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Local and Global Gating of Synaptic Plasticity
1. Local and Global Gating of
Synaptic Plasticity
Manuel A. Sanchez-Montañes
Hannes Schulz
University of Osnabrück, Department of Cognitive Science
Action and Cognition II / May 2nd 2005
2. Learning on Global vs. Local Scale
Mechanisms for learning in neuronal nets:
Local Global
Hebb’s Rule & modifications Signals to the whole network
Forms representations on Modifies local learning
cortex
3. Learning Goals
Local Learning: Global Learning:
Representation of all Influence size of
stimuli representation
Possibility to add new (→ experimental
stimuli later on evidence on basal
forebrain)
Independence from
presentation frequency
◮ How to connect both?
4. The Neuronal Model Experimental Results Discussion
Outline
1 The Neuronal Model
The Cell
The Network
The Inputs
2 Experimental Results
3 Discussion
Hannes Schulz Local and Global Gating of Synaptic Plasticity
5. The Neuronal Model Experimental Results Discussion The Cell The Network The Inputs
Outline
1 The Neuronal Model
The Cell
The Network
The Inputs
2 Experimental Results
3 Discussion
Hannes Schulz Local and Global Gating of Synaptic Plasticity
6. The Neuronal Model Experimental Results Discussion The Cell The Network The Inputs
The Cell Model
integrate-and-fire
delayed transmission
refractory period
Hannes Schulz Local and Global Gating of Synaptic Plasticity
7. The Neuronal Model Experimental Results Discussion The Cell The Network The Inputs
Network Topology
Input Cells Excitatory Cells
Hannes Schulz Local and Global Gating of Synaptic Plasticity
8. The Neuronal Model Experimental Results Discussion The Cell The Network The Inputs
Network Topology
Input Cells Excitatory Cells Inhibitory Cells
Hannes Schulz Local and Global Gating of Synaptic Plasticity
9. The Neuronal Model Experimental Results Discussion The Cell The Network The Inputs
Network Topology
Input Cells Excitatory Cells Inhibitory Cells
Hannes Schulz Local and Global Gating of Synaptic Plasticity
10. The Neuronal Model Experimental Results Discussion The Cell The Network The Inputs
Network Topology
Input Cells Excitatory Cells Inhibitory Cells
Nucleus Basalis
Hannes Schulz Local and Global Gating of Synaptic Plasticity
11. The Neuronal Model Experimental Results Discussion The Cell The Network The Inputs
Network Topology
Input Cells Excitatory Cells Inhibitory Cells
Nucleus Basalis
Layers 1&2, 3&2 fully connected
Random weight initialization
Hannes Schulz Local and Global Gating of Synaptic Plasticity
12. The Neuronal Model Experimental Results Discussion The Cell The Network The Inputs
Network Topology
Input Cells Excitatory Cells Inhibitory Cells
Nucleus Basalis
Layers 1&2, 3&2 fully connected
Random weight initialization
Hannes Schulz Local and Global Gating of Synaptic Plasticity
13. The Neuronal Model Experimental Results Discussion The Cell The Network The Inputs
Retrograde Signal Inhibition
Retrograde Signal
Inhibitory Signal
Dendrit
◮ Retrograde signal
of firing cell
Firing blocked by
inhibitory inputs
Cell
Hannes Schulz Local and Global Gating of Synaptic Plasticity
14. The Neuronal Model Experimental Results Discussion The Cell The Network The Inputs
Heterosynaptic Long Term Depression
Presynaptic Cell Postsynaptic Cell
“Normal” Condition
Hannes Schulz Local and Global Gating of Synaptic Plasticity
15. The Neuronal Model Experimental Results Discussion The Cell The Network The Inputs
Heterosynaptic Long Term Depression
Presynaptic Cell Postsynaptic Cell
Heterosynaptic LTD:
postsynaptic cell
active
presynaptic cell
“Normal” Condition inactive in time
window
◮ Synaptic efficacy
decrease
LTD
Hannes Schulz Local and Global Gating of Synaptic Plasticity
16. The Neuronal Model Experimental Results Discussion The Cell The Network The Inputs
Relative Timing in Hebb Learning
Sample interpretations of “Synchronous activity”:
Symmetric coincidence
window.
