51 research outputs found
Inhibitory synchrony as a mechanism for attentional gain modulation
Recordings from area V4 of monkeys have revealed that when the focus of
attention is on a visual stimulus within the receptive field of a cortical
neuron, two distinct changes can occur: The firing rate of the neuron can
change and there can be an increase in the coherence between spikes and the
local field potential in the gamma-frequency range (30-50 Hz). The hypothesis
explored here is that these observed effects of attention could be a
consequence of changes in the synchrony of local interneuron networks. We
performed computer simulations of a Hodgkin-Huxley type neuron driven by a
constant depolarizing current, I, representing visual stimulation and a
modulatory inhibitory input representing the effects of attention via local
interneuron networks. We observed that the neuron's firing rate and the
coherence of its output spike train with the synaptic inputs was modulated by
the degree of synchrony of the inhibitory inputs. The model suggest that the
observed changes in firing rate and coherence of neurons in the visual cortex
could be controlled by top-down inputs that regulated the coherence in the
activity of a local inhibitory network discharging at gamma frequencies.Comment: J.Physiology (Paris) in press, 11 figure
Anti-correlations in the degree distribution increase stimulus detection performance in noisy spiking neural networks
Neuronal circuits in the rodent barrel cortex are characterized by stable low firing rates. However, recent experiments show that short spike trains elicited by electrical stimulation in single neurons can induce behavioral responses. Hence, the underlying neural networks provide stability against internal fluctuations in the firing rate, while simultaneously making the circuits sensitive to small external perturbations. Here we studied whether stability and sensitivity are affected by the connectivity structure in recurrently connected spiking networks. We found that anti-correlation between the number of afferent (in-degree) and efferent (out-degree) synaptic connections of neurons increases stability against pathological bursting, relative to networks where the degrees were either positively correlated or uncorrelated. In the stable network state, stimulation of a few cells could lead to a detectable change in the firing rate. To quantify the ability of networks to detect the stimulation, we used a receiver operating characteristic (ROC) analysis. For a given level of background noise, networks with anti-correlated degrees displayed the lowest false positive rates, and consequently had the highest stimulus detection performance. We propose that anti-correlation in the degree distribution may be a computational strategy employed by sensory cortices to increase the detectability of external stimuli. We show that networks with anti-correlated degrees can in principle be formed by applying learning rules comprised of a combination of spike-timing dependent plasticity, homeostatic plasticity and pruning to networks with uncorrelated degrees. To test our prediction we suggest a novel experimental method to estimate correlations in the degree distribution
Creation and manipulation of Feshbach resonances with radio-frequency radiation
We present a simple technique for studying collisions of ultracold atoms in
the presence of a magnetic field and radio-frequency radiation (rf). Resonant
control of scattering properties can be achieved by using rf to couple a
colliding pair of atoms to a bound state. We show, using the example of 6Li,
that in some ranges of rf frequency and magnetic field this can be done without
giving rise to losses. We also show that halo molecules of large spatial extent
require much less rf power than deeply bound states. Another way to exert
resonant control is with a set of rf-coupled bound states, linked to the
colliding pair through the molecular interactions that give rise to
magnetically tunable Feshbach resonances. This was recently demonstrated for
87Rb [Kaufman et al., Phys. Rev. A 80:050701(R), 2009]. We examine the
underlying atomic and molecular physics which made this possible. Lastly, we
consider the control that may be exerted over atomic collisions by placing
atoms in superpositions of Zeeman states, and suggest that it could be useful
where small changes in scattering length are required. We suggest other species
for which rf and magnetic field control could together provide a useful tuning
mechanism.Comment: 21 pages, 8 figures, submitted to New Journal of Physic
Prediction of Feshbach resonances from three input parameters
We have developed a model of Feshbach resonances in gases of ultracold alkali
metal atoms using the ideas of multichannel quantum defect theory. Our model
requires just three parameters describing the interactions - the singlet and
triplet scattering lengths, and the long range van der Waals coefficient - in
addition to known atomic properties. Without using any further details of the
interactions, our approach can accurately predict the locations of resonances.
It can also be used to find the singlet and triplet scattering lengths from
measured resonance data. We apply our technique to Li--K and
K--Rb scattering, obtaining good agreement with experimental
results, and with the more computationally intensive coupled channels
technique.Comment: 5 pages, 2 figures, revised versio
The missing link: Predicting connectomes from noisy and partially observed tract tracing data
Our understanding of the wiring map of the brain, known as the connectome, has increased greatly in the last decade, mostly due to technological advancements in neuroimaging techniques and improvements in computational tools to interpret the vast amount of available data. Despite this, with the exception of the C. elegans roundworm, no definitive connectome has been established for any species. In order to obtain this, tracer studies are particularly appealing, as these have proven highly reliable. The downside of tract tracing is that it is costly to perform, and can only be applied ex vivo. In this paper, we suggest that instead of probing all possible connections, hitherto unknown connections may be predicted from the data that is already available. Our approach uses a 'latent space model' that embeds the connectivity in an abstract physical space. Regions that are close in the latent space have a high chance of being connected, while regions far apart are most likely disconnected in the connectome. After learning the latent embedding from the connections that we did observe, the latent space allows us to predict connections that have not been probed previously. We apply the methodology to two connectivity data sets of the macaque, where we demonstrate that the latent space model is successful in predicting unobserved connectivity, outperforming two baselines and an alternative model in nearly all cases. Furthermore, we show how the latent spatial embedding may be used to integrate multimodal observations (i.e. anterograde and retrograde tracers) for the mouse neocortex. Finally, our probabilistic approach enables us to make explicit which connections are easy to predict and which prove difficult, allowing for informed follow-up studies
26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017
This work was produced as part of the activities of FAPESP Research,\ud
Disseminations and Innovation Center for Neuromathematics (grant\ud
2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud
FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud
supported by a CNPq fellowship (grant 306251/2014-0)
Modelling human choices: MADeM and decision‑making
Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)
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