Thouless-Anderson-Palmer equation for analog neural network with temporally fluctuating white synaptic noise

Effects of synaptic noise on the retrieval process of associative memory neural networks are studied from the viewpoint of neurobiological and biophysical understanding of information processing in the brain. We investigate the statistical mechanical properties of stochastic analog neural networks with temporally fluctuating synaptic noise, which is assumed to be white noise. Such networks, in general, defy the use of the replica method, since they have no energy concept. The self-consistent signal-to-noise analysis (SCSNA), which is an alternative to the replica method for deriving a set of order parameter equations, requires no energy concept and thus becomes available in studying networks without energy functions. Applying the SCSNA to stochastic network requires the knowledge of the Thouless-Anderson-Palmer (TAP) equation which defines the deterministic networks equivalent to the original stochastic ones. The study of the TAP equation which is of particular interest for the case without energy concept is very few, while it is closely related to the SCSNA in the case with energy concept. This paper aims to derive the TAP equation for networks with synaptic noise together with a set of order parameter equations by a hybrid use of the cavity method and the SCSNA.Comment: 13 pages, 3 figure

Private costs on water conservation: study case at Cantareira Mantiqueira Corridor Region.

This study aims to evaluate the private opportunity cost for an extensive forest recover program in the Cantareira-Mantiqueira Corridor Region and discuss its results focusing on three central questions: i. what is the private opportunity cost of forest restoration for the main land use activities in the Cantareira-Mantiqueira Corridor Region? ii. how the private opportunity costs varies throughout the region? iii. What are the most cost-effectiveness PES strategies available for the Cantareira- Mantiqueira Corridor Region

Statistical Mechanics of Soft Margin Classifiers

We study the typical learning properties of the recently introduced Soft Margin Classifiers (SMCs), learning realizable and unrealizable tasks, with the tools of Statistical Mechanics. We derive analytically the behaviour of the learning curves in the regime of very large training sets. We obtain exponential and power laws for the decay of the generalization error towards the asymptotic value, depending on the task and on general characteristics of the distribution of stabilities of the patterns to be learned. The optimal learning curves of the SMCs, which give the minimal generalization error, are obtained by tuning the coefficient controlling the trade-off between the error and the regularization terms in the cost function. If the task is realizable by the SMC, the optimal performance is better than that of a hard margin Support Vector Machine and is very close to that of a Bayesian classifier.Comment: 26 pages, 12 figures, submitted to Physical Review

Slowly evolving random graphs II: Adaptive geometry in finite-connectivity Hopfield models

We present an analytically solvable random graph model in which the connections between the nodes can evolve in time, adiabatically slowly compared to the dynamics of the nodes. We apply the formalism to finite connectivity attractor neural network (Hopfield) models and we show that due to the minimisation of the frustration effects the retrieval region of the phase diagram can be significantly enlarged. Moreover, the fraction of misaligned spins is reduced by this effect, and is smaller than in the infinite connectivity regime. The main cause of this difference is found to be the non-zero fraction of sites with vanishing local field when the connectivity is finite.Comment: 17 pages, 8 figure

A bird’s eye view: using circuit theory to study urban landscape connectivity for birds

Context Connectivity is fundamental to understanding how landscape form influences ecological function. However, uncertainties persist due to the difficulty and expense of gathering empirical data to drive or to validate connectivity models, especially in urban areas, where relationships are multifaceted and the habitat matrix cannot be considered to be binary. Objectives This research used circuit theory to model urban bird flows (i.e. ‘current’), and compared results to observed abundance. The aims were to explore the ability of this approach to predict wildlife flows and to test relationships between modelled connectivity and variation in abundance. Methods Circuitscape was used to model functional connectivity in Bedford, Luton/Dunstable, and Milton Keynes, UK, for great tits (Parus major) and blue tits (Cyanistes caeruleus), drawing parameters from published studies of woodland bird flows in urban environments. Model performance was then tested against observed abundance data. Results Modelled current showed a weak yet positive agreement with combined abundance for P. major and C. caeruleus. Weaker correlations were found for other woodland species, suggesting the approach may be expandable if re-parameterised. Conclusions Trees provide suitable habitat for urban woodland bird species, but their location in large, contiguous patches and corridors along barriers also facilitates connectivity networks throughout the urban matrix. Urban connectivity studies are well-served by the advantages of circuit theory approaches, and benefit from the empirical study of wildlife flows in these landscapes to parameterise this type of modelling more explicitly. Such results can prove informative and beneficial in designing urban green space and new developments

