Associative Memory, Gestalt, Dendrite Tree

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

Associative Memory, Gestalt, Dendrite Tree

All Memory Is Associative

Memories are necessarily associative and never identical. (Edelman; Wider than the Sky, 53)

By the associative property of memory, neural assemblies aggregate via the laws of Gestalts into the sparse but widespread neural assemblies of the dynamic core of consciousness.

The operation of association involves the linkage of information with other information. (Anderson; Associative Networks, 102)

The key ingredient in the cortical globalization process is the ability of the oscillatory mechanisms to recruit anatomically distant cortical neurons into temporal coalitions. (Buzsáki; Rhythms of the Brain, 185)

Association is the most natural form of neural network computation. (Anderson; Associative Networks, 102)

The ability of autoassociative systems to reconstruct missing or noisy parts of the learned patterns. (Anderson; Associative Networks, 105)

Research study — Hippocampus Association Memory for Decisions — hippocampus can complete the pattern and automatically reactivate the neural representation of other items, allowing the integration of old memories with new ones.

Gestalts and Consciousness

Most scholars emphasize how the collective Gestalt-like traits of the brain and its networks are critical to understanding consciousness. (Koch; Quest for Consciousness, 311)

The figurative smoothing out of memory is strikingly similar to a literal smoothing out that Gestalt psychologists in the 1920s had noticed in studies of people's memory for geometric shapes. (Mlodinow; Subliminal, 69)

Consciousness is an emergent property of nonspecialized and divergent groups of neurons that is continuously variable with respect to, and always entailing, a stimulus epicenter. (Greenfield; Centers of Mind, 104)

Brain supplies the missing information. (Pinker; How the Mind Works, 28)

The ability of autoassociative systems to reconstruct missing or noisy parts of the learned patterns. (Anderson; Associative Networks, 105)

Neural Network — Dendritic Trees

Average firing rate of a neuron varies and represents some aspect of the information signal.

Information appears to be coded within single neurons only by the average rate of firing [over a time window interval of perhaps 20 ms] and not by the precise composition of the intervals between spikes. (Motter; Neurophysiology, 52)

The firing rate of neurons conveys information in the neural network.

Research study — Dendritic Spines nonlinear processing enhances neuronal computation

Neural Network — Stochastic Behavior

Almost Poisson nature of the spike trains of single neurons found in most brain areas. (Rolls; Memory, Attention, and Decision-Making, 72)

The variability in interspike intervals observed for many neurons in various locations in the nervous system is consistent with the presence of a random Poisson process. (Motter; Neurophysiology, 52)

Spike trains are essentially Poisson-like because the cell potential hovers noisily close to the threshold for firing, the noise being generated in part by the Poisson-like firing of the other neurons in the network. (Rolls & Deco; Noisy Brain, 78)

Noise and spontaneous firing help to ensure that when a stimulus arrives, there are always some neurons very close to threshold that respond rapidly, and then communicate their firing to other neurons through the modified synaptic weights, so that an attractor process can take place very rapidly. (Rolls & Deco; Noisy Brain, 78)

Noise inherent in brain activity has a number of advantages by making the dynamics stochastic, which allows for many remarkable features of the brain, including creativity, probabilistic decision making, stochastic resonance, unpredictability, conflict resolution, symmetry breaking, allocation to discrete categories, and many of the important memory properties. (Rolls & Deco; Noisy Brain, 80)

Neural Network — Populations of Neurons

Because single neurons have small and uncertain effects on other neurons, the cortical description must be carried out in terms of neuronal populations rather than at the level of individual cells. (Stevens; Cortical Theory, 242)