neuroscience

Animals are usually considered to behave as complex automata, responding predictably to external stimuli. This study suggests otherwise, showing that even the humble fruit fly can behave spontaneously. The flight paths of flies in a completely featureless environment were neither random nor predictable, but followed a complicated fractal pattern generated within the fly's brain.

http://nature-wonder.livejournal.com/32718.html

http://ethology.ru/interview/?id=143

http://www.neuroscience.ru/index.php?option=com_content&task=view&id=313&Itemid=100

Люди не только предсказывают будущее ("если я разожму пальцы, чашка упадет и разобьется"), они еще и выделяют абстракции и преобразуют их цепочки согласно определенным правилам. Интеллект первого типа может плавно меняться, он есть у большей части животных, понятно, как его моделировать. Интеллект второго типа крайне редок в животном мире (любые его наблюдения даже у приматов уже привлекают к себе огромное внимание). Как его связать с интеллектом первого типа, можно ли его выразить в терминах "foretelling device", и если да, то как, как он смог появиться эволюционным путем - все это, насколько понимаю, неясно.

Computational Neuroscience / NeuroInformatics

Neural Computation Magazine: http://neco.mitpress.org

http://www.hirnforschung.net/cneuro/

Synchronization of pulse neural networks: http://brain.cc.kogakuin.ac.jp/~kanamaru/Chaos/e

http://brain.cc.kogakuin.ac.jp/~kanamaru/research/publications.html

Modeling brain activity: http://ralyx.inria.fr/2006/Raweb/odyssee/id2748919.html#id2748919

http://sulcus.berkeley.edu The Freeman Laboratory for Nonlinear Neurodynamics

http://en.wikipedia.org/wiki/Computational_neuroscience

http://www.scholarpedia.org/article/Category:Computational_neuroscience

http://www.scholarpedia.org/article/Encyclopedia_of_computational_neuroscience

http://www.nest-initiative.org/index.php/Main_Page

Hawkins. On Intelligence

Hawkins' basic idea is that the brain is a mechanism to predict the future, specifically, hierarchical regions of the brain predict their future input sequences. Perhaps not always far in the future, but far enough to be of real use to an organism. As such, the brain is a feed forward hierarchical state machine with special properties that enable it to learn.

The state machine actually controls the behavior of the organism. Since it is a feed forward state machine, the organism responds to future events predicted from past data.

The hierarchy is capable of memorizing frequently observed sequences of patterns and developing invariant representations. Higher levels of the cortical hierarchy predict the future on a longer time scale, or over a wider range of sensory input. Lower levels interpret or control limited domains of experience, or sensory or effector systems. Connections from the higher level states predispose some selected transitions in the lower-level state machines.

Hebbian learning is part of the framework, in which the event of learning physically alters neurons and connections, as learning takes place.

Vernon Mountcastle's formulation of a cortical column is a basic element in the framework. Hawkins places particular emphasis on the role of the interconnections from peer columns, and the activation of columns as a whole. He strongly implies that a column is the cortex's physical representation of a state in a state machine.

As an engineer, any specific failure to find a natural occurrence of some process in his framework does not signal a fault in the memory-prediction framework per se, but merely signals that the natural process has performed Hawkins' functional decomposition in a different, unexpected way, as Hawkins' motivation is to create intelligent machines. For example, for the purposes of his framework, the nerve impulses can be taken to form a temporal sequence (but phase encoding could be a possible implementation of such a sequence; these details are immaterial for the framework).

11 Testable Predictions:

Enhanced neural activity in anticipation of a sensory event

1. In all areas of cortex, Hawkins (2004) predicts we should find anticipatory cells, cells that fire in anticipation of a sensory event.

As of 2005 mirror neurons have been observed to fire before an anticipated event.

Spacially specific prediction

2. In primary sensory cortex, Hawkins predicts, for example, we should find anticipatory cells in or near V1, at a precise location in the visual field (the scene). It has been experimentally determined, for example, after mapping the angular position of some objects in the visual field, there will be a one-to-one correspondence of cells in the scene to the angular positions of those objects. Hawkins predicts that when the features of a visual scene are known in a memory, anticipatory cells should fire before the actual objects are seen in the scene.

Prediction should stop propagating in the cortical column at layers 2 and 3

3. In layers 2 and 3, predictive activity (neural firing) should stop propagating at specific cells, corresponding to a specific prediction. Hawkins does not rule out anticipatory cells in layers 4 and 5.

"Name cells" at layers 2 and 3 should preferentially connect to layer 6 cells of cortex

4. Learned sequences of firings comprise a representation of temporally constant invariants. Hawkins calls the cells which fire in this sequence "name cells". Hawkins suggests that these name cells are in layer 2, physically adjacent to layer 1. Hawkins does not rule out the existence of layer 3 cells with dendrites in layer 1, which might perform as name cells.

"Name cells" should remain ON during a learned sequence

5. By definition, a temporally constant invariant will be active during a learned sequence. Hawkins posits that these cells will remain active for the duration of the learned sequence, even if the remainder of the cortical column is shifting state. Since we do not know the encoding of the sequence, we do not yet know the definition of ON or active; Hawkins suggests that the ON pattern may be as simple as a simultaneous AND (i.e., the name cells simultaneously "light up") across an array of name cells.

"Exception cells" should remain OFF during a learned sequence

6. Hawkins' novel prediction is that certain cells are inhibited during a learned sequence. A class of cells in layers 2 and 3 should NOT fire during a learned sequence, the axons of these "exception cells" should fire only if a local prediction is failing. This prevents flooding the brain with the usual sensations, leaving only exceptions for post-processing.

"Exception cells" should propagate unanticipated events

7. If an unusual event occurs (the learned sequence fails), the "exception cells" should fire, propagating up the cortical hierarchy to the hippocampus, the repository of new memories.

"Aha! cells" should trigger predictive activity

8. Hawkins predicts a cascade of predictions, when recognition occurs, propagating downward the column (with each cascade of the eye over a learned scene, for example).

Pyramidal cells should detect coincidences of synaptic activity on thin dendrites

9. Pyramidal cells should be capable of detecting coincident events on thin dendrites, even for a neuron with thousands of synapses. Hawkins posits a temporal window (presuming time-encoded firing) which is necessary for his theory to remain viable.

Learned representations move down the cortical hierarchy, with training

10. Hawkins posits, for example, that if the inferotemporal (IT) layer has learned a sequence, that eventually cells in V4 will also learn the sequence.

"Name cells" exist in all regions of cortex

11. Hawkins predicts that "Name cells" will be found in all regions of cortex.

how signals are conducted within neurons

http://en.wikipedia.org/wiki/Biological_neuron_models

http://en.wikipedia.org/wiki/Soliton_model

SELF AWARENESS: THE LAST FRONTIER By V.S. Ramachandran

http://www.edge.org/q2009/q09_index.html

http://www.edge.org/documents/archive/edge270.html#rama

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