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

In many cognitive situations, context is specified globally-encompassing the entire duration of an episode, but explicitly identified only at its initiation. Examples of such contexts are social situations, spatial environments, task specifications, etc. In many such cases, a neural system needs to respond differently to the same immediate stimulus depending on the global context. Clearly, the system must be able to switch into different response regimes and remain there while the context remains fixed. Simultaneously, it must remain sensitive to external stimuli, and respond to them in a reliable and informative manner. We propose a neurally plausible approach for handling these requirements.

Generating smooth context-dependent neural representations

Unifying Themes in Complex Systems VII

http://www.ece.uc.edu/~aminai/papers/doboli_neurocomp01.pdf

An attractor model for hippocampal place cell hysteresis

Synchronizing multiple chaotic maps with a randomized scalar coupling

Humans often engage in tasks that require or are made more efficient by coordinating with other humans. The coordination involved in these tasks can be understood in terms of the behavioral and affordance dynamics of socially embedded agents engaged in joint action activities. Behavioral dynamics provide mathematical (differential equation) models of human behavior and interaction and affordance dynamics identify and model the ways that an agent's action capabilities evolve over time. Taken together, models of human joint-action based on these approaches may provide a basis for developing robust, natural, and easy to engage artificial agents. In this paper we introduce behavioral and affordance dynamics models of human joint action in a pick-and-place task. Based on these models we provide a proof of concept pick-and-place artificial agent and implement the agent in a 3D virtual environment to interact with human co-actors.

https://dl.acm.org/doi/pdf/10.1145/3125739.3125771

Ali A. Minai

Papers

Generating smooth context-dependent neural representations

Unifying Themes in Complex Systems VII

An attractor model for hippocampal place cell hysteresis

Synchronizing multiple chaotic maps with a randomized scalar coupling

PAPAc: A Pick and Place Agent Based on Human Behavioral Dynamics