Activity Dependent Conductances:An “Emergent” Separation of Time-Scales

David McLaughlin

Courant Institute & Center for Neural Science

New York University

dmac@courant.nyu.edu

Input Layer of Primary Visual Cortex (V1) for Macaque Monkey

Modeled at :

Courant Institute of Math. Sciences

& Center for Neural Science, NYU

In collaboration with:

Robert Shapley (Neural Sci)

Michael Shelley

Louis Tao

Jacob Wielaard

Visual Pathway: Retina --> LGN --> V1 --> Beyond

Our Model • A detailed, fine scale model of a local patch of

input layer of Primary Visual Cortex;• Realistically constrained by experimental data;

Refs: McLaughlin, Shapley, Shelley & Wielaard

--- PNAS (July 2000)

--- J Neural Science (July 2001)

--- J Neural Science (submitted, 2001) Today

Equations of the Model

= E,I

vj -- membrane potential

-- = Exc, Inhib -- j = 2 dim label of location on

cortical layer -- 16000 neurons per sq mm (12000 Exc, 4000 Inh)

VE & VI -- Exc & Inh Reversal Potentials (Constants)

Conductance Based Model

= E,I

Schematic of Conductances

gE(t) = gLGN(t) + gnoise(t) + gcortical(t)

Conductance Based Model

= E,I

Schematic of Conductances

gE(t) = gLGN(t) + gnoise(t) + gcortical(t) (driving term)

Conductance Based Model

= E,I

Schematic of Conductances

gE(t) = gLGN(t) + gnoise(t) + gcortical(t) (driving term) (synaptic noise)

(synaptic time scale)

Conductance Based Model

= E,I

Schematic of Conductances

gE(t) = gLGN(t) + gnoise(t) + gcortical(t) (driving term) (synaptic noise) (cortico-cortical)

(synaptic time scale) (LExc > LInh) (Isotropic)

Conductance Based Model

= E,I

Schematic of Conductances

gE(t) = gLGN(t) + gnoise(t) + gcortical(t) (driving term) (synaptic noise) (cortico-cortical)

(synaptic time scale) (LExc > LInh) (Isotropic)

Inhibitory Conductances:

gI(t) = gnoise(t) + gcortical(t)

Integrate & Fire Model

= E,I

Spike Times:

tjk = kth spike time of jth neuron

Defined by:

vj(t = tj

k ) = 1,vj

(t = tjk + ) = 0

Conductances from Spiking Neurons

LGN & Noise Spatial Temporal Cortico-cortical

Here tkl (Tk

l) denote the lth spike time of kth neuron

Elementary Feature Detectors

Individual neurons in V1 respond preferentially to elementary features of the visual scene (color, direction of motion, speed of motion, spatial wave-length).

Elementary Feature Detectors

Individual neurons in V1 respond preferentially to elementary features of the visual scene (color, direction of motion, speed of motion, spatial wave-length).

Three important features:

Elementary Feature Detectors

Individual neurons in V1 respond preferentially to elementary features of the visual scene (color, direction of motion, speed of motion, spatial wave-length).

Three important features:

• Spatial location (receptive field of the neuron)

Elementary Feature Detectors

Individual neurons in V1 respond preferentially to elementary features of the visual scene (color, direction of motion, speed of motion, spatial wave-length).

Three important features:

• Spatial location (receptive field of the neuron)• Spatial phase (relative to receptive field center)

Elementary Feature Detectors

Individual neurons in V1 respond preferentially to elementary features of the visual scene (color, direction of motion, speed of motion, spatial wave-length).

Three important features:

• Spatial location (receptive field of the neuron)• Spatial phase (relative to receptive field center)

• Orientation of edges.

Two Angles:

Angle of orientation --

Angle of spatial phase -- (relevant for standing gratings)

Grating Stimuli

Standing & Drifting

Orientation Tuning Curves(Firing Rates Vs Angle of Orientation)

Terminology:

• Orientation Preference

• Orientation Selectivity

Measured by “ Half-Widths” or “Peak-to-Trough”

Spikes/sec

Orientation Preference

Orientation Preference

• Model neurons receive their

orientation preference

from convergent LGN input;

Orientation Preference

• Model neurons receive theirorientation preference

from convergent LGN input;

• How does the orientation preference k of the kth

cortical neuron depend upon the neuron’s

location k = (k1, k2) in the cortical layer?

