https://www2.econ.iastate.edu/tesfatsi/DeepLearningInNeuralNetworksOverview.JSchmidhuber2015.pdf

Deep learning in neural networks

▪ Abstract The prefrontal cortex has long been suspected to play an important role in cognitive control, in the ability to orchestrate thought and action in accordance with internal goals. Its neural basis, however, has remained a mystery. Here, we propose that cognitive control stems from the active maintenance of patterns of activity in the prefrontal cortex that represent goals and the means to achieve them. They provide bias signals to other brain structures whose net effect is to guide the flow of activity along neural pathways that establish the proper mappings between inputs, internal states, and outputs needed to perform a given task. We review neurophysiological, neurobiological, neuroimaging, and computational studies that support this theory and discuss its implications as well as further issues to be addressed

/pdf/an-integrative-theory-of-prefrontal-cortex-function-2j9unprn5f.pdf

An integrative theory of prefrontal cortex function

http://www.maths.qmul.ac.uk/%7Elatora/report_06.pdf

Complex networks: Structure and dynamics

Damage to the hippocampal system disrupts recent memory but leaves remote memory intact. The account presented here suggests that memories are first stored via synaptic changes in the hippocampal system, that these changes support reinstatement of recent memories in the neocortex, that neocortical synapses change a little on each reinstatement, and that remote memory is based on accumulated neocortical changes. Models that learn via changes to connections help explain this organization. These models discover the structure in ensembles of items if learning of each item is gradual and interleaved with learning about other items. This suggests that the neocortex learns slowly to discover the structure in ensembles of experiences. The hippocampal system permits rapid learning of new items without disrupting this structure, and reinstatement of new memories interleaves them with others to integrate them into structured neocortical memory systems.

Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory.

The theory of affordances

This paper addresses the map building problem for cooperative search by a team of uninhabited air vehicles (UAVs) operating in an unknown and uncertain environment . We present and compare two evidential map-building approaches based on Bayesian theory and Dempster-Shafer theory respectively. We illustrate how to utilize the generated maps into the UAVs path planning procedure so that they could cooperatively localize targets in the environment. The simulation results illustrate the effectiveness of the proposed strategies.

/pdf/evidential-map-building-approaches-for-multi-uav-cooperative-1l0o6aqmtd.pdf

Evidential map-building approaches for multi-UAV cooperative search

http://www.ece.uc.edu/~aminai/papers/marupaka_creativity_NN12.pdf

2012 Special Issue: Connectivity and thought: The influence of semantic network structure in a neurodynamical model of thinking

Many creative activities take place in a group context, whether in short-term meetings, work teams, or by means of electronic interaction. The group creative process necessarily involves the exchan...

Modeling Ideational Creativity in Groups: Connecting Cognitive, Neural, and Computational Approaches

Proposed a decomposed evolutionary algorithm for learning fuzzy cognitive map models.Applied the proposed method to infer causal networks from gene expression time series.Learned 300-node fuzzy cognitive maps while less than 40 nodes are common in the literature.Compared four stochastic optimization methods for learning fuzzy cognitive maps. Fuzzy cognitive maps have been widely used as abstract models for complex networks. Traditional ways to construct fuzzy cognitive maps rely on domain knowledge. In this paper, we propose to use fuzzy cognitive map learning algorithms to discover domain knowledge in the form of causal networks from data. More specifically, we propose to infer gene regulatory networks from gene expression data. Furthermore, a new efficient fuzzy cognitive map learning algorithm based on a decomposed genetic algorithm is developed to learn large scale networks. In the proposed algorithm, the simulation error is used as the objective function, while the model error is expected to be minimized. Experiments are performed to explore the feasibility of this approach. The high accuracy of the generated models and the approximate correlation between simulation errors and model errors suggest that it is possible to discover causal networks using fuzzy cognitive map learning. We also compared the proposed algorithm with ant colony optimization, differential evolution, and particle swarm optimization in a decomposed framework. Comparison results reveal the advantage of the decomposed genetic algorithm on datasets with small data volumes, large network scales, or the presence of noise.

/pdf/inferring-causal-networks-using-fuzzy-cognitive-maps-and-1pgka1vg3g.pdf

Inferring causal networks using fuzzy cognitive maps and evolutionary algorithms with application to gene regulatory network reconstruction

Uninhabited autonomous vehicles (UAVs) have many useful military applications, including reconnaissance, search-and-destroy, and search-and-rescue missions in hazardous environments such as battlefields or disaster areas. Recently, there has been considerable interest in the possibility of using large teams (swarms) of UAVs functioning cooperatively to accomplish a large number of tasks (e.g., finding and attacking targets). However, this requires the assignment of multiple spatially distributed tasks to each UAV along with a feasible path that minimizes effort and avoids threats.

Ali A. Minai

Papers

Evidential map-building approaches for multi-UAV cooperative search

2012 Special Issue: Connectivity and thought: The influence of semantic network structure in a neurodynamical model of thinking

Modeling Ideational Creativity in Groups: Connecting Cognitive, Neural, and Computational Approaches

Inferring causal networks using fuzzy cognitive maps and evolutionary algorithms with application to gene regulatory network reconstruction

Multi-Target Assignment and Path Planning for Groups of UAVs