Differential cAMP signaling at hippocampal output synapses

cAMP is a critical second messenger involved in synaptic transmission and synaptic plasticity. Here, we show that activation of the adenylyl cyclase by forskolin and application of the cAMP-analog Sp-5,6-DCl-cBIMPS both mimicked and occluded tetanus-induced long-term potentiation (LTP) in subicular bursting neurons, but not in subicular regular firing cells. Furthermore, LTP in bursting cells was inhibited by protein kinase A (PKA) inhibitors Rp-8-CPT-cAMP and H-89. Variations in the degree of EPSC blockade by the low-affinity competitive AMPA receptor-antagonist {gamma}-d-glutamyl-glycine ({gamma}-DGG), analysis of the coefficient of variance as well as changes in short-term potentiation suggest an increase of glutamate concentration in the synaptic cleft after expression of LTP. We conclude that presynaptic LTP in bursting cells requires activation of PKA by a calcium-dependent adenylyl cyclase while LTP in regular firing cells is independent of elevated cAMP levels. Our results provide evidence for a differential role of cAMP in LTP at hippocampal output synapses

Collective Animal Behavior from Bayesian Estimation and Probability Matching

Animals living in groups make movement decisions that depend, among other factors, on social interactions with other group members. Our present understanding of social rules in animal collectives is based on empirical fits to observations and we lack first-principles approaches that allow their derivation. Here we show that patterns of collective decisions can be derived from the basic ability of animals to make probabilistic estimations in the presence of uncertainty. We build a decision-making model with two stages: Bayesian estimation and probabilistic matching.
In the first stage, each animal makes a Bayesian estimation of which behavior is best to perform taking into account personal information about the environment and social information collected by observing the behaviors of other animals. In the probability matching stage, each animal chooses a behavior with a probability given by the Bayesian estimation that this behavior is the most appropriate one. This model derives very simple rules of interaction in animal collectives that depend only on two types of reliability parameters, one that each animal assigns to the other animals and another given by the quality of the non-social information. We test our model by obtaining theoretically a rich set of observed collective patterns of decisions in three-spined sticklebacks, Gasterosteus aculeatus, a shoaling fish species. The quantitative link shown between probabilistic estimation and collective rules of behavior allows a better contact with other fields such as foraging, mate selection, neurobiology and psychology, and gives predictions for experiments directly testing the relationship between estimation and collective behavior

A Simple Artificial Life Model Explains Irrational Behavior in Human Decision-Making

Although praised for their rationality, humans often make poor decisions, even in simple situations. In the repeated binary choice experiment, an individual has to choose repeatedly between the same two alternatives, where a reward is assigned to one of them with fixed probability. The optimal strategy is to perseverate with choosing the alternative with the best expected return. Whereas many species perseverate, humans tend to match the frequencies of their choices to the frequencies of the alternatives, a sub-optimal strategy known as probability matching. Our goal was to find the primary cognitive constraints under which a set of simple evolutionary rules can lead to such contrasting behaviors. We simulated the evolution of artificial populations, wherein the fitness of each animat (artificial animal) depended on its ability to predict the next element of a sequence made up of a repeating binary string of varying size. When the string was short relative to the animats’ neural capacity, they could learn it and correctly predict the next element of the sequence. When it was long, they could not learn it, turning to the next best option: to perseverate. Animats from the last generation then performed the task of predicting the next element of a non-periodical binary sequence. We found that, whereas animats with smaller neural capacity kept perseverating with the best alternative as before, animats with larger neural capacity, which had previously been able to learn the pattern of repeating strings, adopted probability matching, being outperformed by the perseverating animats. Our results demonstrate how the ability to make predictions in an environment endowed with regular patterns may lead to probability matching under less structured conditions. They point to probability matching as a likely by-product of adaptive cognitive strategies that were crucial in human evolution, but may lead to sub-optimal performances in other environments

How Haptic Size Sensations Improve Distance Perception

Determining distances to objects is one of the most ubiquitous perceptual tasks in everyday life. Nevertheless, it is challenging because the information from a single image confounds object size and distance. Though our brains frequently judge distances accurately, the underlying computations employed by the brain are not well understood. Our work illuminates these computions by formulating a family of probabilistic models that encompass a variety of distinct hypotheses about distance and size perception. We compare these models' predictions to a set of human distance judgments in an interception experiment and use Bayesian analysis tools to quantitatively select the best hypothesis on the basis of its explanatory power and robustness over experimental data. The central question is: whether, and how, human distance perception incorporates size cues to improve accuracy. Our conclusions are: 1) humans incorporate haptic object size sensations for distance perception, 2) the incorporation of haptic sensations is suboptimal given their reliability, 3) humans use environmentally accurate size and distance priors, 4) distance judgments are produced by perceptual “posterior sampling”. In addition, we compared our model's estimated sensory and motor noise parameters with previously reported measurements in the perceptual literature and found good correspondence between them. Taken together, these results represent a major step forward in establishing the computational underpinnings of human distance perception and the role of size information.National Institutes of Health (U.S.) (NIH grant R01EY015261)University of Minnesota (UMN Graduate School Fellowship)National Science Foundation (U.S.) (Graduate Research Fellowship)University of Minnesota (UMN Doctoral Dissertation Fellowship)National Institutes of Health (U.S.) (NIH NRSA grant F32EY019228-02)Ruth L. Kirschstein National Research Service Awar

Protease-activated receptor 2 activation induces behavioural changes associated with depression-like behaviour through microglial-independent modulation of inflammatory cytokines

Rationale: Major depressive disorder (MDD) is a leading cause of disability worldwide but currently prescribed treatments do not adequately ameliorate the disorder in a significant portion of patients. Hence, a better appreciation of its aetiology may lead to the development of novel therapies. Objectives: In the present study, we have built on our previous findings indicating a role for protease-activated receptor-2 (PAR2) in sickness behaviour to determine whether the PAR2 activator, AC264613, induces behavioural changes similar to those observed in depression-like behaviour. Methods: AC264613-induced behavioural changes were examined using the open field test (OFT), sucrose preference test (SPT), elevated plus maze (EPM), and novel object recognition test (NOR). Whole-cell patch clamping was used to investigate the effects of PAR2 activation in the lateral habenula with peripheral and central cytokine levels determined using ELISA and quantitative PCR. Results: Using a blood–brain barrier (BBB) permeable PAR2 activator, we reveal that AC-264613 (AC) injection leads to reduced locomotor activity and sucrose preference in mice but is without effect in anxiety and memory-related tasks. In addition, we show that AC injection leads to elevated blood sera IL-6 levels and altered cytokine mRNA expression within the brain. However, neither microglia nor peripheral lymphocytes are the source of these altered cytokine profiles. Conclusions: These data reveal that PAR2 activation results in behavioural changes often associated with depression-like behaviour and an inflammatory profile that resembles that seen in patients with MDD and therefore PAR2 may be a target for novel antidepressant therapies

Control of somatosensory cortical processing by thalamic posterior medial nucleus: A new role of thalamus in cortical function

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Current knowledge of thalamocortical interaction comes mainly from studying lemniscal thalamic systems. Less is known about paralemniscal thalamic nuclei function. In the vibrissae system, the posterior medial nucleus (POm) is the corresponding paralemniscal nucleus. POm neurons project to L1 and L5A of the primary somatosensory cortex (S1) in the rat brain. It is known that L1 modifies sensory-evoked responses through control of intracortical excitability suggesting that L1 exerts an influence on whisker responses. Therefore, thalamocortical pathways targeting L1 could modulate cortical firing. Here, using a combination of electrophysiology and pharmacology in vivo, we have sought to determine how POm influences cortical processing. In our experiments, single unit recordings performed in urethane- anesthetized rats showed that POm imposes precise control on the magnitude and duration of supra- and infragranular barrel cortex whisker responses. Our findings demonstrated that L1 inputs from POm imposed a time and intensity dependent regulation on cortical sensory processing. Moreover, we found that blocking L1 GABAergic inhibition or blocking P/Q-type Ca2+ channels in L1 prevents POm adjustment of whisker responses in the barrel cortex. Additionally, we found that POm was also controlling the sensory processing in S2 and this regulation was modulated by corticofugal activity from L5 in S1. Taken together, our data demonstrate the determinant role exerted by the POm in the adjustment of somatosensory cortical processing and in the regulation of cortical processing between S1 and S2. We propose that this adjustment could be a thalamocortical gain regulation mechanism also present in the processing of information between cortical areas.This work was supported by a grant from Ministerio de Economia y Competitividad (BFU2012- 36107

On the Origins of Suboptimality in Human Probabilistic Inference

Humans have been shown to combine noisy sensory information with previous experience (priors), in qualitative and sometimes quantitative agreement with the statistically-optimal predictions of Bayesian integration. However, when the prior distribution becomes more complex than a simple Gaussian, such as skewed or bimodal, training takes much longer and performance appears suboptimal. It is unclear whether such suboptimality arises from an imprecise internal representation of the complex prior, or from additional constraints in performing probabilistic computations on complex distributions, even when accurately represented. Here we probe the sources of suboptimality in probabilistic inference using a novel estimation task in which subjects are exposed to an explicitly provided distribution, thereby removing the need to remember the prior. Subjects had to estimate the location of a target given a noisy cue and a visual representation of the prior probability density over locations, which changed on each trial. Different classes of priors were examined (Gaussian, unimodal, bimodal). Subjects' performance was in qualitative agreement with the predictions of Bayesian Decision Theory although generally suboptimal. The degree of suboptimality was modulated by statistical features of the priors but was largely independent of the class of the prior and level of noise in the cue, suggesting that suboptimality in dealing with complex statistical features, such as bimodality, may be due to a problem of acquiring the priors rather than computing with them. We performed a factorial model comparison across a large set of Bayesian observer models to identify additional sources of noise and suboptimality. Our analysis rejects several models of stochastic behavior, including probability matching and sample-averaging strategies. Instead we show that subjects' response variability was mainly driven by a combination of a noisy estimation of the parameters of the priors, and by variability in the decision process, which we represent as a noisy or stochastic posterior