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DFT Summer School:Lecture 3John P. SpencerProfessorSchool of PsychologyUniversity of East AngliaNorwich, UK

How neural is DFT?ªDoes DFT simply create an analogy to neural systems?ªOr is DFT ‘neural’ in a deeper sense?

DFT is deeply neural…ªWe’ll show this using an analysis method called the

distribution of population activation (DPA)ªDPA creates a direct mapping from electrophysiological

recording to the neural population dynamics captured in DF models

ªBut more than that, we’ll see direct neurophysiological evidence for the types of neural interactions instantiated in DF models à neural evidence for DFT

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Receptive fields of single neuronsSingle cells are ‘tuned’ to metric dimensionsªThey fire maximally to certain

stimuliªBut tuning curves are generally

quite broadªAs a consequence, it’s hard to

glean much from the firing rates of individual cells

ªBy contrast, response across multiple cells can be quite informative

Spi

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ate

Feature DimensionValue 3Value 2

A B CNeurons

Value 1

(a)

Value 1 Value 3

Spi

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(c)

(b) Neuron A Neuron C

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1 3

Neuron B

2 1 32 1 32

Value 2

A CBA CB A CB

Does the whole population really matter?Yes!ªDeactivate A and visually

evoke an eye movement to A à still get an eye movement to A

ªDeactivate A and visually evoke an eye movement to B à get an eye movement biased toward D, even though B is still engaged (A has an influence, even though at the periphery)

Amplitude

+40°

+20°

0°

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-40°2°5° 10° 20° 30° 50°

A

B

C

B

A

C

Amplitude

+40°

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-40°2°5° 10° 20° 30° 50°

A

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Result (A)

C

Amplitude

+40°

+20°

0°

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-40°2°5° 10° 20° 30° 50°

A

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ResultD

D

(a)

(b)

(c)

A

Angula

rD

irec

tion

Angula

rD

irec

tion

Angula

rD

irec

tion

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What about non-topographic areas?ªMotor cortex is not topographic…but it is functionally

topographic

Population vectors

0°

30°

60°90°

120°

150°

180°

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240°270°

300°

330°

0°

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60°90°

120°

150°

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240°270°

300°

330°0° 90° 180° 270° 360°

Spik

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Movement Direction

(a) (b) (c)

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Population vector predicts movement direction prior to actual movement

DPA generalizes this approachªCells contribute their entire tuning curves, not just their

maximal direction

acti

vat

ion

feature space

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Evidence for surround inhibition

From excitation close to inhibition far

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Neural repulsion effect!

DPA in motor cortex

a

b

c

4 →

23

5 6

Start trial 500 ms PS

1

RS MVT

RTPP

1000 ms

← →← →

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complete information(a)

PS500

RS1500

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340

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time (ms)targets

popula

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two-target information(b)

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time (ms)targets

popula

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ctiv

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three-target information(c)

PS500

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time (ms)targets

popula

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ctiv

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34

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6 PS

RS

acti

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6 PS

RS

acti

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timetargets

timetargets

complete information (DNF model)(d)

two-target information (DNF model)(e)

DFT is not a neural analogyªEvidence suggests that the brain actually work this wayªNeural population dynamics captured by DFT are

observable in cortex