@article{537,
abstract = {Transgenerational effects are broader than only parental relationships. Despite mounting evidence that multigenerational effects alter phenotypic and life-history traits, our understanding of how they combine to determine fitness is not well developed because of the added complexity necessary to study them. Here, we derive a quantitative genetic model of adaptation to an extraordinary new environment by an additive genetic component, phenotypic plasticity, maternal and grandmaternal effects. We show how, at equilibrium, negative maternal and negative grandmaternal effects maximize expected population mean fitness. We define negative transgenerational effects as those that have a negative effect on trait expression in the subsequent generation, that is, they slow, or potentially reverse, the expected evolutionary dynamic. When maternal effects are positive, negative grandmaternal effects are preferred. As expected under Mendelian inheritance, the grandmaternal effects have a lower impact on fitness than the maternal effects, but this dual inheritance model predicts a more complex relationship between maternal and grandmaternal effects to constrain phenotypic variance and so maximize expected population mean fitness in the offspring.},
author = {Prizak, Roshan and Ezard, Thomas and Hoyle, Rebecca},
journal = {Ecology and Evolution},
number = {15},
pages = {3139 -- 3145},
publisher = {Wiley-Blackwell},
title = {{Fitness consequences of maternal and grandmaternal effects}},
doi = {10.1002/ece3.1150},
volume = {4},
year = {2014},
}
@inproceedings{1708,
abstract = {It has been long argued that, because of inherent ambiguity and noise, the brain needs to represent uncertainty in the form of probability distributions. The neural encoding of such distributions remains however highly controversial. Here we present a novel circuit model for representing multidimensional real-valued distributions using a spike based spatio-temporal code. Our model combines the computational advantages of the currently competing models for probabilistic codes and exhibits realistic neural responses along a variety of classic measures. Furthermore, the model highlights the challenges associated with interpreting neural activity in relation to behavioral uncertainty and points to alternative population-level approaches for the experimental validation of distributed representations.},
author = {Savin, Cristina and Denève, Sophie},
location = {Montreal, Canada},
number = {January},
pages = {2024 -- 2032},
publisher = {Neural Information Processing Systems},
title = {{Spatio-temporal representations of uncertainty in spiking neural networks}},
volume = {3},
year = {2014},
}
@article{1931,
abstract = {A wealth of experimental evidence suggests that working memory circuits preferentially represent information that is behaviorally relevant. Still, we are missing a mechanistic account of how these representations come about. Here we provide a simple explanation for a range of experimental findings, in light of prefrontal circuits adapting to task constraints by reward-dependent learning. In particular, we model a neural network shaped by reward-modulated spike-timing dependent plasticity (r-STDP) and homeostatic plasticity (intrinsic excitability and synaptic scaling). We show that the experimentally-observed neural representations naturally emerge in an initially unstructured circuit as it learns to solve several working memory tasks. These results point to a critical, and previously unappreciated, role for reward-dependent learning in shaping prefrontal cortex activity.},
author = {Savin, Cristina and Triesch, Jochen},
journal = {Frontiers in Computational Neuroscience},
number = {MAY},
publisher = {Frontiers Research Foundation},
title = {{Emergence of task-dependent representations in working memory circuits}},
doi = {10.3389/fncom.2014.00057},
volume = {8},
year = {2014},
}
@article{2257,
abstract = {Maximum entropy models are the least structured probability distributions that exactly reproduce a chosen set of statistics measured in an interacting network. Here we use this principle to construct probabilistic models which describe the correlated spiking activity of populations of up to 120 neurons in the salamander retina as it responds to natural movies. Already in groups as small as 10 neurons, interactions between spikes can no longer be regarded as small perturbations in an otherwise independent system; for 40 or more neurons pairwise interactions need to be supplemented by a global interaction that controls the distribution of synchrony in the population. Here we show that such “K-pairwise” models—being systematic extensions of the previously used pairwise Ising models—provide an excellent account of the data. We explore the properties of the neural vocabulary by: 1) estimating its entropy, which constrains the population's capacity to represent visual information; 2) classifying activity patterns into a small set of metastable collective modes; 3) showing that the neural codeword ensembles are extremely inhomogenous; 4) demonstrating that the state of individual neurons is highly predictable from the rest of the population, allowing the capacity for error correction.},