Thresholds of riparian forest use by terrestrial mammals in a fragmented Amazonian deforestation frontier

Species persistence in fragmented landscapes is intimately related to the quality, structure, and context of remaining habitat remnants. Riparian vegetation is legally protected within private landholdings in Brazil, so we quantitatively assessed occupancy patterns of terrestrial mammals in these remnants, examining under which circumstances different species effectively use them. We selected 38 riparian forest patches and five comparable riparian sites within continuous forest, at which we installed four to five camera-traps per site (199 camera-trap stations). Terrestrial mammal assemblages were sampled for 60 days per station during the dry seasons of 2013 and 2014. We modelled species occupancy and detection probabilities within riparian forest remnants, and examined the effects of patch size, habitat quality, and landscape structure on occupancy probabilities. We then scaled-up modelled occupancies to all 1915 riparian patches throughout the study region to identify which remnants retain the greatest potential to work as habitat for terrestrial vertebrates. Of the ten species for which occupancy was modelled, six responded to forest quality (remnant degradation, cattle intrusion, palm aggregations, and understorey density) or structure (remnant width, isolation, length, and area of the patch from which it originates). Patch suitability was lower considering habitat quality than landscape structure, and virtually all riparian remnants were unsuitable to maintain a high occupancy probability for all species that responded to forest patch quality or structure. Beyond safeguarding legal compliance concerning riparian remnant amount, ensuring terrestrial vertebrate persistence in fragmented landscapes will require curbing the drivers of forest degradation within private landholdings

Peptide Array X-Linking (PAX): A New Peptide-Protein Identification Approach

Many protein interaction domains bind short peptides based on canonical sequence consensus motifs. Here we report the development of a peptide array-based proteomics tool to identify proteins directly interacting with ligand peptides from cell lysates. Array-formatted bait peptides containing an amino acid-derived cross-linker are photo-induced to crosslink with interacting proteins from lysates of interest. Indirect associations are removed by high stringency washes under denaturing conditions. Covalently trapped proteins are subsequently identified by LC-MS/MS and screened by cluster analysis and domain scanning. We apply this methodology to peptides with different proline-containing consensus sequences and show successful identifications from brain lysates of known and novel proteins containing polyproline motif-binding domains such as EH, EVH1, SH3, WW domains. These results suggest the capacity of arrayed peptide ligands to capture and subsequently identify proteins by mass spectrometry is relatively broad and robust. Additionally, the approach is rapid and applicable to cell or tissue fractions from any source, making the approach a flexible tool for initial protein-protein interaction discovery.National Institutes of Health (U.S.) (Grant R21-CA-140030-01

Distance to range edge determines sensitivity to deforestation

It is generally assumed that deforestation affects a species consistently across space, however populations near their geographic range edge may exist at their niche limits and therefore be more sensitive to disturbance. We found that both within and across Atlantic Forest bird species, populations are more sensitive to deforestation when near their range edge. In fact, the negative effects of deforestation on bird occurrences switched to positive in the range core (>829 km), in line with Ellenberg’s rule. We show that the proportion of populations at their range core and edge varies across Brazil, suggesting deforestation effects on communities, and hence the most appropriate conservation action, also vary geographically

Identification of an elaborate complex mediating postsynaptic inhibition

Inhibitory synapses dampen neuronal activity through postsynaptic hyperpolarization. The composition of the inhibitory postsynapse and the mechanistic basis of its regulation, however, remains poorly understood. We used an in vivo chemico-genetic proximity-labeling approach to discover inhibitory postsynaptic proteins. Quantitative mass spectrometry not only recapitulated known inhibitory postsynaptic proteins, but also revealed a large network of new proteins, many of which are either implicated in neurodevelopmental disorders or are of unknown function. CRISPR-depletion of one of these previously uncharacterized proteins, InSyn1, led to decreased postsynaptic inhibitory sites, reduced frequency of miniature inhibitory currents, and increased excitability in the hippocampus. Our findings uncover a rich and functionally diverse assemblage of previously unknown proteins that regulate postsynaptic inhibition and might contribute to developmental brain disorders