" ...we managed to explicitly dissociate reinforcement learning from Hebbian learning and demonstrated covert learning inside the basal ganglia. These results suggest that a behavioral decision results from both the cooperation (acquisition) and competition (expression) of two distinct but entangled memory systems, the goal-directed system and the habit system that may represent the two ends of the same graded phenomenon."
" ...we managed to explicitly dissociate reinforcement learning from Hebbian learning and demonstrated covert learning inside the basal ganglia. These results suggest that a behavioral decision results from both the cooperation (acquisition) and competition (expression) of two distinct but entangled memory systems, the goal-directed system and the habit system that may represent the two ends of the same graded phenomenon."
Computational approaches for mapping the human connectomeCameron Craddock
Describes open challenges and ongoing work for mapping the human functional connectome and identifying inter-individual variation in the connectome that maps to phenotype and clinical outcomes. Also describes open science initiatives to help scientists from disparate backgrounds to become involved in this research.
Positron-emission tomography studies of cross-modality inhibition in selectiv...Dr Brendan O'Sullivan
Published in 1994, this groundbreaking paper featured the research of Professor Per Roland, Professor Ryuta Kawashima (of “Brain Training” fame) and Professor Brendan O’ Sullivan.
This landmark research was the first to prove in human brain imaging studies that visual attention is impaired when we do other attention-competing tasks such as manual tasks such as using mobile phones while driving.
Does Event Related Desynchronization reveal anticipatory attention in the som...Onno Romijn
Attention that is directed at an upcoming stimulus is termed anticipatory attention. The extended thalamocortical gating model (Brunia, 1999) addresses the processes underlying anticipatory attention. According to this model, both the thalamic relay (TCR) nuclei and the reticular nucleus (RN) are involved in the selection (i.e. gating) of the relevant sensory modality. The TCR nuclei can fire in two modes. The tonic mode is associated with the transmission of afferent and subcortical input to the cortex and leads to desynchronization of 10 Hz rhythmic activity in the cortical projection area of the TCR nucleus. The burst mode is associated with a disruption in this transmission and results in the occurrence of 10 Hz rhythmic activity at the cortical projection area. This implies that event-related changes in 10 Hz activity in the scalp recorded EEG may give insight into the firing mode of the TCR nuclei and thus into the process of anticipatory attention. Event Related Desynchronization (ERD, Pfurtscheller & Aranibar, 1977) can quantify such changes. The extended thalamocortical model states that anticipatory attention is manifest as a prestimulus activation of the sensory cortex corresponding to the modality of the anticipated stimulus. Anticipatory attention to somatosensory stimuli would therefore be manifest as a 10 Hz ERD over the postcentral cortex, whereas anticipatory attention to visual stimuli would be manifest as a 10 Hz ERD over the occipital cortex. To test this hypothesis 9 subjects performed a time-estimation task. They received a Knowledge of Results (KR) stimulus 2 seconds after their manual response. ERD was recorded in the 10 Hz and 20 Hz frequency bands. An occipital ERD was present preceding visual KR stimuli, whereas no significant postcentral ERD was present prior to somatosensory KR stimuli. Nonetheless, the statistical analyses indicated that these differences between conditions were not significant. Therefore, these results do not support the extended thalamocortical gating model. It can be hypothesized that the postcentral ERD preceding somatosensory stimuli is masked by a postmovement Event Related Synchronization (ERS).
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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Week 4 the neural basis of consciousness introduction to the visual systemNao (Naotsugu) Tsuchiya
12-week lecture series on "the neural basis of consciousness" by Prof Nao Tsuchiya.
Given to 3rd year undergraduate level. No prerequisites.
Contents:
1) What are behavioral and neural signatures of nonconscious processing?
2) Can blindsight-like behavior induced in monkeys? What are the evidence?
3) How can we discriminate nonconscious from conscious behaviors using a concept of metacognition?
4) What is the structure of eye and how does it shape our conscious vision?
Toward Tractable AGI: Challenges for System Identification in Neural CircuitryRandal Koene
This is the presentation I gave at AGI-12 (also called the Winter Intelligence 2012 conferece) in Oxford, UK, on Dec.11, 2012. There is an AGI-12 proceedings paper that accompanies this talk. I will make that available on my publications page at http://randalkoene.com and I will put both together on the http://carboncopies.org page about this event. The video (recorded by Adam Ford) should also appear soon.
Abstract. Feasible and practical routes to Artificial General Intelligence involve short-cuts tailored to environments and challenges. A prime example of a system with built-in short-cuts is the human brain. Deriving from the brain the functioning system that implements intelligence and generality at the level of neurophysiology is interesting for many reasons, but also poses a set of specific challenges. Representations and models demand that we pick a constrained set of signals and behaviors of interest. The systematic and iterative process of model building involves what is known as System Identification, which is made feasible by decomposing the overall problem into a collection of smaller System Identification problems. There is a roadmap to tackle that includes structural scanning (a way to obtain the “connectome”) as well as new tools for functional recording. We examine the scale of the endeavor, and the many challenges that remain, as we consider specific approaches to System Identification in neural circuitry.
