Scientific Understanding of Consciousness
Consciousness as an Emergent Property of Thalamocortical Activity

Modeling the Brain Functionality of Consciousness

Neurocomputational models enable the single neuron level of analysis to be linked to the level of large-scale neuronal networks and the interactions between them, so that large-scale processes such as memory retrieval, object recognition, attention, and decision-making can be understood. (Rolls; Memory, Attention, and Decision-Making, 3)

Neural network models are needed in order to provide a basis for understanding the processing and memory functions performed by neuronal networks in the brain. (Rolls; Memory, Attention, and Decision-Making, 543)

Neural network simulation of biologically plausible pattern association memories such as may be present in orbitofrontal cortex and amygdala and autoassociation or attractor networks. (Rolls; Emotion Explained, 454)

Biological Models of Brain Functionality

Much of the most exciting current progress in cognitive science combines experimental studies of the brain with computational models of how it works. (Thagard; Brain and the Meaning of Life, 63)

Eric Kandel won a Nobel Prize for his research on how learning works in the sea slug, Aplysia. (Thagard; Brain and the Meaning of Life, 49)

Link to — Consciousness-Models Diagrams

Consciousness

Edelman’s Primary (Core) Consciousness Diagram

Edelman’s Primary Consciousness Model

Building Blocks of Consciousness

Wakeful Consciousness Information Flow

Self

Three Stages of the Self

Neural Network Models

Brain Stem Nuclei Involved in Homeostasis

Convergence-Divergence Zones Architecture

Functionality Diagram of Damasio’s Convergence-Divergence Architecture

Hierarchical Organization Diagram

Cortical Layers Diagram

With the help of the neural network model we can more clearly described the computations that the circuit structure might exert upon thalamic-column interactions, and thereby begin to understand how the entire thalamic circuit might function as a mechanism of attention. (LaBerge; Attentional Processing, 179)

Speed of Processing of a Four Layer Hierarchical Network with Integrate-and-Fire Attractor Dynamics in Each Layer

The visual system has a whole series of cortical areas organized predominantly hierarchically (e.g. V1 to V2 to V4 to.inferior temporal cortex); the rapid information processing that can be performed for object recognition is predominantly feedforward. (Rolls; Memory, Attention, and Decision-Making, 616)

An analysis of response latencies indicates that there is sufficient time for only 10-20 ms per processing stage in the visual system. (Rolls; Memory, Attention, and Decision-Making, 616)

Memory

Computational models have assisted our understanding of the neural bases of learning and memory. (Gluck & Myers; Gateway to Memory, 350)

Covering a range of models from a variety of researchers, makes it possible for different models to capture different aspects of anatomy and physiology and different kinds of behavior, and in many cases these models complement each other. (Gluck & Myers; Gateway to Memory, 350)

Short-Term Memory

There are a number of different short-term memory systems, each implemented in a different cortical area. Short-term memory may be implemented by subpopulations of neurons that show maintained activity while the stimulus or event is being remembered. These memories may operate as autoassociative attractor networks. The autoassociation could be implemented by associatively modifiable synapses between connected pyramidal cells within an area, or by the forward and backward connections between adjacent cortical areas in a hierarchy. (Rolls; Memory, Attention, and Decision-Making, 375)

There are many at least partially independent modules are short-term memory functions in prefrontal cortex. (Rolls; Memory, Attention, and Decision-Making, 383)

Autoassociation or Attractor Memory

Autoassociative memories or attractor neural networks, store memories, each one of which is represented by a pattern of neural activity. They memories are stored in the recurrent synaptic connections of the neurons of the network. (Rolls; Memory, Attention, and Decision-Making, 360)

And autoassociation memory can be used as a short-term memory, in which iterative processing around the recurrent collateral connection loop keeps a representation active by continuing neuronal firing. (Rolls; Memory, Attention, and Decision-Making, 360)

Autoassociation Diagram

Model of Working Memory