Hannes Schulz Local and Global Gating of Synaptic Plasticity
17. The Neuronal Model Experimental Results Discussion The Cell The Network The Inputs
Relative Timing in Hebb Learning
Sample interpretations of “Synchronous activity”:
Symmetric coincidence
window.
Asymmetric
coincidence window.
Used in this study.
Hannes Schulz Local and Global Gating of Synaptic Plasticity
18. The Neuronal Model Experimental Results Discussion The Cell The Network The Inputs
Input
Trial I
Ten different stimuli
pseudorandom order
500 examples shown to
network
Hannes Schulz Local and Global Gating of Synaptic Plasticity
19. The Neuronal Model Experimental Results Discussion The Cell The Network The Inputs
Input
Trial I Trial II
Ten different stimuli 9+1 different stimuli
pseudorandom order pseudorandom order
500 examples shown to 500 examples shown to
network network
One stimulus paired with
stimulus in basal-neuron
Hannes Schulz Local and Global Gating of Synaptic Plasticity
20. The Neuronal Model Experimental Results Discussion The Cell The Network The Inputs
Input
Trial I Trial II
Ten different stimuli 9+1 different stimuli
pseudorandom order pseudorandom order
500 examples shown to 500 examples shown to
network network
One stimulus paired with
stimulus in basal-neuron
Both run 40 times with random start parameters
Hannes Schulz Local and Global Gating of Synaptic Plasticity
21. The Neuronal Model Experimental Results Discussion
Outline
1 The Neuronal Model
The Cell
The Network
The Inputs
2 Experimental Results
3 Discussion
Hannes Schulz Local and Global Gating of Synaptic Plasticity
22. The Neuronal Model Experimental Results Discussion
Formed Representations – Trial I
Neuron Specificity
Stimuli shown more often
44.8% unspecific
to network not better
50.5% specific to 1 stimulus
represented
4.7% intermediate
Hannes Schulz Local and Global Gating of Synaptic Plasticity
23. The Neuronal Model Experimental Results Discussion
Formed Representations – Trial II
Number of neurons
representing
paired stimulus
increases
Number of neurons
representing other
stimuli stay the
same
Hannes Schulz Local and Global Gating of Synaptic Plasticity
24. The Neuronal Model Experimental Results Discussion
Role of the Global Mechanism
1 Basal ganglion neuron fires
2 Inhibitory neurons have a
prolonged refractory period
3 Inhibitory neurons do not fire
4 Excitatory neurons can fire
and shorten refractory period
again
Network Topology
Hannes Schulz Local and Global Gating of Synaptic Plasticity
25. The Neuronal Model Experimental Results Discussion
Role of the Global Mechanism
◮ Unchanged mean activity of
inhibitory neurons
◮ Delay of inhibitory activity relative to
excitatory activity
◮ Retrograde APs invade dendritic
tree
Network Topology
Hannes Schulz Local and Global Gating of Synaptic Plasticity
26. The Neuronal Model Experimental Results Discussion
The Same, in Graphs
Delay of inhibitory Number of neurons
activity relative to representing paired
excitatory activity stimulus increases
Number of neurons
representing other stimuli
stay the same
Hannes Schulz Local and Global Gating of Synaptic Plasticity
27. The Neuronal Model Experimental Results Discussion
Outline
1 The Neuronal Model
The Cell
The Network
The Inputs
2 Experimental Results
3 Discussion
Hannes Schulz Local and Global Gating of Synaptic Plasticity
28. The Neuronal Model Experimental Results Discussion
Comparison of Results to Goals
New stimuli can be trained w/o loss
Optimally activated neurons inhibit others
Unspecific neurons are result
Frequency invariance, variable representation size
Invariance due to mechanism described above
Stimulus representation enhanced by global
mechanism
Hannes Schulz Local and Global Gating of Synaptic Plasticity
29. The Neuronal Model Experimental Results Discussion
Predictions for Experimentors
During basal forbrain stimulation:
Delayed inhibitory activity
Invasion of dendritic tree by more retrograde APs
Hannes Schulz Local and Global Gating of Synaptic Plasticity
