coding with dynamic synapses and receptive fields · possible roles of short-term plasticity...
TRANSCRIPT
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Benjamin Lindner MPI für Physik komplexer Systeme Dresden
André Longtin
Jason Middleton, John Lewis, Len Maler
Jakub Cieniak, Dorian Gangloff
Physics, Cellular and Molecular MedicineCenter for Neural Dynamics
University of Ottawa
Coding with Dynamic Synapses and Receptive Fields
Funding by NSERC, CIHR, MPI
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Two Parts
• Both motivated by our work on weakly electric fish
• Two general coding principles
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Part 1: Synchrony and Receptive Fields
•Motivation: Electrosensory communication
•Synchrony data
•Neural modeling of decoding
•Synchrony decoded with large receptive fields
Middleton, Longtin, Benda, Maler, J. Neurophysiol. (2009)
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Electrosensory system
ElectricOrgan
Weakly electric fish(brown ghost)
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EOD full cycle
From Brian Rasnow, Caltech
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Electrosensory Lateral Line Lobe (ELL)
Electroreceptors
PyramidalCell
Higher Brain
Amplitude Modulation (AM)
Krahe and Gabbiani (2004) Nat. Neurosci.Rev. 5:13-23
afferents
Electrolocation
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Beat patterns due to neighbors
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Parallel Fish
Kelly, Babineau, Longtin, Lewis, Biol. Cybern. 2008
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ELECTRORECEPTORS
• � ALL SPATIO-TEMPORAL SCALES
• The EOD field excites 16,000 cutaneous electroreceptors.
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Electrocommunication
Female: EOD <800 Hz Male: EOD >800 Hz
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Same gender interactions: Calls synchronize receptors
Benda, Longtin, Maler, J. Neurosci. 2005
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Female-Male Interactions: Calls desynchronize receptors
Benda, Longtin, Maler, Neuron 2006
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Leaky Integrate-and-fire Model with Dynamic ThresholdChacron, Longtin, St-Hilaire, Maler, Phys.Rev.Lett. 85, 1576 (2000)
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How are changes in synchrony decoded?
Model of receptors
Models of ELL pyramidal cells driven by receptor data
Eventually include short-term plasticity between them
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Spectral measuresFourier transform
Cross spectra of synaptic input/voltage and input signal
Coherence functions
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Data: synchronous spike coherence
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Postsynaptic Decoders
• 3 Somatotopic maps:
• Centro-medial (CMS)
• Centro-lateral (CLS)
• Lateral (LS)
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Cerebellar granule cells
ELL
Pyramidal cells
GABA interneurons
LS
CLS
CMS
Electroreceptors
Maps
columns
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Receptive Fields
CLS map: intermediate
Electric image
Prey
RF
x
y
z
LS map: large receptive fields=low spatial frequency
CMS map: small receptive fields=high spatial frequency
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Mehaffey, et al. (2008)
cohe
renc
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low-pass
high-pass
Temporal Filtering Properties
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Neural models:experimental constraints
Neural models:experimental constraints
Different receptive field (RF) sizes:LS - large RFs (high convergence)CMS - small RFs (low convergence)
Different spike thresholds:LS - high threshold (-67mV)CMS - low threshold (-61 mV)
Output firing rates are roughly conserved across maps:LS - 18 HzCMS - 14 Hz
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input
threshold
Neural models: current threshold modelNeural models: current threshold model
Low convergencelow threshold
High convergencehigh threshold
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input
output
voltage
Cdv(t)
dt= IDC − gshunt v(t) − Eshunt( )− gsynx(t) v(t) − EAMPA( )
Conductance-based model
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Mehaffey, et al. (2008)
cohe
renc
e
low-pass
high-pass
Temporal Filtering Properties
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Synchronous (electro)sensory afferent activity encodes high frequency information
Summed activity encodes all frequencies
Postsynaptic cells with high convergence and high spike threshold preferentially decode synchronous activity
ELL: high convergence map (LS) decodes fast chirps
Other sensory systems (visual: X and Y cells) could consist of parallel streams of different temporal information which are determined by transmission of synchronous activity
SummarySummary
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Part 2: Coding with Plastic Synapses
• General properties of short term plasticity• Amplitude-rate picture• Frequency response picture• Spontaneous Poisson activity• Modulated Poisson activity• Controlling Broadband Coding
• Lindner, Gangloff, Longtin, Lewis,
• Broadband Coding with Dynamic Synapses, J. Neurosci. (2009)
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Short-term plasticity
1mV
100ms
Change in the synaptic efficacy by incoming spikes
Increase in efficacy = synaptic facilitation
Decrease in efficacy = synaptic depression
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Possible roles of short-term plasticity
•input compression (Tsodyks & Markram 1997, Abbott et al. 1997)
•signaling of transients (Lisman 1997, Senn et al. 2000, Richardson et al. 2005)
•switching between neural codes (Tsodyks & Markram 1997)
•spectral filtering (Fortune&Rose 2001, Abbott et al. 1997, Dittman et al. 2000)
•synaptic amplitude can keep info about the presynaptic spike train seen so far (e.g. Fuhrmann et al. 2001)
•redundancy reduction (Goldman et al. 2002)
•sensory adaptation and decorrelation (Chung et al. 2002)
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Information transfer
•Need more than synaptic amplitudes
•One also needs accompanying noise
•Noise comes mainly from (asynchronous) inputs
•Need to figure out how synaptic amplitudes and noise depend on time (due to signal)
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Facilitation-depression model from experiments
1mV
100ms
Postsynaptic amplitude
Lewis &Maler J. Neurophysiol. (2002,2004)
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Facilitation and Depression Dynamics
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Trajectories for Poisson stimulus
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Model
Poisson input spike trains
F-D
F-D
Synaptic facilitation and
depression
.
.
.
Synapticinput
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Conductance and voltage dynamics
Synaptic inputs
Conductance dynamics
Membrane voltage dynamics
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Spontaneous activity
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Model for spontaneous activity
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Map description
input ISI
Facilitiation (for small input rates)
Depression
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Mean value for low input rates
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Distinction between different regimes
• Facilitation dominated regime (FDR)
• Depression dominated regime(DDR)
At low firing input rate
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Distinction between different regimes
When does facilitation dominate? And when depression?
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Power spectra
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Power spectra
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Signal transmission
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Model with rate modulation
Modulation of the input firing rate by a band-limited Gaussian white noise (0-100Hz)
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0 < COHERENCE < 1
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Input=electrical stimulus Output= ELL spikes
How can one infer receptor-to-ELL plasticity?
Chacron et al., Nat. Neurosci. 2005
C1=C2 C3
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Depression alone
Szalisznyo, Longtin, Maler, Biosystems 2008
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Facilitation alone
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Prediction that D dominates, mixed AMPA+NMDA
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Band-limited noise stimulus (0-100Hz)
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Cross-spectra
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Coherence function DDR
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Coherence function FDR
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Input: Poisson rate modulationOutput: LIF spikes
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Summary‣Analytical results for the spontaneous case permit distinction between different regimes (FDR & DDR)
‣Synaptic input and subthreshold membrane voltage show a flat coherence with rate modulation for both
FDR and DDR-> broadband coding
‣Information transmission about a stationary rate modulation is always reduced by dynamic synapses
‣Coherence can be controlled by LIF mean rate
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Chaos
Special Issue
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3rd University of OttawaComputational Neuroscience
Summer School
June 7-20, 2009
http://www.neurodynamic.uottawa.ca