Temporal Code: Dynamics in neuronal networks Alexa Riehle Institut de Neurosciences Cognitives de la Méditerrannée INCM - CNRS Marseille [email protected]

Temporal Code:Dynamics in neuronal networks

Alexa RiehleInstitut de Neurosciences Cognitives de la Méditerrannée

INCM - CNRS

Marseille

[email protected]

It is commonly accepted that perceptual and motor functions are

based on distributed processes where neurons do not act in isolation, but

organize in functional groups. It is less clear, though, how these networks organize dynamically in space and

time to cope with momentary computational demands

Neural coding:

“rate vs temporal code”

Although our knowledge about the morphology and the physiology of the cerebral cortex drastically increased since the last 30 years, its

basic operational mode is still highly controversial debated.

rate code temporal code

What is a code ? the neuronal representation of information.

1) What ?

2) How ?

3) Which precision ?

How do all known components interact to form an efficient working system?How are complex functions such as perception and action realized within

neuronal networks?

The development of a new paradigm : the concept of "cell assemblies"

it is not a matter of an alternative, but a complementary mode

“Let us .. assume as the basis of all our subsequent reasoning this law: When two elementary brain processes have been active together or in immediate succession, one of them, on re-occurring, tends to propagate its excitement into the other.”

Cooperativity in cortical networks

William James (1890)Psychology (Briefer Course)

“The general idea is an old one, that any two cells or systems of cells that are repeatedly active at the same time will tend to become associated, so that activity in one facilitates activity in the other…

This is then the cell assembly. ”

Donald O. Hebb (1949)The Organization of Behavior

Raphael Lorente de Nó (1937)


“Each set (of neurons) excites synchronous firing in the next set, which in turn excites synchronously the next set of neurons, etc. We shall call this arrangement … the synfire chain. .. although the neuron operates as an integrator it is especially sensitive to coincident firing of a few presynaptic sources. ..It is evident that activity within a network of neurons would tend to organize itself in chains of synchronously firing groups..”

Moshe Abeles (1982)Local Cortical Circuits


“.. (cell assemblies) are dynamic entities, defined … by the ever-changing level of correlation among the activities of their member neurons. … there is no need for the synaptic contacts to be particularly strong: the corresponding connections become effective through synchronous activity with other neurons”

Ad Aertsen et al. (1991)Z. Hirnforsch. 6: 735-743


Functional and dynamic units

Defined by the synchronization or another precise temporal structure of the discharge

Flexibility : each neuron can participate, successively,

at differents functional groups

integration time long : 5 to 50 ms short : 1 to 5 msmean interval between spikes short long i.e., discharge rate high lowwho contributes to the generation of output ? all spikes only synchronous spikestemporal precision irrelevant relevant i.e. relevant information mean discharge rate temporal structure of spikeshow many spikes are necessary for producing a spike at the output ? ~300 ~30

Two modes of fonctioning of pyramidal neurons in the cerebral cortex

Temporal integration Coincidence detection

Synchronization of neuronal activity 1

There are two explanations for synchronous activity :* common input or* functional interaction by means of relatively

small neuronal networks

A common input modulates simultaneously the discharge patterns of the two neurons; there is, thus, no direct interaction between the two neurons. A functional interaction involves a mechanism by which the discharge of one neuron influences the discharge probability of the other.

We have to discriminate between * a structural connectivity (or an anatomic one) and * a functional connectivity (or an efficient one).

The first might be described as * stationary and fixed,

whereas the second as * dynamic having a time constant of modulation in the range of tens to hundreds of milliseconds.

Synchronization of neuronal activity 2

Are neurons able to produce action potentiels with a temporal precision

in the range of milliseconds ?

The temporal precision of neuronal discharge

Cat auditory cortex

the cat listens during 20 minutes to natural noise

Aertsen et al., Biol. Cybernetics 32: 175-185

(1979)


(1979)


…. the same tape is repeated


(1979)


…. the two records are then cross-correlated


… a constant electrical stimulus is repeatedly applied, the precision

vanishes


…. a noisy stimulus is applied, discharge is very

precise and repetitive

Two, three, many electrodes in the brain

20 m

Asynchronous action potentials

Neuron 1

Neuron 2

Synchronous action potentials

Neuron 1

Neuron 2

neuron 1

neuron 2

-3 -2 -1 0 1 2 3 time units

The most basic technique of data analysis

The cross-correlogram#

coin

cide

nces

Cross-correlation and shift predictor

Temporal precision : 1-2 ms

# of

spi

kes

/ bin

lead / lag (ms)

The binding problem

The understanding of how neurons co-operate in order to form perceptual or motor representations is one of the major objectives in neurobiology

How is individual neuronal activity integrated to form functionally efficient spatio-temporally patterns within networks?

