61 research outputs found
Enhancement of tunneling from a correlated 2D electron system by a many-electron Mossbauer-type recoil in a magnetic field
We consider the effect of electron correlations on tunneling from a 2D
electron layer in a magnetic field parallel to the layer. A tunneling electron
can exchange its momentum with other electrons, which leads to an exponential
increase of the tunneling rate compared to the single-electron approximation.
Explicit results are obtained for a Wigner crystal. They provide a qualitative
and quantitative explanation of the data on electrons on helium. We also
discuss tunneling in semiconductor heterostructures.Comment: published version, 4 pages, 2 figures, RevTeX 3.
Tunneling decay in a magnetic field
We provide a semiclassical theory of tunneling decay in a magnetic field and
a three-dimensional potential of a general form. Because of broken
time-reversal symmetry, the standard WKB technique has to be modified. The
decay rate is found from the analysis of the set of the particle Hamiltonian
trajectories in complex phase space and time. In a magnetic field, the
tunneling particle comes out from the barrier with a finite velocity and behind
the boundary of the classically allowed region. The exit location is obtained
by matching the decaying and outgoing WKB waves at a caustic in complex
configuration space. Different branches of the WKB wave function match on the
switching surface in real space, where the slope of the wave function sharply
changes. The theory is not limited to tunneling from potential wells which are
parabolic near the minimum. For parabolic wells, we provide a bounce-type
formulation in a magnetic field. The theory is applied to specific models which
are relevant to tunneling from correlated two-dimensional electron systems in a
magnetic field parallel to the electron layer.Comment: 16 pages, 11 figure
Adaptive Filtering Enhances Information Transmission in Visual Cortex
Sensory neuroscience seeks to understand how the brain encodes natural
environments. However, neural coding has largely been studied using simplified
stimuli. In order to assess whether the brain's coding strategy depend on the
stimulus ensemble, we apply a new information-theoretic method that allows
unbiased calculation of neural filters (receptive fields) from responses to
natural scenes or other complex signals with strong multipoint correlations. In
the cat primary visual cortex we compare responses to natural inputs with those
to noise inputs matched for luminance and contrast. We find that neural filters
adaptively change with the input ensemble so as to increase the information
carried by the neural response about the filtered stimulus. Adaptation affects
the spatial frequency composition of the filter, enhancing sensitivity to
under-represented frequencies in agreement with optimal encoding arguments.
Adaptation occurs over 40 s to many minutes, longer than most previously
reported forms of adaptation.Comment: 20 pages, 11 figures, includes supplementary informatio
Tunneling transverse to a magnetic field, and how it occurs in correlated 2D electron systems
We investigate tunneling decay in a magnetic field. Because of broken
time-reversal symmetry, the standard WKB technique does not apply. The decay
rate and the outcoming wave packet are found from the analysis of the set of
the particle Hamiltonian trajectories and its singularities in complex space.
The results are applied to tunneling from a strongly correlated 2D electron
system in a magnetic field parallel to the layer. We show in a simple model
that electron correlations exponentially strongly affect the tunneling rate.Comment: 4 pages, 3 figure
Second Order Dimensionality Reduction Using Minimum and Maximum Mutual Information Models
Conventional methods used to characterize multidimensional neural feature selectivity, such as spike-triggered covariance (STC) or maximally informative dimensions (MID), are limited to Gaussian stimuli or are only able to identify a small number of features due to the curse of dimensionality. To overcome these issues, we propose two new dimensionality reduction methods that use minimum and maximum information models. These methods are information theoretic extensions of STC that can be used with non-Gaussian stimulus distributions to find relevant linear subspaces of arbitrary dimensionality. We compare these new methods to the conventional methods in two ways: with biologically-inspired simulated neurons responding to natural images and with recordings from macaque retinal and thalamic cells responding to naturalistic time-varying stimuli. With non-Gaussian stimuli, the minimum and maximum information methods significantly outperform STC in all cases, whereas MID performs best in the regime of low dimensional feature spaces
Estimating Receptive Fields from Responses to Natural Stimuli with Asymmetric Intensity Distributions
The reasons for using natural stimuli to study sensory function are quickly mounting, as recent studies have revealed important differences in neural responses to natural and artificial stimuli. However, natural stimuli typically contain strong correlations and are spherically asymmetric (i.e. stimulus intensities are not symmetrically distributed around the mean), and these statistical complexities can bias receptive field (RF) estimates when standard techniques such as spike-triggered averaging or reverse correlation are used. While a number of approaches have been developed to explicitly correct the bias due to stimulus correlations, there is no complementary technique to correct the bias due to stimulus asymmetries. Here, we develop a method for RF estimation that corrects reverse correlation RF estimates for the spherical asymmetries present in natural stimuli. Using simulated neural responses, we demonstrate how stimulus asymmetries can bias reverse-correlation RF estimates (even for uncorrelated stimuli) and illustrate how this bias can be removed by explicit correction. We demonstrate the utility of the asymmetry correction method under experimental conditions by estimating RFs from the responses of retinal ganglion cells to natural stimuli and using these RFs to predict responses to novel stimuli
