Rupture of a liposomal vesicle

We discuss pore dynamics in osmotically stressed vesicles. A set of equations which govern the liposomal size, internal solute concentration, and pore diameter is solved numerically. We find that dependent on the internal solute concentration and vesicle size, liposomes can stay pore-free, nucleate a short lived pore, or nucleate a long-lived pore. The phase diagram of pore stability is constructed, and the different scaling regimes are deduced analytically.Comment: in press, Phys. Rev.

A Mean-Field Method for Generic Conductance-Based Integrate-and-Fire Neurons with Finite Timescales

The construction of transfer functions in theoretical neuroscience plays an important role in determining the spiking rate behavior of neurons in networks. These functions can be obtained through various fitting methods, but the biological relevance of the parameters is not always clear. However, for stationary inputs, such functions can be obtained without the adjustment of free parameters by using mean-field methods. In this work, we expand current Fokker-Planck approaches to account for the concurrent influence of colored and multiplicative noise terms on generic conductance-based integrate-and-fire neurons. We reduce the resulting stochastic system from the application of the diffusion approximation to a one-dimensional Langevin equation. An effective Fokker-Planck is then constructed using Fox Theory, which is solved numerically to obtain the transfer function. The solution is capable of reproducing the transfer function behavior of simulated neurons across a wide range of parameters. The method can also be easily extended to account for different sources of noise with various multiplicative terms, and it can be used in other types of problems in principle.Comment: 11 pages, 6 figures, research articl

Simulations of viscous shape relaxation in shuffled foam clusters

We simulate the shape relaxation of foam clusters and compare them with the time exponential expected for Newtonian fluid. Using two-dimensional Potts Model simulations, we artificially create holes in a foam cluster and shuffle it by applying shear strain cycles. We reproduce the experimentally observed time exponential relaxation of cavity shapes in the foam as a function of the number of strain steps. The cavity rounding up results from local rearrangement of bubbles, due to the conjunction of both a large applied strain and local bubble wall fluctuations

Internal Cholinergic Regulation of Learning and Recall in a Model of Olfactory Processing

In the olfactory system, cholinergic modulation has been associated with contrast modulation and changes in receptive fields in the olfactory bulb, as well the learning of odor associations in olfactory cortex. Computational modeling and behavioral studies suggest that cholinergic modulation could improve sensory processing and learning while preventing pro-active interference when task demands are high. However, how sensory inputs and/or learning regulate incoming modulation has not yet been elucidated. We here use a computational model of the olfactory bulb, piriform cortex (PC) and horizontal limb of the diagonal band of Broca (HDB) to explore how olfactory learning could regulate cholinergic inputs to the system in a closed feedback loop. In our model, the novelty of an odor is reflected in firing rates and sparseness of cortical neurons in response to that odor and these firing rates can directly regulate learning in the system by modifying cholinergic inputs to the system. In the model, cholinergic neurons reduce their firing in response to familiar odors—reducing plasticity in the PC, but increase their firing in response to novel odor—increasing PC plasticity. Recordings from HDB neurons in awake behaving rats reflect predictions from the model by showing that a subset of neurons decrease their firing as an odor becomes familiar

Maturation of pyramidal cells in anterior piriform cortex may be sufficient to explain the end of early olfactory learning in rats

Studies have shown that neonate rodents exhibit high ability to learn a preference for novel odors associated with thermotactile stimuli that mimics maternal care. Artificial odors paired with vigorous strokes in rat pups younger than 10 postnatal days (P), but not older, rapidly induce an orientation-approximation behavior toward the conditioned odor in a two-choice preference test. The olfactory bulb (OB) and the anterior olfactory cortex (aPC), both modulated by norepinephrine (NE), have been identified as part of a neural circuit supporting this transitory olfactory learning. One possible explanation at the neuronal level for why the odor-stroke pairing induces consistent orientation-approximation behavior in P10, is the coincident activation of prior existent neurons in the aPC mediating this behavior. Specifically, odorstroke conditioning in P10 pups, promoting orientation-approximation behavior in the former but not in the latter. In order to test this hypothesis, we performed in vitro patch-clamp recordings of the aPC pyramidal neurons from rat pups from two age groups (P5–P8 and P14–P17) and built computational models for the OB-aPC neural circuit based on this physiological data. We conditioned the P5–P8 OB-aPC artificial circuit to an odor associated with NE activation (representing the process of maternal odor learning during mother–infant interactions inside the nest) and then evaluated the response of the OB-aPC circuit to the presentation of the conditioned odor. The results show that the number of responsive aPC neurons to the presentation of the conditioned odor in the P14–P17 OB-aPC circuit was lower than in the P5–P8 circuit, suggesting that at P14–P17, the reduced number of responsive neurons to the conditioned (maternal) odor might not be coincident with the responsive neurons for a second conditioned odor

