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 book follows the methodologies of complex adaptive systems research in their application to addressing the problems of terrorism, specifically terrorist networks, their structure and various methods of mapping and interdicting them as well as exploring the complex landscape of network-centric and irregular warfare. A variety of new models and approaches are presented here, including Dynamic Network Analysis, DIME/PMESII models, percolation models and emergent models of insurgency. In addition, the analysis is informed by practical experience, with analytical and policy guidance from authors who have served within the U.S. Department of Defense, the British Ministry of Defence as well as those who have served in a civilian capacity as advisors on terrorism and counter-terrorism.

Conflict and Complexity: Countering Terrorism, Insurgency, Ethnic and Regional Violence

The amount of news delivered by the different media in the current environment can be overwhelming. Although the events being reported are factually the same, the ways with which the news is delivered vary with the media sources involved. In many cases, it is difficult to reliably uncover the latent information hidden within the news reports due to the great diversity of topics and the sheer volume of news. Analysis of the news media has always been of interest to news analysts, politicians and policy makers in order to aggregate and make sense of the information generated every day. News sources try to achieve relevance to their audiences by providing them with news that the audience wants or finds interesting, but often also have implicit motives such as shaping the perceptions of their audience. Although these agendas or target audiences are not explicitly identified, we consider ways in which this information can be inferred by applying the tools of natural language processing and semantic analysis to the news streams from these sources.

Semantic knowledge inference from online news media using an LDA-NLP approach

The self-organization of social networks and the emergence of ideas have both been studied extensively in recent years, but seldom in a single framework. In this paper, we describe a distributed multi-agent model for the self-organization of social networks from encounters between agents with specific ideas, which are seen as combinations of words. Each agent maintains a semantic network of the words it knows, which implicitly defines the ideas in its repertoire. Agents exchange their ideas over their social networks, and incorporate the received ideas in their semantic networks. Social bonds are made and broken based on the agents' social and semantic preferences (i.e., shared ideas), leading to the emergence of social communities. Thus, the model embodies a circular interaction between the formation of social networks and new ideas. We mine the resulting communities for novel ideas that are generated by their members, and look at the effect of interaction choices on their formation.

A multi-agent model for the co-evolution of ideas and communities

Latent attractor networks are recurrent neural networks with weak embedded attractors. The attractors bias the network's response to external inputs without becoming fully manifested themselves. Latent attractor networks have been used to model context-dependent spatial representations in the hippocampus, and to encode context-dependent stimuli in neural networks. In the current model, the selection of the biasing attractor occurred in response to an initial triggering stimulus indicating the context signal. For example, the sign on a door may set the context for the representation of a room. However, in many realistic situations, context is set by a set of cues rather than a single cue, and these cues are typically seen sequentially, though not in a particular order. The problem addressed here is: how can a latent attractor network progressively select an attractor in response to a sequence of context patterns?.

Progressive attractor selection in latent attractor networks

The paper presents a neural model for learning sequences of relevant patterns embedded in distractors. A contextual episode is a sequence of relevant patterns - always in the same order - intermixed with distractors. By repeated presentations of all contextual episodes, the model discovers for each episode the set of relevant patterns and their order. The problem is solved in two stages: (a) by eliminating distractors, and (b) by learning the order between relevant patterns. The model uses the concept of latent attractors - essential in creating different neural representations for same patterns in distinct episodes. No external teacher and only Hebbian type learning rules are used.

Ali A. Minai

Papers

Conflict and Complexity: Countering Terrorism, Insurgency, Ethnic and Regional Violence

Semantic knowledge inference from online news media using an LDA-NLP approach

A multi-agent model for the co-evolution of ideas and communities

Progressive attractor selection in latent attractor networks

Using latent attractors to discern temporal order