Generalized Bienenstock-Cooper-Munro rule for spiking neurons that maximizes information transmission

Maximization of information transmission by a spiking-neuron model predicts changes of synaptic connections that depend on timing of pre- and postsynaptic spikes and on the postsynaptic membrane potential. Under the assumption of Poisson firing statistics, the synaptic update rule exhibits all of the features of the Bienenstock-Cooper-Munro rule, in particular, regimes of synaptic potentiation and depression separated by a sliding threshold. Moreover, the learning rule is also applicable to the more realistic case of neuron models with refractoriness, and is sensitive to correlations between input spikes, even in the absence of presynaptic rate modulation. The learning rule is found by maximizing the mutual information between presynaptic and postsynaptic spike trains under the constraint that the postsynaptic firing rate stays close to some target firing rate. An interpretation of the synaptic update rule in terms of homeostatic synaptic processes and spike-timing-dependent plasticity is discussed

How Gibbs distributions may naturally arise from synaptic adaptation mechanisms. A model-based argumentation

This paper addresses two questions in the context of neuronal networks dynamics, using methods from dynamical systems theory and statistical physics: (i) How to characterize the statistical properties of sequences of action potentials ("spike trains") produced by neuronal networks ? and; (ii) what are the effects of synaptic plasticity on these statistics ? We introduce a framework in which spike trains are associated to a coding of membrane potential trajectories, and actually, constitute a symbolic coding in important explicit examples (the so-called gIF models). On this basis, we use the thermodynamic formalism from ergodic theory to show how Gibbs distributions are natural probability measures to describe the statistics of spike trains, given the empirical averages of prescribed quantities. As a second result, we show that Gibbs distributions naturally arise when considering "slow" synaptic plasticity rules where the characteristic time for synapse adaptation is quite longer than the characteristic time for neurons dynamics.Comment: 39 pages, 3 figure

Mechanisms explaining transitions between tonic and phasic firing in neuronal populations as predicted by a low dimensional firing rate model

Several firing patterns experimentally observed in neural populations have been successfully correlated to animal behavior. Population bursting, hereby regarded as a period of high firing rate followed by a period of quiescence, is typically observed in groups of neurons during behavior. Biophysical membrane-potential models of single cell bursting involve at least three equations. Extending such models to study the collective behavior of neural populations involves thousands of equations and can be very expensive computationally. For this reason, low dimensional population models that capture biophysical aspects of networks are needed. \noindent The present paper uses a firing-rate model to study mechanisms that trigger and stop transitions between tonic and phasic population firing. These mechanisms are captured through a two-dimensional system, which can potentially be extended to include interactions between different areas of the nervous system with a small number of equations. The typical behavior of midbrain dopaminergic neurons in the rodent is used as an example to illustrate and interpret our results. \noindent The model presented here can be used as a building block to study interactions between networks of neurons. This theoretical approach may help contextualize and understand the factors involved in regulating burst firing in populations and how it may modulate distinct aspects of behavior.Comment: 25 pages (including references and appendices); 12 figures uploaded as separate file

Noise Suppression and Surplus Synchrony by Coincidence Detection

The functional significance of correlations between action potentials of neurons is still a matter of vivid debates. In particular it is presently unclear how much synchrony is caused by afferent synchronized events and how much is intrinsic due to the connectivity structure of cortex. The available analytical approaches based on the diffusion approximation do not allow to model spike synchrony, preventing a thorough analysis. Here we theoretically investigate to what extent common synaptic afferents and synchronized inputs each contribute to closely time-locked spiking activity of pairs of neurons. We employ direct simulation and extend earlier analytical methods based on the diffusion approximation to pulse-coupling, allowing us to introduce precisely timed correlations in the spiking activity of the synaptic afferents. We investigate the transmission of correlated synaptic input currents by pairs of integrate-and-fire model neurons, so that the same input covariance can be realized by common inputs or by spiking synchrony. We identify two distinct regimes: In the limit of low correlation linear perturbation theory accurately determines the correlation transmission coefficient, which is typically smaller than unity, but increases sensitively even for weakly synchronous inputs. In the limit of high afferent correlation, in the presence of synchrony a qualitatively new picture arises. As the non-linear neuronal response becomes dominant, the output correlation becomes higher than the total correlation in the input. This transmission coefficient larger unity is a direct consequence of non-linear neural processing in the presence of noise, elucidating how synchrony-coded signals benefit from these generic properties present in cortical networks

Synergistic effects of oncolytic reovirus and docetaxel chemotherapy in prostate cancer

Reovirus type 3 Dearing (T3D) has demonstrated oncolytic activity in vitro, in in vivo murine models and in early clinical trials. However the true potential of oncolytic viruses may only be realized fully in combination with other modalities such as chemotherapy, targeted therapy and radiotherapy. In this study, we examine the oncolytic activity of reovirus T3D and chemotherapeutic agents against human prostate cancer cell lines, with particular focus on the highly metastatic cell line PC3 and the chemotherapeutic agent docetaxel. Docetaxel is the standard of care for metastatic prostate cancer and acts by disrupting the normal process of microtubule assembly and disassembly. Reoviruses have been shown to associate with microtubules and may require this association for efficient viral replication