Binding by synchrony

From: Engel et al, Cerebral Cortex 7: 571-582 (1997)

Freiwald, Kreiter & Singer, NeuroReport 6: 2348-2352 (1995)

Temporal coding in the visual cortex

Roy & Alloway, J Neurophysiol 81: 999-1013 (1999), Fig. 3

Temporal coding in the somatosensory cortex

Roy & Alloway, J Neurophysiol 81: 999-1013 (1999), Fig. 9

Temporal coding in the somatosensory cortex Width of the peak as a function of the stimulus

Raw Joint Peri-Stimulus-Time-Histogram (Joint PSTH)

Aertsen, Gerstein, Habib, Palm (1989) Dynamics of neuronal firing correlation modulation of "effective connectivity". J. Neurophysiol. 61: 900-917

Aertsen & Gerstein In: Krüger J (ed) Neuronal cooperativity. pp 52-67 (1991)

Aertsen & Gerstein In: Krüger J (ed) Neuronal cooperativity. pp 52-67 (1991)

Normalized Joint Peri-Stimulus-Time-Histogram

Joint Peri-Stimulus-Time-Histogram

(Joint PSTH)

Aertsen & Gerstein In: Krüger J (ed)

Neuronal cooperativity pp 52-67 (1991)

Normalized Joint PSTH

In two different behavioral conditions recorded in the frontal cortex of the monkey

Vaadia et al., Nature 373: 515-518 (1995)

Vaadia et al., Nature 373: 515-518 (1995)

Correlation in frontal eye field of the monkey

(1) Detection of precise spike coincidences :Activity of two simultaneously recorded neurons

Riehle et al., Science 278: 1950-1953 (1997)

(2) Detection of precise spike coincidences :Synchronous spikes (precision : 2 ms)


(3) Detection of precise spike coincidences :Measured (red) and expected (black) coincidence rates


(2) Detection of precise spike coincidences :Synchronous spikes (precision : 2 ms)


(4) Detection of precise spike coincidences :Statistically significant coincidences ("Unitary Events")


Multiple single-neuron recordings using

7 independently movable micro-electrodes

(Reitböck system, Thomas Recording, Germany)

Simple Reaction Time Task

One Movement Direction Uncertainty about Signal Occurrence

(“Conditional Probability”)Four possible Delay Durations presented

at random with equal probability: 600 - 900 - 1200 - 1500 ms

Riehle, Grün, Diesmann, Aertsen. Science 278: 1950-1953 (1997)

start switch PS 1.RS 2.RS 3.RS 4.RS

- 500ms 0 600 900 1200 1500 ms

time (ms)

Conditional Probability 0.25 0.33 0.5 1

Riehle, Grün, Diesmann, Aertsen. Science 278: 1950-1953 (1997)

Behavioral results

Conditional probability: 0.25 0.33 0.5 1

Spike synchronization in relation to signal

expectancy


Event expectancy with increasing probability


Synchronization and discharge rate

are used in a complementary way

by motor cortex

Internal event External event

% o

f pai

rs o

f neu

rons no rate

modulation rate modulation

After Riehle et al., Science 278: 1950-1953 (1997)

Bastian, Riehle, Erlhagen, Schöner. NeuroReport 9: 315-319 (1998)Grammont, Riehle. Exp. Brain Res. 128: 118-122 (1999)Erlhagen, Bastian, Jancke, Riehle, Schöner. J. Neurosci. Meth. 94: 53-66 (1999) Riehle, Grammont, Diesmann, Grün, J. Physiol (Paris) 94: 569-582 (2000)Bastian, Schöner, Riehle, Eur. J. Neurosci. (2003, in press)

Multi-directional Pointing Movement:Simple Reaction Time Task

No Uncertainty:Prior Information about Direction

Fixed Delay: 1000 ms

Schematic representation of the three main types of neurons recorded in the preparation paradigm

Riehle & Requin, 1987 - 1995

Assembly formation

Grammont & Riehle Exp. Brain Res. 128: 118-122 (1999)

Discharge rates of two simultaneously recorded neurons

The one is preparation-related (purple) and the other rather execution-related (green)

Assembly formation

Coincidences are detected with a temporal precision of 1 ms


Assembly formation

Raw (blue) and expected (black) coincidence rates


Assembly formation

statistical significance :Joint-surprise value


Assembly formation

Binding by synchronyor "shake-hand neurons":

Neurons form an assembly to strengthen the transition from

preparation to action

Unitary Events


Dynamics of synchronous spiking

activity:

Temporal precision

Binding by synchrony or "shake-hand neurons":

Neurons form an assembly to strengthen the transition from

preparation to action


Riehle, Grammont, Diesmann, Grün, J. Physiol (Paris) 94: 569-582 (2000)

Dynamics of synchronous spiking activity:

Modulation in time



Modulation in time


Modulation in time


Dynamics of of synchronous spiking activity:

Temporal Precision


Determining a binary vector from the joint-surprise values for each pair of neurons:

00000000000000000001111111100000011111000000000000000000000000000000

By averaging binary vectors from several pairs of neurons, one obtains the probabilibity of being significantly synchronized

Calculation of the probability of statistically significant synchronization in a population of neurons

Comparison of synchronicity and mean

firing rate

Tightening of precision and increase in firing rate toward the end of the

preparatory period


Grammont & Riehle, Exp. Brain Res. 128: 118-122 (1999)

Dynamics of cell assemblies

Multi-directional Pointing Task:Choice Reaction Time Task

Prior Information about Movement DirectionUncertainty about Signal Occurrence

(“Conditional Probability”)Two possible Delay Durations:

600 - 1200 ms

Riehle, Grammont, Diesmann, Grün, J. Physiol (Paris) 94: 569-582 (2000)Grammont & Riehle Biol Cybern 88: 360-373 (2003)

Reaction Times in relation to the Conditional Probability

short delay (p = 0.5) long delay (p = 1)

reac

tion

time

(ms )

Neuronal activity related to time estimation

Riehle, Bastian, Böye, Grammont (unpublished data, 2000)

PS ES RS


Cell assemblies are dynamic entities, where neurons participate in different assemblies at different times

Riehle & Grammont, unpubl. data (1998)

Cooperativity in cortical networks Dynamics of synchrony: Modulation in time


Probability of significant

Synchronization and mean Firing Rate

1 pair of neurons 6 movement directions







Time course of synchrony in a

population of motor cortical neurons

Probability of significant synchronization

Grammont & RiehleBiol Cybern 88: 360-373 (2003)

Mean firing rate


Time course of synchrony and

mean discharge rate in a population of

motor cortical neurons


7 ms precision


Time course of synchronicity and mean discharge rate of a population of motor cortical neurons

Grammont & Riehle Biol Cybern 88: 360-373 (2003)

Time course of synchronicity and mean discharge rate of a population in relation to movement direction


Left Right

Time course of synchronicity and mean discharge rate of a population in relation to movement direction


χ² -values per sliding window:

5.05 < χ² < 11.25

0.0001 < p < 0.05

Difference in firing rate never

statistically significant (t-test of Student)

Time course of mean discharge rate of a population of motor cortical neurons in relation to movement direction


Distribution of Preferred Directions


synchronization per movement

direction in a population

of motor cortical neurons

Grammont & Riehle Biol Cybern 88: 360-373

(2003)


and mean discharge rate

per movement direction in a population of motor

cortical neurons


(2003)




cortical neurons

tuning and preferred direction during the preparatory period


(2003)




cortical neurons

…during movement onset


(2003)

Reaction times in relation to movement direction


(2003)

Same preferred direction (PD) for synchrony, firing rate, and reaction time


(2003)

Not only neuronal firing rate, but also synchrony is predictive for

performance speed

Firing rate and reaction time

after Riehle & Requin Behav. Brain Res. 53:

35-49 (1993)

Reaction times and synchrony in relation to delay duration, i.e. conditional probability

short delay (p = 0.5) long delay (p = 1)

reac

tion

time

(ms)

Time course of synchronicity and mean discharge

rate of a population of motor cortical

neurons

Normalized cross-correlations calculated

in sliding windows

Riehle & Grammont (unpublished results, 2001)

Cross-correlation histogram and its normalization

Roux & Riehle (unpublished results, 2001)

Strengthening of precise synchronization toward the end of the preparatory period

Normalized cross-correlation calculated in sliding windows


lead

/ la

g (m

s)


Precise synchronization as a function of reaction timeCalculated during the last 500 ms of the preparatory period

Precise synchronization as a function of reaction timeCalculated during the preparatory period


Conclusions

significant synchronous spiking activity is not maintained for more than 100 to 200ms, it modulates in time changes its temporal precision during the instructed delay, most often increases its precision towards the end. is predictive for performance speed

Synchrony seems often to preceed the increase of neuronal activity

… but there is no simple parallel shifting in time.

Synchrony may trigger the increase in firing rate in large neuronal networks, which in turn communicate with the

periphery for initiating the movement

Thanks !!

Theory: Ad Aertsen (Univ. Freiburg, Germany)

Sonja Grün (FU Berlin, Germany) Markus Diesmann (MPI Göttingen, Germany)

Experiments: Franck Grammont (Univ. Parma, Italy)

Sébastien Roux (CNRS Marseille, France)

Documents

Temporal Code: Dynamics in neuronal networks Alexa Riehle Institut de Neurosciences Cognitives de la Méditerrannée INCM - CNRS Marseille [email protected]