Network adaptation improves temporal representation of naturalistic stimuli in drosophila eye: II Mechanisms
Retinal networks must adapt constantly to best present the ever changing visual world to the brain. Here we test the hypothesis that adaptation is a result of different mechanisms at several synaptic connections within the network. In a companion paper (Part I), we showed that adaptation in the photoreceptors (R1-R6) and large monopolar cells (LMC) of the Drosophila eye improves sensitivity to under-represented signals in seconds by enhancing both the amplitude and frequency distribution of LMCs' voltage responses to repeated naturalistic contrast series. In this paper, we show that such adaptation needs both the light-mediated conductance and feedback-mediated synaptic conductance. A faulty feedforward pathway in histamine receptor mutant flies speeds up the LMC output, mimicking extreme light adaptation. A faulty feedback pathway from L2 LMCs to photoreceptors slows down the LMC output, mimicking dark adaptation. These results underline the importance of network adaptation for efficient coding, and as a mechanism for selectively regulating the size and speed of signals in neurons. We suggest that concert action of many different mechanisms and neural connections are responsible for adaptation to visual stimuli. Further, our results demonstrate the need for detailed circuit reconstructions like that of the Drosophila lamina, to understand how networks process information
Genotype to phenotype mapping and the fitness landscape of the E. coli lac promoter
Genotype-to-phenotype maps and the related fitness landscapes that include
epistatic interactions are difficult to measure because of their high
dimensional structure. Here we construct such a map using the recently
collected corpora of high-throughput sequence data from the 75 base pairs long
mutagenized E. coli lac promoter region, where each sequence is associated with
its phenotype, the induced transcriptional activity measured by a fluorescent
reporter. We find that the additive (non-epistatic) contributions of individual
mutations account for about two-thirds of the explainable phenotype variance,
while pairwise epistasis explains about 7% of the variance for the full
mutagenized sequence and about 15% for the subsequence associated with protein
binding sites. Surprisingly, there is no evidence for third order epistatic
contributions, and our inferred fitness landscape is essentially single peaked,
with a small amount of antagonistic epistasis. There is a significant selective
pressure on the wild type, which we deduce to be multi-objective optimal for
gene expression in environments with different nutrient sources. We identify
transcription factor (CRP) and RNA polymerase binding sites in the promotor
region and their interactions without difficult optimization steps. In
particular, we observe evidence for previously unexplored genetic regulatory
mechanisms, possibly kinetic in nature. We conclude with a cautionary note that
inferred properties of fitness landscapes may be severely influenced by biases
in the sequence data
Modeling convergent ON and OFF pathways in the early visual system
For understanding the computation and function of single neurons in sensory systems, one needs to investigate how sensory stimuli are related to a neuron’s response and which biological mechanisms underlie this relationship. Mathematical models of the stimulus–response relationship have proved very useful in approaching these issues in a systematic, quantitative way. A starting point for many such analyses has been provided by phenomenological “linear–nonlinear” (LN) models, which comprise a linear filter followed by a static nonlinear transformation. The linear filter is often associated with the neuron’s receptive field. However, the structure of the receptive field is generally a result of inputs from many presynaptic neurons, which may form parallel signal processing pathways. In the retina, for example, certain ganglion cells receive excitatory inputs from ON-type as well as OFF-type bipolar cells. Recent experiments have shown that the convergence of these pathways leads to intriguing response characteristics that cannot be captured by a single linear filter. One approach to adjust the LN model to the biological circuit structure is to use multiple parallel filters that capture ON and OFF bipolar inputs. Here, we review these new developments in modeling neuronal responses in the early visual system and provide details about one particular technique for obtaining the required sets of parallel filters from experimental data
Functional Clustering Drives Encoding Improvement in a Developing Brain Network during Awake Visual Learning
Sensory experience drives dramatic structural and functional plasticity in developing neurons. However, for single-neuron plasticity to optimally improve whole-network encoding of sensory information, changes must be coordinated between neurons to ensure a full range of stimuli is efficiently represented. Using two-photon calcium imaging to monitor evoked activity in over 100 neurons simultaneously, we investigate network-level changes in the developing Xenopus laevis tectum during visual training with motion stimuli. Training causes stimulus-specific changes in neuronal responses and interactions, resulting in improved population encoding. This plasticity is spatially structured, increasing tuning curve similarity and interactions among nearby neurons, and decreasing interactions among distant neurons. Training does not improve encoding by single clusters of similarly responding neurons, but improves encoding across clusters, indicating coordinated plasticity across the network. NMDA receptor blockade prevents coordinated plasticity, reduces clustering, and abolishes whole-network encoding improvement. We conclude that NMDA receptors support experience-dependent network self-organization, allowing efficient population coding of a diverse range of stimuli.Canadian Institutes of Health Researc
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