Dataset Information

A Bayesian context fear learning algorithm/automaton.

ABSTRACT: Contextual fear conditioning is thought to involve the synaptic plasticity-dependent establishment in hippocampus of representations of to-be-conditioned contexts which can then become associated with USs in the amygdala. A conceptual and computational model of this process is proposed in which contextual attributes are assumed to be sampled serially and randomly during contextual exposures. Given this assumption, moment-to-moment information about such attributes will often be quite different from one exposure to another and, in particular, between exposures during which representations are created, exposures during which conditioning occurs, and during recall sessions. This presents challenges to current conceptual models of hippocampal function. In order to meet these challenges, our model's hippocampus was made to operate in different modes during representation creation and recall, and non-hippocampal machinery was constructed that controlled these hippocampal modes. This machinery uses a comparison between contextual information currently observed and information associated with existing hippocampal representations of familiar contexts to compute the Bayesian Weight of Evidence that the current context is (or is not) a known one, and it uses this value to assess the appropriateness of creation or recall modes. The model predicts a number of known phenomena such as the immediate shock deficit, spurious fear conditioning to contexts that are absent but similar to actually present ones, and modulation of conditioning by pre-familiarization with contexts. It also predicts a number of as yet unknown phenomena.

SUBMITTER: Krasne FB

PROVIDER: S-EPMC4445248 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Bayesian context fear learning algorithm/automaton.

Krasne Franklin B FB Cushman Jesse D JD Fanselow Michael S MS

Frontiers in behavioral neuroscience 20150527

Contextual fear conditioning is thought to involve the synaptic plasticity-dependent establishment in hippocampus of representations of to-be-conditioned contexts which can then become associated with USs in the amygdala. A conceptual and computational model of this process is proposed in which contextual attributes are assumed to be sampled serially and randomly during contextual exposures. Given this assumption, moment-to-moment information about such attributes will often be quite different f ...[more]

PMID: 26074792

Similar Datasets

Project description:The extinction of contextual fear is commonly an essential requirement for successful exposure therapy for fear disorders. However, experimental work on extinction of contextual fear is limited, and there little or no directly relevant theoretical work. Here, we extend BACON, a neurocomputational model of context fear conditioning that provides plausible explanations for a number of aspects of context fear conditioning, to deal with extinction (calling the model BaconX). In this model, contextual representations are formed in the hippocampus and association of fear to them occurs in the amygdala. Representation creation, conditionability, and development of between-session extinction are controlled by degree of confidence (assessed by the Bayesian weight of evidence) that an active contextual representation is in fact that of the current context (i.e., is "valid"). The model predicts that: (1) extinction which persists between sessions will occur only if at a sessions end there is high confidence that the active representation is valid. It follows that the shorter the context placement-to-US (shock) interval ("PSI") and the less is therefore learned about context, the longer extinction sessions must be for enduring extinction to occur, while too short PSIs will preclude successful extinction. (2) Short-PSI deficits can be rescued by contextual exposure even after conditioning has occurred. (3) Learning to discriminate well between a conditioned and similar safe context requires representations of each to form, which may not occur if PSI was too short. (4) Extinction-causing inhibition must be applied downstream of the conditioning locus for reasonable generalization properties to be generated. (5) Context change tends to cause return of extinguished contextual fear. (6). Extinction carried out in the conditioning context generalizes better than extinction executed in contexts to which fear has generalized (as done in exposure therapy). (7) BaconX suggests novel approaches to exposure therapy.

Project description:With the rapid development of artificial intelligence and data science, Dynamic Bayesian Network (DBN), as an effective probabilistic graphical model, has been widely used in many engineering fields. And swarm intelligence algorithm is an optimization algorithm based on natural selection with the characteristics of distributed, self-organization and robustness. By applying the high-performance swarm intelligence algorithm to DBN structure learning, we can fully utilize the algorithm's global search capability to effectively process time-based data, improve the efficiency of network generation and the accuracy of network structure. This study proposes an improved bacterial foraging optimization algorithm (IBFO-A) to solve the problems of random step size, limited group communication, and the inability to maintain a balance between global and local searching. The IBFO-A algorithm framework comprises four layers. First, population initialization is achieved using a logistics-sine chaotic mapping strategy as the basis for global optimization. Second, the activity strategy of a colony foraging trend is constructed by combining the exploration phase of the Osprey optimization algorithm. Subsequently, the strategy of bacterial colony propagation is improved using a "genetic" approach and the Multi-point crossover operator. Finally, the elimination-dispersal activity strategy is employed to escape the local optimal solution. To solve the problem of complex DBN learning structures due to the introduction of time information, a DBN structure learning method called IBFO-D, which is based on the IBFO-A algorithm framework, is proposed. IBFO-D determines the edge direction of the structure by combining the dynamic K2 scoring function, the designed V-structure orientation rule, and the trend activity strategy. Then, according to the improved reproductive activity strategy, the concept of "survival of the fittest" is applied to the network candidate solution while maintaining species diversity. Finally, the global optimal network structure with the highest score is obtained based on the elimination-dispersal activity strategy. Multiple tests and comparison experiments were conducted on 10 sets of benchmark test functions, two non-temporal and temporal data types, and six data samples of two benchmark 2T-BN networks to evaluate and analyze the optimization performance and structure learning ability of the proposed algorithm under various data types. The experimental results demonstrated that IBFO-A exhibits good convergence, stability, and accuracy, whereas IBFO-D is an effective approach for learning DBN structures from data and has practical value for engineering applications.

Dataset Information

A Bayesian context fear learning algorithm/automaton.

Publications

A Bayesian context fear learning algorithm/automaton.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets