balanced state and the primary visual cortex · balanced state and the primary visual cortex...
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![Page 1: Balanced state and the primary visual cortex · Balanced state and the primary visual cortex Germán Mato Statistical and Interdisciplinary Physics Group CNEA and CONICET Instituto](https://reader030.vdocuments.us/reader030/viewer/2022040305/5eae5ff986108e2100364b09/html5/thumbnails/1.jpg)
Balanced state and the primary visual cortexGermán Mato
Statistical and Interdisciplinary Physics GroupCNEA and CONICET
Instituto Balseiro, Centro Atómico Bariloche
ASG, Dresden, June-July 2016
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Main research interests● Single neuron dynamics
● Relation between coding properties and dynamics● Intrinsic conductances and dynamics of thalamic
neurons● Dynamics of large neural networks
● Primary Visual Cortex: effects of plasticity and homeostatic processes
● Prefrontal cortex: short term plasticity and working memory
● Motor system: thalamo-cortical-basal ganglia loop and Parkinson's disease
● Medical imaging ● Segmentation and registration
● Transfer of information in complex networks● Propagation of errors
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Collaborators● Single neuron dynamics
● Inés Samengo (Bariloche)● Marcelo Montemurro (Manchester)● Marcela Nadal, Yimy Amarillo (Bariloche)
● Dynamics of large neural networks● Román Rossi (Bariloche, now in Mexico)● Soledad Gonzalo (Bariloche)● David Hansel, Carl van Vreeswijk (Paris)● Damián Dellavalle, Osvaldo Velarde (Bariloche)
● Medical imaging ● Rodrigo Cárdenas (Bariloche)● Roberto Isoardi (FUESMEN, Mendoza)● Ariel Curiale (U. Cuyo, Mendoza)
● Transfer of information in complex networks● Marcelo Kuperman, Andrés Chacoma (Bariloche)
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Outline● Balanced state: motivation and properties
● Balanced state in systems with spatial structure
● Primary visual cortex: systems with and without orientation maps
● Plasticity in the primary visual cortex: analytical and numerical results
● Conclusions
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Balanced state● The firing patterns of
cortical neurons in intact animals display a strong degree of temporal variability
● They can be approxi-mated by a Poisson process with a smallrefractory period
(Softy and Koch, 1993)
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Balanced state● On the other hand cortical neurons fire quite
regularly when they receive a constant input (in vitro experiments)
● (Holt et al., 1996)
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Balanced state● Each cortical neuron receives hundreds or
thousands of synaptic connections
● 3 x 10**8 synapses/mm**310**5 neurons/mm**3
(Beaulieu and Colonnier, 1985)
● Correlations between the activity of different cortical neurons are weak
(Renart et al., 2010)
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Balanced state● In the limit of very large connectivity (K) each
neuron should receive a net input order K + fluctuations order sqrt(K)
● How all this be compatible?
● Very low firing rate?
● Some degree of synchronization?
● Additional sources of noise?
● Cancellation of excitation and inhibition?
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Balanced state● Cancellation of excitation and inhibition
(van Vreeswijk and Sompolinsky, 1996, 1998)
● NE, N
I excitatory and inhibitory neurons
● K connections per neuron (average), no spatial structure
● 1 << K << NE,N
I (strongly diluted connectivity)
● Synaptic efficacy O(1/sqrt(K)): “strong” synapses
● For binary neurons this system can be solved analytically
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Balanced state● The diluted connectivity guarantees there are
no correlations.● As K>>1 the distribution of inputs is
Gaussian● The distribution is characterized by fast and
quenched terms whose correlations can be evaluated self-consistently
● In the limit of very large K it is easy to see why it leads to a highly variable state
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Balanced state● PYR: excitatory neurons● IN: Inhibitory neurons
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Balanced state● n
E=F(h
E), n
I=F(h
I)
● n: firing rate; h: synaptic input; F: input-output transfer function
hE=sqrt(K){J
EE n
E – J
EI n
I + I
E}
hI=sqrt(K){J
IE n
E – J
II n
I + I
I}
● In the limit of large K it must beJEE
nE – J
EI n
I + I
E =O(1/sqrt(K))
JIE n
E – J
II n
I + I
i = O(1/sqrt(K))
● The net inputs (hE,h
I) must be order 1, otherwise the activity
would be 0 or at saturation value● But the fluctuations are proportional to n
E2 , n
I2
● The total fluctuation is order 1
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Balanced state● Numerical simulations
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Balanced state● Properties of the balanced state
● In the limit of very large K it must be
0=JEE
nE – J
EI n
I + I
E
0=JIE
nE – J
II n
I + I
I
Or
nE = (J
II I
E – J
EI I
I)/(J
EI J
IE – J
EE J
II)
nI = (J
IE I
E – J
EE I
I)/(J
EI J
IE – J
EE J
II)
● There is a LINEAR relation between the firing rates and theexternal inputs. Single neuron input-output transfer function (F) is irrelevant
● The firing rates become lower if all the couplings become stronger
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Balanced state● Properties of the balanced state
● Conditions for stability
● JEI
JIE – J
EE J
II > 0
● JII – J
EE > 0
● tI < t
E (fast inhibition)
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Balanced state● Systems with spatial structure
(Rosenbaum and Doiron, 2014)
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Balanced state● Systems with spatial structure
● Now the balance conditions have to be satisfied locallyJ
EE* n
E(q) – J
EI* n
I (q) + I
E(q)
=O(1/sqrt(K))
JIE* n
E(q) – J
II * n
I(q) + I
i(q)= O(1/sqrt(K))
where * is the convolution operator
● In Fourier space: J
EE(n) n
E(n)– J
EI(n) n
I (n)+ I
E(n)
=O(1/sqrt(K))
JIE(n) n
E(n)– J
II(n) n
I(n)+ I
i(n)= O(1/sqrt(K))
● Taking the limit of very large K: n
E(n)= (J
II(n) I
E(n) – J
EI(n) I
I(n))/(J
EI(n) J
IE(n)– J
EE(n)J
II(n))
nI(n)= (J
IE(n) I
E(n)– J
EE(n) I
I(n))/(J
EI(n) J
IE(n)– J
EE(n)J
II(n))
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Primary visual cortex
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Primary visual cortex● Orientation selectivity
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Primary visual cortex● Orientation selectivity
q
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Primary visual cortex● Orientation maps
Modified from Blasdel G.G. and Salama G. Voltage sensitive dyes reveal a modular organization in monkey striate cortex, Nature (1986)
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Primary visual cortex● Cat/monkey vs. rat/mouse
(Ohki and Reid, 2007)
Salt-and-pepper
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Primary visual cortex● Neurons in rodent V1 are orientation selective (Niell and Stryker, 2008)
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Primary visual cortex● What are different mechanisms underlying the two cases?● What is the contribution of the of the intracortical connections to
selectivity?● In rodents, are intracortical connections functionally organized
and if this is so what is the effect on orientation selectivity?