author = {Tkacik, Gasper and Marre, Olivier and Amodei, Dario and Schneidman, Elad and Bialek, William and Berry, Michael},
issn = {1553734X},
journal = {PLoS Computational Biology},
number = {1},
publisher = {Public Library of Science},
title = {{Searching for collective behavior in a large network of sensory neurons}},
doi = {10.1371/journal.pcbi.1003408},
volume = {10},
year = {2014},
}
@article{1886,
abstract = {Information processing in the sensory periphery is shaped by natural stimulus statistics. In the periphery, a transmission bottleneck constrains performance; thus efficient coding implies that natural signal components with a predictably wider range should be compressed. In a different regime—when sampling limitations constrain performance—efficient coding implies that more resources should be allocated to informative features that are more variable. We propose that this regime is relevant for sensory cortex when it extracts complex features from limited numbers of sensory samples. To test this prediction, we use central visual processing as a model: we show that visual sensitivity for local multi-point spatial correlations, described by dozens of independently-measured parameters, can be quantitatively predicted from the structure of natural images. This suggests that efficient coding applies centrally, where it extends to higher-order sensory features and operates in a regime in which sensitivity increases with feature variability.},
author = {Hermundstad, Ann and Briguglio, John and Conte, Mary and Victor, Jonathan and Balasubramanian, Vijay and Tkacik, Gasper},
journal = {eLife},
number = {November},
publisher = {eLife Sciences Publications},
title = {{Variance predicts salience in central sensory processing}},
doi = {10.7554/eLife.03722},
year = {2014},
}
@article{2277,
abstract = {Redundancies and correlations in the responses of sensory neurons may seem to waste neural resources, but they can also carry cues about structured stimuli and may help the brain to correct for response errors. To investigate the effect of stimulus structure on redundancy in retina, we measured simultaneous responses from populations of retinal ganglion cells presented with natural and artificial stimuli that varied greatly in correlation structure; these stimuli and recordings are publicly available online. Responding to spatio-temporally structured stimuli such as natural movies, pairs of ganglion cells were modestly more correlated than in response to white noise checkerboards, but they were much less correlated than predicted by a non-adapting functional model of retinal response. Meanwhile, responding to stimuli with purely spatial correlations, pairs of ganglion cells showed increased correlations consistent with a static, non-adapting receptive field and nonlinearity. We found that in response to spatio-temporally correlated stimuli, ganglion cells had faster temporal kernels and tended to have stronger surrounds. These properties of individual cells, along with gain changes that opposed changes in effective contrast at the ganglion cell input, largely explained the pattern of pairwise correlations across stimuli where receptive field measurements were possible.},
author = {Simmons, Kristina and Prentice, Jason and Tkacik, Gasper and Homann, Jan and Yee, Heather and Palmer, Stephanie and Nelson, Philip and Balasubramanian, Vijay},
journal = {PLoS Computational Biology},
number = {12},
publisher = {Public Library of Science},
title = {{Transformation of stimulus correlations by the retina}},
doi = {10.1371/journal.pcbi.1003344},
volume = {9},
year = {2013},
}
@article{2861,
abstract = {We consider a two-parameter family of piecewise linear maps in which the moduli of the two slopes take different values. We provide numerical evidence of the existence of some parameter regions in which the Lyapunov exponent and the topological entropy remain constant. Analytical proof of this phenomenon is also given for certain cases. Surprisingly however, the systems with that property are not conjugate as we prove by using kneading theory.},
author = {Botella Soler, Vicente and Oteo, José and Ros, Javier and Glendinning, Paul},
journal = {Journal of Physics A: Mathematical and Theoretical},
number = {12},
publisher = {IOP Publishing Ltd.},
title = {{Lyapunov exponent and topological entropy plateaus in piecewise linear maps}},
