findings suggest that neurons can "self-regulate" the gain control
effects of inhibition via combinations of synaptic plasticity and/or modulation
of the conductance and kinetics of A-type Potassium channels. This novel role
for IA would add flexibility to neurons and networks, and may relate to recent
observations of divisive inhibitory effects on neurons in the nucleus of the
solitary tract.Comment: 20 pages, 11 figure
Gain Control With A-Type Potassium Current: IA As A Switch Between Divisive And Subtractive Inhibition
Neurons process and convey information by transforming barrages of synaptic inputs into spiking activity. Synaptic inhibition typically suppresses the output firing activity of a neuron, and is commonly classified as having a subtractive or divisive effect on a neuron’s output firing activity. Subtractive inhibition can narrow the range of inputs that evoke spiking activity by eliminating responses to non-preferred inputs. Divisive inhibition is a form of gain control: it modifies firing rates while preserving the range of inputs that evoke firing activity. Since these two “modes” of inhibition have distinct impacts on neural coding, it is important to understand the biophysical mechanisms that distinguish these response profiles. In this study, we use simulations and mathematical analysis of a neuron model to find the specific conditions (parameter sets) for which inhibitory inputs have subtractive or divisive effects. Significantly, we identify a novel role for the A-type Potassium current (IA). In our model, this fast-activating, slowly-inactivating outward current acts as a switch between subtractive and divisive inhibition. In particular, if IA is strong (large maximal conductance) and fast (activates on a time-scale similar to spike initiation), then inhibition has a subtractive effect on neural firing. In contrast, if IA is weak or insufficiently fast-activating, then inhibition has a divisive effect on neural firing. We explain these findings using dynamical systems methods (plane analysis and fast-slow dissection) to define how a spike threshold condition depends on synaptic inputs and IA. Our findings suggest that neurons can “self-regulate” the gain control effects of inhibition via combinations of synaptic plasticity and/or modulation of the conductance and kinetics of A-type Potassium channels. This novel role for IA would add flexibility to neurons and networks, and may relate to recent observations of divisive inhibitory effects on neurons in the nucleus of the solitary tract
Dendritic inhibition enhances neural coding properties.
The presence of a large number of inhibitory contacts at the soma and axon
initial segment of cortical pyramidal cells has inspired a large and influential
class of neural network model which use post-integration lateral inhibition as a
mechanism for competition between nodes. However, inhibitory synapses also
target the dendrites of pyramidal cells. The role of this dendritic inhibition
in competition between neurons has not previously been addressed. We
demonstrate, using a simple computational model, that such pre-integration
lateral inhibition provides networks of neurons with useful representational and
computational properties which are not provided by post-integration
inhibition
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