The maturational characteristics of the GABA input in the anterior piriform cortex may also contribute to the rapid learning of the maternal odor during the sensitive period

During the first ten postnatal days (P), infant rodents can learn olfactory preferences for novel odors if they are paired with thermo-tactile stimuli that mimic components of maternal care. After P10, the thermo-tactile pairing becomes ineffective for conditioning. The current explanation for this change in associative learning is the alteration in the norepinephrine (NE) inputs from the locus coeruleus (LC) to the olfactory bulb (OB) and the anterior piriform cortex (aPC). By combining patchclamp electrophysiology and computational simulations, we showed in a recent work that a transitory high responsiveness of the OB-aPC circuit to the maternal odor is an alternative mechanism that could also explain early olfactory preference learning and its cessation after P10. That result relied solely on the maturational properties of the aPC pyramidal cells. However, the GABAergic system undergoes important changes during the same period. To address the importance of the maturation of the GABAergic system for early olfactory learning, we incorporated data from the GABA inputs, obtained from in vitro patch-clamp experiment in the aPC of rat pups aged P5–P7 reported here, to the model proposed in our previous publication. In the younger than P10 OB-aPC circuit with GABA synaptic input, the number of responsive aPC pyramidal cells to the conditioned maternal odor was amplified in 30% compared to the circuit without GABAergic input. When compared with the circuit with other younger than P10 OB-aPC circuit with adult GABAergic input profile, this amplification was 88%. Together, our results suggest that during the olfactory preference learning in younger than P10, the GABAergic synaptic input presumably acts by depolarizing the aPC pyramidal neurons in such a way that it leads to the amplification of the pyramidal neurons response to the conditioned maternal odor. Furthermore, our results suggest that during this developmental period, the aPC pyramidal cells themselves seem to resolve the apparent lack of GABAergic synaptic inhibition by a strong firing adaptation in response to increased depolarizing inputs

Rating Distributions and Bayesian Inference. Enhancing Cognitive Models of Spatial Language Use

Kluth T, Schultheis H. Rating Distributions and Bayesian Inference. Enhancing Cognitive Models of Spatial Language Use. In: Idiart M, Lenci A, Poibeau T, Villavicencio A, eds. Proceedings of the Eighth Workshop on Cognitive Aspects of Computational Language Learning and Processing. Melbourne, Australia: Association for Computational Linguistics; 2018: 47-55.We present two methods that improve the assessment of cognitive models. The first method is applicable to models computing average acceptability ratings. For these models, we propose an extension that simulates a full rating distribution (instead of average ratings) and allows generating individual ratings. Our second method enables Bayesian inference for models generating individual data. To this end, we propose to use the cross-match test (Rosenbaum, 2005) as a likelihood function. We exemplarily present both methods using cognitive models from the domain of spatial language use. For spatial language use, determining linguistic acceptability judgments of a spatial preposition for a depicted spatial relation is assumed to be a crucial process (Logan and Sadler, 1996). Existing models of this process compute an average acceptability rating. We extend the models and – based on existing data – show that the extended models allow extracting more information from the empirical data and yield more readily interpretable information about model successes and failures. Applying Bayesian inference, we find that model performance relies less on mechanisms of capturing geometrical aspects than on mapping the captured geometry to a rating interval

How the brain represents language and answers questions? Using an AI system to understand the underlying neurobiological mechanisms

To understand the computations that underlie high-level cognitive processes we propose a framework of mechanisms that could in principle implement START, an AI program that answers questions using natural language. START organizes a sentence into a series of triplets, each containing three elements (subject, verb, object). We propose that the brain similarly defines triplets and then chunks the three elements into a spatial pattern. A complete sentence can be represented using up to 7 triplets in a working memory buffer organized by theta and gamma oscillations. This buffer can transfer information into long-term memory networks where a second chunking operation converts the serial triplets into a single spatial pattern in a network, with each triplet (with corresponding elements) represented in specialized subregions. The triplets that define a sentence become synaptically linked, thereby encoding the sentence in synaptic weights. When a question is posed, there is a search for the closest stored memory (having the greatest number of shared triplets). We have devised a search process that does not require that the question and the stored memory have the same number of triplets or have triplets in the same order. Once the most similar memory is recalled and undergoes 2-level dechunking, the sought for information can be obtained by element-by-element comparison of the key triplet in the question to the corresponding triplet in the retrieved memory. This search may require a reordering to align corresponding triplets, the use of pointers that link different triplets, or the use of semantic memory. Our framework uses 12 network processes; existing models can implement many of these, but in other cases we can only suggest neural implementations. Overall, our scheme provides the first view of how language-based question answering could be implemented by the brain