How nitric oxide helps update memories

Some dopaminergic neurons release both dopamine and nitric oxide to increase the flexibility of olfactory memories

SpaRCe : sparse reservoir computing

"Sparse" neural networks, in which relatively few neurons or connections are active, are common in both machine learning and neuroscience. Whereas in machine learning, "sparseness" is related to a penalty term which effectively leads to some connecting weights becoming small or zero, in biological brains, sparseness is often created when high spiking thresholds prevent neuronal activity. Inspired by neuroscience, here we introduce sparseness into a reservoir computing network via neuron-specific learnable thresholds of activity, allowing neurons with low thresholds to give output but silencing outputs from neurons with high thresholds. This approach, which we term "SpaRCe", optimises the sparseness level of the reservoir and applies the threshold mechanism to the information received by the read-out weights. Both the read-out weights and the thresholds are learned by a standard on-line gradient rule that minimises an error function on the outputs of the network. Threshold learning occurs by the balance of two opposing forces: reducing inter-neuronal correlations in the reservoir by deactivating redundant neurons, while increasing the activity of neurons participating in correct decisions. We test SpaRCe in a set of classification problems and find that introducing threshold learning improves performance compared to standard reservoir computing networks

The Hubbard model on a complete graph: Exact Analytical results

We derive the analytical expression of the ground state of the Hubbard model with unconstrained hopping at half filling and for arbitrary lattice sites.Comment: Email:[email protected]

Localized inhibition in the Drosophila mushroom body

Many neurons show compartmentalized activity, in which activity does not spread readily across the cell, allowing input and output to occur locally. However, the functional implications of compartmentalized activity for the wider neural circuit are often unclear. We addressed this problem in the Drosophila mushroom body, whose principal neurons, Kenyon cells, receive feedback inhibition from a non-spiking interneuron called the anterior paired lateral (APL) neuron. We used local stimulation and volumetric calcium imaging to show that APL inhibits Kenyon cells’ dendrites and axons, and that both activity in APL and APL’s inhibitory effect on Kenyon cells are spatially localized (the latter somewhat less so), allowing APL to differentially inhibit different mushroom body compartments. Applying these results to the Drosophila hemibrain connectome predicts that individual Kenyon cells inhibit themselves via APL more strongly than they inhibit other individual Kenyon cells. These findings reveal how cellular physiology and detailed network anatomy can combine to influence circuit function

Entropy bounds, monotonicity properties and scaling in CFTs

We study the ratio of the entropy to the total energy in conformal field theories at finite temperature. For the free field realizations of {\cal N}=4 super Yang-Mills theory in D=4 and the (2,0) tensor multiplet in D=6, the ratio is bounded from above. The corresponding bounds are less stringent than the recently proposed Verlinde bound. We show that entropy bounds arise generically in CFTs in connection to monotonicity properties with respect to temperature changes of a generalized C-function. For strongly coupled CFTs with AdS duals, we show that the ratio obeys the Verlinde bound even in the presence of rotation. For such CFTs, we point out an intriguing resemblance in their thermodynamic formulas with the corresponding ones of two-dimensional CFTs. We show that simple scaling forms for the free energy and entropy of CFTs with AdS duals reproduce the thermodynamical properties of (D+1)-dimensional AdS black holes.Comment: 19p, LaTeX, v2 minor clarifications and added references, v3 version to appear in NP

Fine Structure Discussion of Parity-Nonconserving Neutron Scattering at Epithermal Energies

The large magnitude and the sign correlation effect in the parity non-conserving resonant scattering of epithermal neutrons from $^{232}$Th is discussed in terms of a non-collective $2p-1h$ local doorway model. General conclusions are drawn as to the probability of finding large parity violation effects in other regions of the periodic table.Comment: 6 pages, Tex. CTP# 2296, to appear in Z. Phys.

An Equivalent Medium Method for the Vacuum Assisted Resin Transfer Molding Process Simulation

Computer simulation has been an efficient and cost-effective tool for liquid composite molding, including resin transfer molding (RTM), vacuum assisted resin transfer molding (VARTM), and resin infusion, compared to trial and error. The control volume finite element method (CVFEM) has been the predominant method for simulation. When the CVFEM simulation is used for the VARTM process, because of the existence of two distinct flow media: fiber preform and high permeable media (HPM), 3-D models are required. Since the HPM is usually much thinner than the fiber preform, a large number of nodes and elements need to be used in simulation, which significantly increases the computation load and time. In addition, the time-consuming preprocessing process makes simulation not feasible for industry applications. This article presents an equivalent medium method (EMM) for fast and accurate VARTM process simulation. This method increases the thickness of the HPM or both the HPM and the fiber preform and applies the equivalent material properties. This is an improved method over previously presented equivalent permeability method (EPM) by correcting its two shortcomings: (1) The EPM does not account for the influence of the porosity of HPM, thus the resin flow through HPM is changed and (2) The EPM does not consider the change of through-thickness permeability after the equivalence. A new mesh generation algorithm is also discussed, which provides a faster and more convenient way for preprocessing. The approach presented in this article provides the fundamental for developing a universal computer simulation tool for both the RTM and VARTM processes. The effectiveness of this approach has been validated by comparing to the conventional CVFEM simulation and experiments

Compensatory variability in network parameters enhances memory performance in the Drosophila mushroom body

Neural circuits use homeostatic compensation to achieve consistent behavior despite variability in underlying intrinsic and network parameters. However, it remains unclear how compensation regulates variability across a population of the same type of neurons within an individual and what computational benefits might result from such compensation. We address these questions in the Drosophila mushroom body, the fly’s olfactory memory center. In a computational model, we show that under sparse coding conditions, memory performance is degraded when the mushroom body’s principal neurons, Kenyon cells (KCs), vary realistically in key parameters governing their excitability. However, memory performance is rescued while maintaining realistic variability if parameters compensate for each other to equalize KC average activity. Such compensation can be achieved through both activity-dependent and activity-independent mechanisms. Finally, we show that correlations predicted by our model’s compensatory mechanisms appear in the Drosophila hemibrain connectome. These findings reveal compensatory variability in the mushroom body and describe its computational benefits for associative memory