Gordon Pipa Institute of Cognitive Science University of
Transcript of Gordon Pipa Institute of Cognitive Science University of
Identification of complex biological Motion patterns
Gordon Pipa
Institute of Cognitive Science University of Osnabrück
With:
Matthias Staib (UOS) Frank Jäckel (UOS) Robert Haslinger (MIT)
The Brain: Large, self-organized but mostly random Network
Giese 2003
RF V1
RF V4
RF IT
Hagmann, et al. (2008), ’Mapping the structural core of human
cerebral cortex’ PLoS Biol 6(7): e159
• Complex network with a high degree of randomness, and that is changing at all times
• Intrinsic activity dominates complex neuronal dynamics (Stimulus = Perturbation)
• Haslinger, R., Pipa, G., Lima, B., Singer, W., Brown, E. N., & Neuenschwander, S. (2012). Context Matters:
The Illusive Simplicity of Macaque V1 Receptive Fields. PLoS ONE, 7(7), e39699.
Biological Motion with Reservoir Computing
excitatory
inhibitory
Output
SORN:
• 25 bipolar cells (center surround RF), project randomly on excitatory neurons
• Reservoir: 100 exc. + 20 inh.
• Plasticity for Reservoir learning
• RF covers ~6% of visual field
• A. Lazar, G. Pipa, and J. Triesch, Frontiers Computational Neuroscience 2009
• R. Haslinger, G. Pipa, B. Lima, W. Singer, E.N. Brown, S. Neuenschwander (2012). PLoS ONE, 7(7), e39699
happy,
sad,
angry
Classification
Reservoir Computing
Incoming drive Threshold Input
task: Mood
Bipolar Cells
With center
surround
1
1 ( )N
i ij j i
j
h t f W t x t T t I t
• A. Lazar*, G. Pipa*, and J. Triesch (*authors contributed equally), Neural Networks, 20(3):312--322, 2007
• A. Lazar, G. Pipa, and J. Triesch, Frontiers Computational Neuroscience 2009
RF covers 6%
Network structure
Network activity
Plasticity
time
un
it
Information processing
Two types of neuronal plasticity:
• Spike timing dependent plasticity (STDP) for network structure formation
• Intrinsic plasticity for homeostasis of average activity of neurons
• Unsupervised learning in the recurrent network
Self-organization of the network and network activity
• V. Gomez, A. Kaltenbrunner, V. Lopez, H. Kappen, ’Self-organization using synaptic plasticity’, NIPS 2008
• Savin C, Joshi P, Triesch J (2010) Independent Component Analysis in Spiking Neurons. PLoS Comput Biol 6(4)
• A. Lazar*, G. Pipa*, and J. Triesch (*authors contributed equally), Fading memory and time series prediction in
recurrent networks with different forms of plasticity, Neural Networks, 20(3):312--322, 2007
• Binarized STDP:
• Intrinsic plasticity:
• Synaptic scaling:
SORN – 3 plasticity mechanisms
• A. Lazar*, G. Pipa*, and J. Triesch (*authors contributed equally), Neural Networks, 20(3):312--322, 2007
• A. Lazar, G. Pipa, and J. Triesch, Frontiers Computational Neuroscience 2009
bipolar cells
SORN:
• Bachelor Thesis in Cognitive Science, Osnabrück Germany by Matthias Staib (2013)
• A. Lazar, G. Pipa, and J. Triesch, Frontiers Computational Neuroscience 2009
Cycle:
16 Frames
PreTraining:
500x3 cycles
Training:
100x3 cycles
Testing:
100x3 cycles
from 35 subjects
Performance
• Similar performance for SORN and LSM with
shuffled weights and thresholds.
• RC properties, i.e. feature expansion and
memory are necessary
(Control Logistic classifier: ~55%)
• Why not just a random LSM ?
• What is the advantage of SORN that learns
temporal patterns based on plasticity ?
• A. Lazar*, G. Pipa*, and J. Triesch (*authors contributed equally), Neural Networks, 20(3):312--322, 2007
• A. Lazar, G. Pipa, and J. Triesch, Frontiers Computational Neuroscience 2009
Performance for occluder Task
• Initial fading memory the same for both the LSM and SORN
• After 15 frames memory maintains to be larger for SORN
• Distance between models (Hamming distance) larger for SORN
Generalisation for other viewing directions
Training orientations: 50°, 75°, 110°, 135°
Interpolation: Radom orientation in [55,° 70°] or [115° 130°]
Extrapolation: Radom orientation in [5,° 50°] or [140° 175°]
Conclusions
• Both the hierarchical and the RC based recognition of emotions work
well
• One to one comparison of performance is difficult since models are
rather different in structure
• Most of the activity even in early primary areas is not directly stimulus
related
• Classical approaches using feed forward structures explain this with
noise
• The RC approach provides an alternative that utilizes induced complex
temporal firing sequences to maintain memory, bridge lacking
stimulation, and optimize stimulus presentations.
• Processing a complex stimulus property such a mood can be
implemented with a single recuurent module, for example in V1.