Cortical Map of Orientation Preference

• Optical Imaging Blasdel, 1992

• Outer layers (2/3) of V1

• Color coded for angle of orientation preference

---- 500

----

right eye

left eye

PinwheelCenters

4 Pinwheel Centers

1 mm x 1 mm

Active Model Cortex - High Conductances

When the model performs realistically, with respect to biological measurements –

with proper-- firing rates-- orientation selectivity (tuning width & diversity)-- linearity of simple cells

the numerical cortex resides in a state of high conductance,with inhibitory conductances dominant!

The next few slides demonstrate this “cortical operating point”

\

Conductances Vs Time

• Drifting Gratings -- 8 Hz• Turned on at t = 1.0 sec

• Cortico-cortical excitation weak relative to LGN; inhibition >> excitation

Distribution of ConductanceWithin the Layer

<gT> = Time Average

SD(gT) = Standard DeviationOf Temporal Fluctuations

Sec-1

Sec-1

Active Model Cortex - High Conductances

• Background Firing Statistics

====> gBack = 2-3 gslice

Active Model Cortex - High Conductances

• Background Firing Statistics

====> gBack = 2-3 gslice

• Active operating point

====> gAct = 2-3 gBack = 4-9 gslice

Active Model Cortex - High Conductances

• Background Firing Statistics

====> gBack = 2-3 gslice

• Active operating point

====> gAct = 2-3 gBack = 4-9 gslice

====> gInh >> gExc

Active Model Cortex - High Conductances

• Background Firing Statistics

====> gBack = 2-3 gslice

• Active operating point

====> gAct = 2-3 gBack = 4-9 gslice

====> gInh >> gExc

• Consistent with experiment

Hirsch, et al, J. Neural Sci ‘98;

Borg-Graham, et al, Nature ‘98;

Anderson, et al, J. Physiology ‘00

Active Cortex - Consequences of High Conductances

• Separation of time scales ;

Active Cortex - Consequences of High Conductances

• Separation of time scales ;

• Activity induced g = gT-1 << syn (actually, 2 ms << 4 ms)

Conductance Based Model

= E,I

dv/dt = - gT(t) [ v - VEff(t) ],

where gT(t) denotes the total conductance, and

VEff(t) = [VE gEE(t) - | VI | gEI(t) ] [gT(t)]-1

If [gT(t)] -1 << syn v VEff(t)

But the separation is only a factor of 2

(g = gT-1 = 2 ms; syn =4 ms)

Is this enough for the time scales to be “well separated” ?

Active Cortex - Consequences of High Conductances

Membrane potential ``instantaneously’’ tracks conductances on the synaptic time scale.

V(t) ~ VEff(t) = [VE gEE(t) - | VI | gEI(t) ] [gT(t)]-1

where gT(t) denotes the total conductance

High Conductances in Active Cortex Membrane Potential Tracks Instantaneously “Effective Reversal Potential”

Active Background

Effects of Scale Separation

g = 2 syn

g = syn

g = ½ syn

____(Red) = VEff(t)

____(Green) = V(t)

Fluctuation-driven spiking

Solid: average ( over 72 cycles)

Dashed: 10 temporal trajectories

(very noisy dynamics,on the synaptic time scale)

Coarse-Grained Asymptotics

Coarse-Grained Asymptotics

• Using the spatial regularity of cortical maps (such as orientation preference), we “coarse grain” the cortical layer into local cells or “placquets”.

Cortical Map of Orientation Preference

• Optical Imaging Blasdel, 1992

• Outer layers (2/3) of V1

• Color coded for angle of orientation preference

---- 500

----

right eye

left eye

Coarse-Grained Asymptotics

• Using the spatial regularity of cortical maps (such as orientation preference), we “coarse grain” the cortical layer into local cells or “placquets”.

Coarse-Grained Asymptotics

• Using the spatial regularity of cortical maps (such as orientation preference), we “coarse grain” the cortical layer into local cells or “placquets”.

• Using the separation of time scales which emerge from cortical activity, g << syn

Coarse-Grained Asymptotics

• Using the spatial regularity of cortical maps (such as orientation preference), we “coarse grain” the cortical layer into local cells or “placquets”.

• Using the separation of time scales which emerge from cortical activity, g << syn

Coarse-Grained Asymptotics

• Using the spatial regularity of cortical maps (such as orientation preference), we “coarse grain” the cortical layer into local cells or “placquets”.

• Using the separation of time scales which emerge from cortical activity, g << syn

• Together with an averaging over the random cortical maps (such as spatial phase, De Angelis, et al ‘99)

Coarse-Grained Asymptotics

• Using the spatial regularity of cortical maps (such as orientation preference), we “coarse grain” the cortical layer into local cells or “placquets”.