Robustness of Learning That Is Based on Covariance-Driven Synaptic Plasticity

It is widely believed that learning is due, at least in part, to long-lasting modifications of the strengths of synapses in the brain. Theoretical studies have shown that a family of synaptic plasticity rules, in which synaptic changes are driven by covariance, is particularly useful for many forms of learning, including associative memory, gradient estimation, and operant conditioning. Covariance-based plasticity is inherently sensitive. Even a slight mistuning of the parameters of a covariance-based plasticity rule is likely to result in substantial changes in synaptic efficacies. Therefore, the biological relevance of covariance-based plasticity models is questionable. Here, we study the effects of mistuning parameters of the plasticity rule in a decision making model in which synaptic plasticity is driven by the covariance of reward and neural activity. An exact covariance plasticity rule yields Herrnstein's matching law. We show that although the effect of slight mistuning of the plasticity rule on the synaptic efficacies is large, the behavioral effect is small. Thus, matching behavior is robust to mistuning of the parameters of the covariance-based plasticity rule. Furthermore, the mistuned covariance rule results in undermatching, which is consistent with experimentally observed behavior. These results substantiate the hypothesis that approximate covariance-based synaptic plasticity underlies operant conditioning. However, we show that the mistuning of the mean subtraction makes behavior sensitive to the mistuning of the properties of the decision making network. Thus, there is a tradeoff between the robustness of matching behavior to changes in the plasticity rule and its robustness to changes in the properties of the decision making network

Xnrs and Activin Regulate Distinct Genes during Xenopus Development: Activin Regulates Cell Division

BACKGROUND: The mesoderm of the amphibian embryo is formed through an inductive interaction in which vegetal cells of the blastula-staged embryo act on overlying equatorial cells. Candidate mesoderm-inducing factors include members of the transforming growth factor type β family such as Vg1, activin B, the nodal-related proteins and derrière. METHODOLOGY AND PRINCIPLE FINDINGS: Microarray analysis reveals different functions for activin B and the nodal-related proteins during early Xenopus development. Inhibition of nodal-related protein function causes the down-regulation of regionally expressed genes such as chordin, dickkopf and XSox17α/β, while genes that are mis-regulated in the absence of activin B tend to be more widely expressed and, interestingly, include several that are involved in cell cycle regulation. Consistent with the latter observation, cells of the involuting dorsal axial mesoderm, which normally undergo cell cycle arrest, continue to proliferate when the function of activin B is inhibited. CONCLUSIONS/SIGNIFICANCE: These observations reveal distinct functions for these two classes of the TGF-β family during early Xenopus development, and in doing so identify a new role for activin B during gastrulation

A discrete time neural network model with spiking neurons II. Dynamics with noise

We provide rigorous and exact results characterizing the statistics of spike trains in a network of leaky integrate and fire neurons, where time is discrete and where neurons are submitted to noise, without restriction on the synaptic weights. We show the existence and uniqueness of an invariant measure of Gibbs type and discuss its properties. We also discuss Markovian approximations and relate them to the approaches currently used in computational neuroscience to analyse experimental spike trains statistics.Comment: 43 pages - revised version - to appear il Journal of Mathematical Biolog

Prevalence and Genetic Characterization of Pertactin-Deficient Bordetella pertussis in Japan

The adhesin pertactin (Prn) is one of the major virulence factors of Bordetella pertussis, the etiological agent of whooping cough. However, a significant prevalence of Prn-deficient (Prn−) B. pertussis was observed in Japan. The Prn− isolate was first discovered in 1997, and 33 (27%) Prn− isolates were identified among 121 B. pertussis isolates collected from 1990 to 2009. Sequence analysis revealed that all the Prn− isolates harbor exclusively the vaccine-type prn1 allele and that loss of Prn expression is caused by 2 different mutations: an 84-bp deletion of the prn signal sequence (prn1ΔSS, n = 24) and an IS481 insertion in prn1 (prn1::IS481, n = 9). The frequency of Prn− isolates, notably those harboring prn1ΔSS, significantly increased since the early 2000s, and Prn− isolates were subsequently found nationwide. Multilocus variable-number tandem repeat analysis (MLVA) revealed that 24 (73%) of 33 Prn− isolates belong to MLVA-186, and 6 and 3 Prn− isolates belong to MLVA-194 and MLVA-226, respectively. The 3 MLVA types are phylogenetically closely related, suggesting that the 2 Prn− clinical strains (harboring prn1ΔSS and prn1::IS481) have clonally expanded in Japan. Growth competition assays in vitro also demonstrated that Prn− isolates have a higher growth potential than the Prn+ back-mutants from which they were derived. Our observations suggested that human host factors (genetic factors and immune status) that select for Prn− strains have arisen and that Prn expression is not essential for fitness under these conditions