«The mixed salt-and-pepper organization of preferred orientation in rodents […] argues for specific connectivity between neurons»
(Ohki and Reid, 2007)
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Primary visual cortex●Selectivity present in mouse at eye opening Ko et al, 2013
At eye opening: Neurons are selective to orientation; EE connectivity is non specific.
After critical period: Specificity in the EE connectivity
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The Models
● Reduced model
θ1 θ2
θ3
θ4
θ5
θ6
θ7
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The Models
● Reduced model
θ1 θ2
θ3
θ4
θ5
θ6
θ7
Orientation Map
Salt and Pepper
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The Models
● Computational Model Preferred orientation
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The Models
● Plasticity Rules
W/W
W/(2-W)
tpost- tpre
STDPSpike-Timing
Dependent Plasticity
Reconnection Rule: Connection probability of
neurons with similar preferred orientation is
strengthened
+
-
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The Models
● Orientation Selectivity Index
0 90 180
Stimulus orientationStimulus orientationStimulus orientation0 90 180
0 90 180
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Reduced Model
Connectivity profile
Firing Rate Average number of
connections
External current
Balance equations
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Solutions
Stability of the balanced state
Reduced Model
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1) If the spatial structure of the connectivity profile depends only on the presynaptic population
with:
2) The external current is the same for both populations
Reduced Model
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1) If the spatial structure of the connectivity profile depends only on the presynaptic population
2) The external current is the same for both populations
Reduced Model
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External Current
Orientation selectivity
index of the external current
Reduced Model
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External Current
•Casdasd
•Sdcadca
•asdcasdas
Reduced Model
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•Casdasd
•Sdcadca
•asdcasdas
Reduced Model
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•Casdasd
•Sdcadca
•asdcasdas
OSI
Reduced Model
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Salt and Pepper
Orientation Map
Mean value of the connectivity profileModulation of the connectivity profile
Reduced Model
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Salt and Pepper Orientation Map
Mean value of the connectivity profileModulation of the connectivity profile
Reduced Model
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0.0 0.5 1.00
250
500
750
1000
1250<OSI>=0.57
Cou
nts
OSI
<OSI>=0.27
0.0 0.5 1.00
250
500
750
1000
1250
OSIC
ount
s
Orientation Map Salt and Pepper
As predicted by the model, keeping all the other parameters the same, orientation selectivity is larger for Salt and Pepper.
Simulation Results
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Salt and Pepper
0.57 0.63
Orientation Map
0.23 0.36
0.0 0.5 1.00
400
800
1200Salt and Pepper
Coun
ts
OSI0.0 0.5 1.00
500
1000
OSI
Coun
ts
0.0 0.5 1.00
400
800
1200
OSI
Coun
ts
0.0 0.5 1.00
500
1000
Orientation Map
OSI
Cou
nts
Control STDP
Simulation Results
≈10%
≈56%
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Simulation Resultsn
i=n
i
0(1+2 r cos(q-qi ))
nj=n
j
0(1+2 r cos(q-qj ))
< wij>
ni
0nj
0(1+2 r2 cos(qi-q
j ))
● More selective systems generate more modulate interactions, but this goes AGAINST an increase in selectivity
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ε: Reconnection probability between neurons
εcE εcI
+
-
Reconnection RuleSimulation Results
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Reconnection Rule
As predicted by the model, functional connectivity decreases orientation
selectivity.
Simulation Results
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Reconnection Rule
With Reconnection
Without Reconnection
0.5
0.6
0.7
OS
IIf the balanced state becomes unstable, selectivity can be increased by generating functional connectivity in the excitatory interactions.
Simulation Results
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Conclusions
• Does functional connectivity improves orientation selectivity?• It depends on the dynamical state!
• In the balanced state selectivity increases by decreasing the modulation of the connectivity profile.
• Salt and Pepper is more selective than Orientation Map for the same feed-forward input.
• Since STDP increases the mean value of the connectivity profile, it improves selectivity.
• It is necessary to get more information about functional connectivity of inhibitory interactions
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Conclusions
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Scholl et al., 2013
Cat
Mouse
Retina LGN V1Layer 2/3
V1Layer 4