1. Information Processing by Neuronal Populations Chapter 5 Measuring distributed properties of neural representations beyond the decoding of local variables: implications for cognition Adam Johnson, Jadin C. Jackson, and A. David Redish
Summary by B.-H. Kim
Biointelligence Lab
School of Computer Sci. & Eng.
Seoul National University

2. (c) 2000-2008 SNU CSE Biointelligence Lab
Outline
Introduction
Representation / Encoding (tuning curves) / Decoding (reconstruction)
Non-local reconstruction (memory and cognition)
Self-consistency (coherency)
Comparing actual and expected activity patterns
Validation by simulations
Self-consistency in a Bayesian framework
Multiple models in hippocampus
conclusions
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3. Introduction Neural representations are distributed X S
Neural Activity
sensory description
moter planning for behavior
cognitive process in between
Modern recording technologies
Neural ensembles
Behavioral variables
Immediate reconstruction X
simultaneous recording of large neural ensembles (> 100 cells simultaneously) from awake behaving animals.
Distributed representation
Representation of non-local values for cognitive process
Neural representations
within ensembles
Neural Activity
Measuring self-consistency
Dynamic changes in the self-consistency 
Indicative of cognitive process S
Check the extent the firing pattern matches expectations
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4. Ensemble-based reconstruction
Sensory process
Response
World
Memory process
Decision making
Information
Neural
Representation
Action
Behavior x
encoded information
preceding experience
planned future behaviors s
neural activity
spikes
If information is consistently represented across a population of neurons,
then it should be possible to infer the expectations of the variable x
by examining the neural activity across the population.
Encoding model: hypothesized relationship
tuning curves
mutual information I(x;s)
linear filter kernel
Encoding
Decoding depends on the encoding model
Decoding
(reconstruction)
conditional independency assumption
non-prob. methods
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5. Hippocampus in a brain Hippocampus
plays major roles in short term memory and spatial navigation
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6. Non-local reconstruction (memory and cognition) – Example of a rat experiment
Non-local reconstruction is a sign of memory and cognition
Internal representation reflects Primary inputs (information on location)
rats perform active behavioral tracks on an environment
location of the animal
ex) slow dynamics in which reconstruction tracks behavior
Different information processing modes
Internal representation deviate
from the external world S
cognition potentially plays a role
There is a connection of the observable world with rat’s invisible goals or motivations
sleeping /pausing at feeder sites: reflecting recently experienced memories rather than the current location
reconstruction during non-attentive waking states: representations of non-local information
ex) fast dynamics of replay
Neural Activity
external variable
internal representation
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7. Authors’ contribution
Defining self-consistency (or coherency), in order to differentiate between models
Implications of multiple generative models
for understanding these multiple information processing modes
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8. Self-consistency : Motivation
Possible pitfall of the ensemble-based reconstruction
Risky assumption: brain rigidly adheres to representing the present behavioral status
Reconstruction errors are viewed as “noise in the system”
Ignoring the cognitive questions of memory and recall
Questions
What is recall or confusion?
How does the brain represent competing values in ambiguous situations?
How do units within a network function together to form a coherent representation?
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9. Self-consistency: an Example
(tuning curve)
A coherent or self-consistent representation
The firing of all neurons in a network conforms to some pattern expected from observations during normal encoding.
A: unimodal tuning curve
B: coherent network firing pattern
C: bimodal representation – ambiguous or incoherent state of the network
D: confused or incoherent state of the network
(behavioral variable)
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10. Measures for self-consistency
Studies of Redish, Averbeck, Georgopoulous
Jackson and Redish (2003)
Defining the expected ‘activity packet’
Reflecting actual ‘reconstruction errors’ in the ‘self-consistency’ of the representation
Measures: comparing actual and expected activity patterns
k: available cells in the ensemble
T: turning curve
F: firing rate
Most sensitive
Recommended for real use
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11. Validation by simulations – Setting
Attractor network used for the simulations
Standard local-excitatory/global-inhibitory network
Features of the structure
symmetric local excitatory connections btw neurons with similar preferred directions
Global inhibition with periodic boundary conditions
Can be thought as a circular ring of neurons with a stable attractor state
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12. Validation by simulations – Issue 1
Random network firing vs. stable activity mode
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13. Validation by simulations – Issue 2
Rotation vs. jump
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14. Validation by simulations – Issue 3
Ambiguous vs. single-valued representations
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15. Self-consistency in a Bayesian Framework
Self-consistency measure in a Bayesian Framework
(probability of a given neural activity set)
(observed neural activity)
(decoded neural representation)
(M: the generative model used)
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16. Multiple models in hippocampus
Spatial representations within the hippocampus
Generally, the neural activity of place cells and the decoded spatial representation very well predicts the animal’s position
However, place cell activity can remain well organized even when the decoded representation does not match the animal’s position
Properties of hippocampus of interest
Multiple brain states
Multiple spatiotemporal dynamics, even during awake behaviors
Predictive filter
Prediction step
Correction step
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17. Multiple generative models in the hippocampus
4 generative models used
(prob. dist. is spread with
different rates)
Percentage of samples in which each model was found to be the most consistent
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18. Self-consistency (coherency) measures
Summary
Title revisited
Measuring distributed properties of neural representations - self-consistency measures in neural ensemble
beyond the decoding of local variables - Immediate reconstruction
implications for cognition
Dynamic changes in the self-consistency  Indicative of cognitive process
Self-consistency (coherency) measures
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