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Critical Features for Recognition of Biological Motion
1. Critical features for the recognition of
biological motion
Casile & Giese (2005)
Your Name
Your Title
Your Organization (Line #1)
2005-12-31 Your Organization (Line #2)
2. Introduction
Point-light stimuli experiments
Perception of complex biological movements
(Johansson, 1973)
Not impaired
By adding noise
(Cutting et al. 1988)
By changing the contrast polarity of the dots
Ahlström et al.1997)
If only a subset of dots is visible
If the dots are displaced on the skeleton in every frame
Stimulus Perception
2
3. Introduction
Different Hypotheses
Hypothesis 1: Computational mechanisms reconstruct
the missing information from impoverished stimuli by
fitting a skeleton model to the stimuli (dots)
most of the existing algorithms are computationally
expensive and have no obvious neural implementation
Hypothesis 2: Generalization from normal to point-light
stimuli is based on specific features that are shared by
both stimulus classes.
The nature of such features is largely unknown
It has been discussed whether they are based on
form or motion information Motion Form
3
4. Method
Two movies
Stickman walking Moving dots
Stimulus
Optic Flow
4
5. Analysis and Results
Two movies
Stickman walking Moving dots
Stimulus
r=0.09
PCA
Optic Flow
r=0.93
The dominant motion features are very similar for both stimuli, but the
dominant form features are different.
5
7. Motion Information
Experiment 1
Random Dots
1A Arrangement
Asymmetric CFS Stimulus
Written report about their perceptual impression
13/17: “Human walking”
4/17: “jumping dots” or “nothing” direction
1B
Symmetric CFS Stimulus
2/9: “Human Walking”
4/9: “Human performing actions”
3/9 “Nothing” or “The Number 8”
1C
Random dots
2/10: “Human Performing actions”
8/10: “Nothing”
7
8. Experiment 1
Results
Opponent motion seems to be critical for generating the impression of a
walking human. The presence of moving dots within the same four
regions is not sufficient.
Skeleton model hypothesis seems to be wrong
Coarse position information is not sufficient to fit
this model
The random dots' positions do not comply with
the kinematics of a moving human body
Alternative hypothesis: We use fuzzy templates for the human body
shape that fit the CFS in a sub-optimal way
8
9. Experiment 2
Method
If reconstruction of the human body shape from point positions is critical for the recognition of
point-light walkers, then a stimulus that complies with kinematics should be easier recognized
than the CFS stimulus
SPS CFS
(Sequential Position Stimulus) (Critical Features Stimulus)
.... ...
.. ..
..
... ..
. VS
.. . .
Frame 1 Frame 2 Frame 3
t
SPS does not affect body shape and matches exactly the human body kinematics
1,2,4 dots 1 frame
9
10. Experiment 2
Results
SPS CFS
..
. . VS
.....
7 Subjects
Task: Recognition of direction of walking
No differences between the two stimuli
No precise information about the body shape is needed
Both stimuli might be processed by a common mechanism
Asymmetry of the stimulus seems to be an important factor
10
11. Neural Model
I. Local Motion Detectors (LMD): small receptive fields, direction preference
II. Opponent motion detectors: Respond if LMD -within two adjacent subfields- with oposite
direction preference are active
III. Detectors for complex global optic flow patterns: Larger receptive fields than the whole
point-light stimulus, selectivity established by training, each frame has an optic flow pattern that
is encoded by a radial basis function
IV. Motion Pattern Neurons: Sum and temporally smooth the activities of optic flow pattern
detectors that belong to the same human action
11
12. Neural Model
Psychophysical experiment
Results
Recognition performances for both types of stimuli are very similar.
Recognition performance increases with the number of dots in the stimulus
Recognition rates for 8 and 4 dots are close to the values obtained in the psychophysical experiment
The recognition rates for 2 dots are lower than human performance
This model is not able to analyze stimuli with a single dot
No strong increase of performance with the lifetime of dots
High recognition rates can be accomplished solely based on the proposed critical
motion feature
High performance rates for degraded stimuli can be accomplished without complex
computational mechanisms
12
13. Discussion
Normal and point-light stimuli share very similar dominant mid-level optic flow features
r=0.93
The appropriate spatial arrangement of these features induces the percept of a person
walking, even though the stimuli do not comply with the kinematics of the human body
The detailed form information provided by the SPS does not seem to improve the recognition of
walking direction
A neural model that exploits these critical features achieves substantial recognition rates, even for
degraded point-light stimuli
13
14. Discussion
Physiological studies support this computational model (neural detectors for opponent motion)
Simple neural circuit. Not complex computational mechanism.
The local motion information can be used for
other discrimination tasks (e.g. identification of gait)
Fast
Slow
http://www.biomotionlab.ca/Demos/BMLwalker.html
14
15. Discussion
Physiological studies support this computational model (neural detectors for opponent motion)
Simple neural circuit. Not complex computational mechanism.
The local motion information can be used for
other discrimination tasks (e.g. identification of gait)
For more difficult tasks, more information might be required.
Female
Male
http://www.biomotionlab.ca/Demos/BMLwalker.html
15