14,267 research outputs found
Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection
Efforts to automate the reconstruction of neural circuits from 3D electron
microscopic (EM) brain images are critical for the field of connectomics. An
important computation for reconstruction is the detection of neuronal
boundaries. Images acquired by serial section EM, a leading 3D EM technique,
are highly anisotropic, with inferior quality along the third dimension. For
such images, the 2D max-pooling convolutional network has set the standard for
performance at boundary detection. Here we achieve a substantial gain in
accuracy through three innovations. Following the trend towards deeper networks
for object recognition, we use a much deeper network than previously employed
for boundary detection. Second, we incorporate 3D as well as 2D filters, to
enable computations that use 3D context. Finally, we adopt a recursively
trained architecture in which a first network generates a preliminary boundary
map that is provided as input along with the original image to a second network
that generates a final boundary map. Backpropagation training is accelerated by
ZNN, a new implementation of 3D convolutional networks that uses multicore CPU
parallelism for speed. Our hybrid 2D-3D architecture could be more generally
applicable to other types of anisotropic 3D images, including video, and our
recursive framework for any image labeling problem
Evidence accumulation in a Laplace domain decision space
Evidence accumulation models of simple decision-making have long assumed that
the brain estimates a scalar decision variable corresponding to the
log-likelihood ratio of the two alternatives. Typical neural implementations of
this algorithmic cognitive model assume that large numbers of neurons are each
noisy exemplars of the scalar decision variable. Here we propose a neural
implementation of the diffusion model in which many neurons construct and
maintain the Laplace transform of the distance to each of the decision bounds.
As in classic findings from brain regions including LIP, the firing rate of
neurons coding for the Laplace transform of net accumulated evidence grows to a
bound during random dot motion tasks. However, rather than noisy exemplars of a
single mean value, this approach makes the novel prediction that firing rates
grow to the bound exponentially, across neurons there should be a distribution
of different rates. A second set of neurons records an approximate inversion of
the Laplace transform, these neurons directly estimate net accumulated
evidence. In analogy to time cells and place cells observed in the hippocampus
and other brain regions, the neurons in this second set have receptive fields
along a "decision axis." This finding is consistent with recent findings from
rodent recordings. This theoretical approach places simple evidence
accumulation models in the same mathematical language as recent proposals for
representing time and space in cognitive models for memory.Comment: Revised for CB
Gain control in molecular information processing: Lessons from neuroscience
Statistical properties of environments experienced by biological signaling
systems in the real world change, which necessitate adaptive responses to
achieve high fidelity information transmission. One form of such adaptive
response is gain control. Here we argue that a certain simple mechanism of gain
control, understood well in the context of systems neuroscience, also works for
molecular signaling. The mechanism allows to transmit more than one bit (on or
off) of information about the signal independently of the signal variance. It
does not require additional molecular circuitry beyond that already present in
many molecular systems, and, in particular, it does not depend on existence of
feedback loops. The mechanism provides a potential explanation for abundance of
ultrasensitive response curves in biological regulatory networks.Comment: 10 pages, 5 figure
Recommended from our members
Neural substrates of mnemonic discrimination: A whole-brain fMRI investigation.
IntroductionA fundamental component of episodic memory is the ability to differentiate new and highly similar events from previously encountered events. Numerous functional magnetic resonance imaging (fMRI) studies have identified hippocampal involvement in this type of mnemonic discrimination (MD), but few studies have assessed MD-related activity in regions beyond the hippocampus. Therefore, the current fMRI study examined whole-brain activity in healthy young adults during successful discrimination of the test phase of the Mnemonic Similarity Task.MethodIn the study phase, participants made "indoor"/"outdoor" judgments to a series of objects. In the test phase, they made "old"/"new" judgments to a series of probe objects that were either repetitions from the memory set (targets), similar to objects in the memory set (lures), or novel. We assessed hippocampal and whole-brain activity consistent with MD using a step function to identify where activity to targets differed from activity to lures with varying degrees of similarity to targets (high, low), responding to them as if they were novel.ResultsResults revealed that the hippocampus and occipital cortex exhibited differential activity to repeated stimuli relative to even highly similar stimuli, but only hippocampal activity predicted discrimination performance.ConclusionsThese findings are consistent with the notion that successful MD is supported by the hippocampus, with auxiliary processes supported by cortex (e.g., perceptual discrimination)
Data-driven modeling of the olfactory neural codes and their dynamics in the insect antennal lobe
Recordings from neurons in the insects' olfactory primary processing center,
the antennal lobe (AL), reveal that the AL is able to process the input from
chemical receptors into distinct neural activity patterns, called olfactory
neural codes. These exciting results show the importance of neural codes and
their relation to perception. The next challenge is to \emph{model the
dynamics} of neural codes. In our study, we perform multichannel recordings
from the projection neurons in the AL driven by different odorants. We then
derive a neural network from the electrophysiological data. The network
consists of lateral-inhibitory neurons and excitatory neurons, and is capable
of producing unique olfactory neural codes for the tested odorants.
Specifically, we (i) design a projection, an odor space, for the neural
recording from the AL, which discriminates between distinct odorants
trajectories (ii) characterize scent recognition, i.e., decision-making based
on olfactory signals and (iii) infer the wiring of the neural circuit, the
connectome of the AL. We show that the constructed model is consistent with
biological observations, such as contrast enhancement and robustness to noise.
The study answers a key biological question in identifying how lateral
inhibitory neurons can be wired to excitatory neurons to permit robust activity
patterns
The Complexity of Human Walking: A Knee Osteoarthritis Study
This study proposes a framework for deconstructing complex walking patterns to create a simple principal component space before checking whether the projection to this space is suitable for identifying changes from the normality. We focus on knee osteoarthritis, the most common knee joint disease and the second leading cause of disability. Knee osteoarthritis affects over 250 million people worldwide. The motivation for projecting the highly dimensional movements to a lower dimensional and simpler space is our belief that motor behaviour can be understood by identifying a simplicity via projection to a low principal component space, which may reflect upon the underlying mechanism. To study this, we recruited 180 subjects, 47 of which reported that they had knee osteoarthritis. They were asked to walk several times along a walkway equipped with two force plates that capture their ground reaction forces along 3 axes, namely vertical, anterior-posterior, and medio-lateral, at 1000 Hz. Data when the subject does not clearly strike the force plate were excluded, leaving 1β3 gait cycles per subject. To examine the complexity of human walking, we applied dimensionality reduction via Probabilistic Principal Component Analysis. The first principal component explains 34% of the variance in the data, whereas over 80% of the variance is explained by 8 principal components or more. This proves the complexity of the underlying structure of the ground reaction forces. To examine if our musculoskeletal system generates movements that are distinguishable between normal and pathological subjects in a low dimensional principal component space, we applied a Bayes classifier. For the tested cross-validated, subject-independent experimental protocol, the classification accuracy equals 82.62%. Also, a novel complexity measure is proposed, which can be used as an objective index to facilitate clinical decision making. This measure proves that knee osteoarthritis subjects exhibit more variability in the two-dimensional principal component space
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