396 research outputs found
Structural Plasticity and Associative Memory in Balanced Neural Networks With Spike-Time Dependent Inhibitory Plasticity
Several homeostatic mechanisms enable the brain to maintain desired
levels of neuronal activity. One of these, homeostatic structural plasticity,
has been reported to restore activity in networks disrupted
by peripheral lesions by altering their neuronal connectivity. While
multiple lesion experiments have studied the changes in neurite morphology
that underlie modifications of synapses in these networks,
the underlying mechanisms that drive these changes and the effects of
the altered connectivity on network function are yet to be explained.
Experimental evidence suggests that neuronal activity modulates
neurite morphology and that it may stimulate neurites to selectively
sprout or retract to restore network activity levels. In this study, a new
spiking network model was developed to investigate these activity
dependent growth regimes of neurites. Simulations of the model accurately
reproduce network rewiring after peripheral lesions as reported
in experiments. To ensure that these simulations closely resembled
the behaviour of networks in the brain, a biologically realistic network
model that exhibits low frequency Asynchronous Irregular (AI) activity
as observed in cerebral cortex was deafferented. Furthermore, to
study the functional effects of peripheral lesioning and subsequent
network repair by homeostatic structural plasticity, associative memories
were stored in the network and their recall performances before
deafferentation and after, during the repair process, were compared.
The simulation results indicate that the re-establishment of activity
in neurons both within and outside the deprived region, the Lesion
Projection Zone (LPZ), requires opposite activity dependent growth
rules for excitatory and inhibitory post-synaptic elements. Analysis of
these growth regimes indicates that they also contribute to the maintenance
of activity levels in individual neurons. In this model, the
directional formation of synapses that is observed in experiments requires
that pre-synaptic excitatory and inhibitory elements also follow
opposite growth rules. Furthermore, it was observed that the proposed
model of homeostatic structural plasticity and the inhibitory synaptic
plasticity mechanism that also balances the AI network are both
necessary for successful rewiring. Next, even though average activity
was restored to deprived neurons, these neurons did not retain their
AI firing characteristics after repair. Finally, the recall performance of
associative memories, which deteriorated after deafferentation, was
not restored after network reorganisation
Finding Optimal Strategies in a Multi-Period Multi-Leader-Follower Stackelberg Game Using an Evolutionary Algorithm
Stackelberg games are a classic example of bilevel optimization problems,
which are often encountered in game theory and economics. These are complex
problems with a hierarchical structure, where one optimization task is nested
within the other. Despite a number of studies on handling bilevel optimization
problems, these problems still remain a challenging territory, and existing
methodologies are able to handle only simple problems with few variables under
assumptions of continuity and differentiability. In this paper, we consider a
special case of a multi-period multi-leader-follower Stackelberg competition
model with non-linear cost and demand functions and discrete production
variables. The model has potential applications, for instance in aircraft
manufacturing industry, which is an oligopoly where a few giant firms enjoy a
tremendous commitment power over the other smaller players. We solve cases with
different number of leaders and followers, and show how the entrance or exit of
a player affects the profits of the other players. In the presence of various
model complexities, we use a computationally intensive nested evolutionary
strategy to find an optimal solution for the model. The strategy is evaluated
on a test-suite of bilevel problems, and it has been shown that the method is
successful in handling difficult bilevel problems.Comment: To be published in Computers and Operations Researc
Positive Disruptions Caused by SCRM Activities in the SECI process of Knowledge Creation: Insights from Four Case Studies
Web 2.0 has been in the foray for a while playing an important role in threading business processes, various departments, systems and key stakeholders (within firms) to activate customer participation and involvement. In order to re-emphasize customer centricity, firms have been using SCRM (Social Customer Relationship Management) approach as a part of their CRM (Customer Relationship Management) strategy. The activities under SCRM are a major source for organizational knowledge creation that occurs due to a continuous dialogue between tacit and explicit knowledge. Also, various social platforms (operating for SCRM) where collaboration takes place acts as a shared context for knowledge creation. To comprehend the actions and limitations of a knowledge-creating firm thoroughly, this research paper examines the process of knowledge-creation by (1) revisiting Nonaka-Takeuchi SECI (Socialization, Externalization, Combination & Internalization) process to recognize how SCRM activities can be prolific in organizational knowledge creation (2) exploring positive disruptions created by integrating SCRM activities with four modes of SECI process for additional knowledge creation (3) analyzing case studies of four firms from consumer products sector that use SCRM approach and (4) discovering the elements under SCRM approach that satisfy ‘BA’ as a shared context
Solving bilevel multi-objective optimization problems using evolutionary algorithms
Bilevel optimization problems require every feasible upper-level solution to satisfy optimality of a lower-level optimization problem. These problems commonly appear in many practical problem solving tasks including optimal control, process optimization, game-playing strategy development, transportation problems, and others. In the context of a bilevel single objective problem, there exists a number of theoretical, numerical, and evolutionary optimization results. However, there does not exist too many studies in the context of having multiple objectives in each level of a bilevel optimization problem. In this paper, we address bilevel multi-objective optimization issues and propose a viable algorithm based on evolutionary multi-objective optimization (EMO) principles. Proof-of-principle simulation results bring out the challenges in solving such problems and demonstrate the viability of the proposed EMO technique for solving such problems. This paper scratches the surface of EMO-based solution methodologies for bilevel multi-objective optimization problems and should motivate other EMO researchers to engage more into this important optimization task of practical importance
Progressively interactive evolutionary multiobjective optimization
A complete optimization procedure for a multi-objective problem essentially comprises of search and decision making. Depending upon how the search and decision making task is integrated, algorithms can be classified into various categories. Following `a decision making after search' approach, which is common with evolutionary multi-objective optimization algorithms, requires to produce all the possible alternatives before a decision can be taken. This, with the intricacies involved in producing the entire Pareto-front, is not a wise approach for high objective problems. Rather, for such kind of problems, the most preferred point on the front should be the target. In this study we propose and evaluate algorithms where search and decision making tasks work in tandem and the most preferred solution is the outcome. For the two tasks to work simultaneously, an interaction of the decision maker with the algorithm is necessary, therefore, preference information from the decision maker is accepted periodically by the algorithm and progress towards the most preferred point is made.
Two different progressively interactive procedures have been suggested in the dissertation which can be integrated with any existing evolutionary multi-objective optimization algorithm to improve its effectiveness in handling high objective problems by making it capable to accept preference information at the intermediate steps of the algorithm. A number of high objective un-constrained as well as constrained problems have been successfully solved using the procedures. One of the less explored and difficult domains, i.e., bilevel multiobjective optimization has also been targeted and a solution methodology has been proposed. Initially, the bilevel multi-objective optimization problem has been solved by developing a hybrid bilevel evolutionary multi-objective optimization algorithm. Thereafter, the progressively interactive procedure has been incorporated in the algorithm leading to an increased accuracy and savings in computational cost. The efficacy of using a progressively interactive approach for solving difficult multi-objective problems has, therefore, further been justifie
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