Emergence of rules in cell assemblies of fLIF neurons.

Inspired by biological cognition, CABOT project explores the ways symbolic processing can emerge in a system of neural cell assemblies (CAs). Here we show how a stochastic meta–control process can regulate learning of associations between the CAs, the neural basis of symbols. An experiment illustrates the learning between CAs representing conditions actions pairs, which leads to CA–based representations of ‘if–then’ rules

Conflict resolution and learning probability matching in a neural cell-assembly architecture

Donald Hebb proposed a hypothesis that specialised groups of neurons, called cell-assemblies (CAs), form the basis for neural encoding of symbols in the human mind. It is not clear, however, how CAs can be re-used and combined to form new representations as in classical symbolic systems. We demonstrate that Hebbian learning of synaptic weights alone is not adequate for all tasks, and that additional meta-control processes should be involved. We describe an earlier proposed architecture implementing an adaptive conflict resolution process between CAs, and then evaluate it by modelling the probability matching phenomenon in a classic two-choice task. The model and its results are discussed in view of mathematical theory of learning and existing cognitive architectures

A model of probability matching in a two-choice task based on stochastic control of learning in neural cell-assemblies.

Donald Hebb proposed a hypothesis that specialised groups of neurons, called cell-assemblies (CAs), form the basis for neural encoding of symbols in the human mind. It is not clear, however, how CAs can be re-used and combined to form new representations as in classical symbolic systems. We demonstrate that Hebbian learning of synaptic weights alone is not adequate for all tasks, and that additional meta-control processes should be involved. We describe an earlier proposed architecture \cite{Belavkin08:_ecai08} implementing such a process, and then evaluate it by modelling the probability matching phenomenon in a classic two-choice task. The model and its results are discussed in view of mathematical theory of learning, and existing cognitive architectures as well as some hypotheses about neural functioning in the brain

A spiking half-cognitive model for classification

This paper describes a spiking neural network that learns classes. Following a classic Psychological task, the model learns some types of classes better than other types, so the net is a spiking cognitive model of classification. A simulated neural system, derived from an existing model, learns natural kinds, but is unable to form sufficient attractor states for all of the types of classes. An extension of the model, using a combination of singleton and triplets of input features, learns all of the types. The models make use of a principled mechanism for spontaneous firing, and a compensatory Hebbian learning rule. Combined, the mechanisms allow learning to spread to neurons not directly stimulated by the environment. The overall network learns the types of classes in a fashion broadly consistent with the Psychological data. However, the order of speed of learning the types is not entirely consistent with the Psychological data, but may be consistent with one of two Psychological systems a given person possesses. A Psychological test of this hypothesis is proposed

Counting with neurons: rule application with nets of fatiguing leaking integrate and fire neurons.

This paper shows a system that performs simple symbolic processing. The system is based entirely on fatiguing Leaky Integrate and Fire Neurons, a coarse model of neurons. following Hebb, the symbols are encoded by neirons that form Cell Assemblies. Additionally simple rules of the form i f X - X + 1 are encoded by Cell Assemblies, and this symbolic computation is performed. Finally, a more comples rule while X < F - X = X + 1 is encoded using variable binding via a compensatory learning rule. This rule performs the symbolic computation of counting entirely subsymbolically. the binding can be erased and reused via spontaneious neural activation. Unlike the symbolic parallel, the counting rule fails at times when humans might fail

University Of Sheffield: Description Of The Lasie-Ii System As Used For Muc-7

this article wewere largely successful with all slots except for the Entity Descriptor slot where scores were 50 # precision and 21 # recall. We will #rst explain the particular items we failed on, and then discuss why our Entity Descriptor slots were so poo

Benefit-Cost Analysis of FEMA Hazard Mitigation Grants

Mitigation ameliorates the impact of natural hazards on communities by reducing loss of life and injury, property and environmental damage, and social and economic disruption. The potential to reduce these losses brings many benefits, but every mitigation activity has a cost that must be considered in our world of limited resources. In principle benefit-cost analysis (BCA) can be used to assess a mitigation activity’s expected net benefits (discounted future benefits less discounted costs), but in practice this often proves difficult. This paper reports on a study that refined BCA methodologies and applied them to a national statistical sample of FEMA mitigation activities over a ten-year period for earthquake, flood, and wind hazards. The results indicate that the overall benefit-cost ratio for FEMA mitigation grants is about 4 to 1, though the ratio varies according to hazard and mitigation type.

Coordination and transfer

We study the ability of subjects to transfer principles between related coordination games. Subjects play a class of order statistic coordination games closely related to the well-known minimum (or weak-link) and median games (Van Huyck et al. in Am Econ Rev 80:234–248, 1990, Q J Econ 106(3):885–910, 1991). When subjects play a random sequence of games with differing order statistics, play is less sensitive to the order statistic than when a fixed order statistic is used throughout. This is consistent with the prediction of a simple learning model with transfer. If subjects play a series of similar stag hunt games, play converges to the payoff dominant equilibrium when a convention emerges, replicating the main result of Rankin et al. (Games Econ Behav 32:315–337, 2000). When these subjects subsequently play a random sequence of order statistic games, play is shifted towards the payoff dominant equilibrium relative to subjects without previous experience. The data is consistent with subjects absorbing a general principle, play of the payoff dominant equilibrium, and applying it in a new related setting

It is Hobbes, not Rousseau:an experiment on voting and redistribution

We perform an experiment which provides a laboratory replica of some important features of the welfare state. In the experiment, all individuals in a group decide whether to make a costly effort, which produces a random (independent) outcome for each one of them. The group members then vote on whether to redistribute the resulting and commonly known total sum of earnings equally amongst themselves. This game has two equilibria, if played once. In one of them, all players make effort and there is little redistribution. In the other one, there is no effort and nothingWe thank Iris Bohnet, Tim Cason, David Cooper, John Duffy, Maia Guell, John Van Huyck and Robin Mason for helpful conversations and encouragement. The comments of the Editor and two referees helped improve the paper. We gratefully acknowledge the financial support from Spain’s Ministry of Science and Innovation under grants CONSOLIDER INGENIO 2010 CSD2006-0016 (all authors), ECO2009-10531 (Cabrales), ECO2008-01768 (Nagel) and the Comunidad de Madrid under grant Excelecon (Cabrales), the Generalitat de Catalunya and the CREA program (Nagel), and project SEJ2007-64340 of Spain’s Ministerio de Educación y Ciencia (Rodríguez Mora).Publicad