• Using the separation of time scales which emerge from cortical activity, g << syn

• Together with an averaging over the irregular cortical maps (such as spatial phase)

• we derive a coarse-grained description in terms of the average firing rates of neurons within each placquet

[--- a form of Cowan – Wilson Eqs (1973)]

Uses of Coarse-Grained Eqs

Uses of Coarse-Grained Eqs

• Unveil mechanims for (i) Better orientation selectivity near pinwheel centers

(ii) Balances for simple and complex cells• Input-output relations at high conductance• Comparison of the mechanisms and performance

of distinct models of the cortex• Most importantly, much faster to integrate; • Therefore, potential parameterizations for more

global descriptions of the cortex.

Active Cortex - Consequences of High Conductances

Cortical activity induces a separation of time scales (with the synaptic time scale no longer the shortest), Thus, cortical activity can convert neurons from integrators to burst

generators & coincidence detectors.

For transmission of information:

“Input” temporal resolution -- synaptic time scale syn ;

“Output” temporal resolution -- g = gT-1

Summary: One Model of Local Patch of V1 • A detailed fine scale model -- constrained in its construction and

performance by experimental data ;• Orientation selectivity & its diversity from cortico-cortical activity,

with neurons more selective near pinwheels;• Linearity of Simple Cells -- produced by (i) averages over spatial

phase, together with cortico-cortical overbalance for inhibition;

• Complex Cells -- produced by weaker (and varied) LGN input, together with stronger cortical excitation;

• Operates in a high conductance state -- which results from cortical activity, is consistent with experiment, and makes integration

times shorter than synaptic times, a separation of temporal scales with functional implications;

• Together with a coarse-grained asymptotic reduction -- which unveils cortical mechanisms, and will be used to parameterize or

``scale-up’’ to larger more global cortical models.

Scale-up & Dynamical Issuesfor Cortical Modeling

• Temporal emergence of visual perception• Role of temporal feedback -- within and between cortical

layers and regions• Synchrony & asynchrony• Presence (or absence) and role of oscillations• Spike-timing vs firing rate codes• Very noisy, fluctuation driven system• Emergence of an activity dependent, separation of time

scales• But often no (or little) temporal scale separation

Distribution of Conductances Over Sub-Populations“FAR” & “NEAR” Pinwheel Centers

<gT> = Time Average

SD(gT) = Stand Dev of Temporal Fluctuations

One application of Coarse-Grained Equations

Why the Primary Visual Cortex?

Why the Primary Visual Cortex?

Elementary processing, early in visual pathwayNeurons in V1 detect elementary features of the visual scene,

such as spatial frequency, direction, & orientation

Why the Primary Visual Cortex?

Elementary processing, early in visual pathwayNeurons in V1 detect elementary features of the visual scene,

such as spatial frequency, direction, & orientation

Vast amount of experimental information about V1

Why the Primary Visual Cortex?

Elementary processing, early in visual pathwayNeurons in V1 detect elementary features of the visual scene,

such as spatial frequency, direction, & orientation

Vast amount of experimental information about V1Input from LGN well understood (Shapley, Reid, …)

Anatomy of V1 well understood (Lund, Callaway, ...)

Why the Primary Visual Cortex?

Elementary processing, early in visual pathwayNeurons in V1 detect elementary features of the visual scene,

such as spatial frequency, direction, & orientation

Vast amount of experimental information about V1Input from LGN well understood (Shapley, Reid, …)

Anatomy of V1 well understood (Lund, Callaway, ...)

The cortical region with finest spatial resolution --

Why the Primary Visual Cortex?Elementary processing, early in visual pathway

Neurons in V1 detect elementary features of the visual scene, such as spatial frequency, direction, & orientation

Vast amount of experimental information about V1Input from LGN well understood (Shapley, Reid, …)Anatomy of V1 well understood (Lund, Callaway, ...)

The cortical region with finest spatial resolution --Detailed visual features of input signal;

Why the Primary Visual Cortex?Elementary processing, early in visual pathway

Neurons in V1 detect elementary features of the visual scene, such as spatial frequency, direction, & orientation

Vast amount of experimental information about V1Input from LGN well understood (Shapley, Reid, …)Anatomy of V1 well understood (Lund, Callaway, ...)

The cortical region with finest spatial resolution --Detailed visual features of input signal;Fine scale resolution available for possible representation;