doi = {10.1088/1751-8113/46/12/125101},
volume = {46},
year = {2013},
}
@inbook{2413,
abstract = {Progress in understanding the global brain dynamics has remained slow to date in large part because of the highly multiscale nature of brain activity. Indeed, normal brain dynamics is characterized by complex interactions between multiple levels: from the microscopic scale of single neurons to the mesoscopic level of local groups of neurons, and finally to the macroscopic level of the whole brain. Among the most difficult tasks are those of identifying which scales are significant for a given particular function and describing how the scales affect each other. It is important to realize that the scales of time and space are linked together, or even intertwined, and that causal inference is far more ambiguous between than within levels. We approach this problem from the perspective of our recent work on simultaneous recording from micro- and macroelectrodes in the human brain. We propose a physiological description of these multilevel interactions, based on phase–amplitude coupling of neuronal oscillations that operate at multiple frequencies and on different spatial scales. Specifically, the amplitude of the oscillations on a particular spatial scale is modulated by phasic variations in neuronal excitability induced by lower frequency oscillations that emerge on a larger spatial scale. Following this general principle, it is possible to scale up or scale down the multiscale brain dynamics. It is expected that large-scale network oscillations in the low-frequency range, mediating downward effects, may play an important role in attention and consciousness.},
author = {Valderrama, Mario and Botella Soler, Vicente and Le Van Quyen, Michel},
booktitle = {Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain},
editor = {Meyer, Misha and Pesenson, Z.},
isbn = {9783527411986 },
publisher = {Wiley-VCH},
title = {{Neuronal oscillations scale up and scale down the brain dynamics }},
doi = {10.1002/9783527671632.ch08},
year = {2013},
}
@article{2913,
abstract = {The ability of an organism to distinguish between various stimuli is limited by the structure and noise in the population code of its sensory neurons. Here we infer a distance measure on the stimulus space directly from the recorded activity of 100 neurons in the salamander retina. In contrast to previously used measures of stimulus similarity, this "neural metric" tells us how distinguishable a pair of stimulus clips is to the retina, based on the similarity between the induced distributions of population responses. We show that the retinal distance strongly deviates from Euclidean, or any static metric, yet has a simple structure: we identify the stimulus features that the neural population is jointly sensitive to, and show the support-vector-machine- like kernel function relating the stimulus and neural response spaces. We show that the non-Euclidean nature of the retinal distance has important consequences for neural decoding.},
author = {Tkacik, Gasper and Granot Atedgi, Einat and Segev, Ronen and Schneidman, Elad},
journal = {Physical Review Letters},
number = {5},
publisher = {American Physical Society},
title = {{Retinal metric: a stimulus distance measure derived from population neural responses}},
doi = {10.1103/PhysRevLett.110.058104},
volume = {110},
year = {2013},
}
@article{2850,
abstract = {Recent work emphasizes that the maximum entropy principle provides a bridge between statistical mechanics models for collective behavior in neural networks and experiments on networks of real neurons. Most of this work has focused on capturing the measured correlations among pairs of neurons. Here we suggest an alternative, constructing models that are consistent with the distribution of global network activity, i.e. the probability that K out of N cells in the network generate action potentials in the same small time bin. The inverse problem that we need to solve in constructing the model is analytically tractable, and provides a natural 'thermodynamics' for the network in the limit of large N. We analyze the responses of neurons in a small patch of the retina to naturalistic stimuli, and find that the implied thermodynamics is very close to an unusual critical point, in which the entropy (in proper units) is exactly equal to the energy. © 2013 IOP Publishing Ltd and SISSA Medialab srl.
},
author = {Tkacik, Gasper and Marre, Olivier and Mora, Thierry and Amodei, Dario and Berry, Michael and Bialek, William},
journal = {Journal of Statistical Mechanics Theory and Experiment},
number = {3},
publisher = {IOP Publishing Ltd.},
title = {{The simplest maximum entropy model for collective behavior in a neural network}},
doi = {10.1088/1742-5468/2013/03/P03011},
volume = {2013},
year = {2013},
}