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Vidna kognicija III Danko Nikolić
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Teme Neurofiziološki kodovi prijenosa i obrade informacija u vidnom sustavu Dva kôda za percepciju svjetline Problem povezivanja dijelova vidne scene u cjelinu (tzv. binding problem) Uloga pažnje u pohranjivanju informacija u radno pamćenje Uloga radnog pamćenja za formiranje dugoročnog vidnog pamćenja Mehanizmi sinestezijskih asocijacija
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New lecture Small delays in synchronization
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→ 150 ms → 250 ms
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20 ms per stage! 1 spike per neuron! The speed of vision Thorpe & Fabre-Thorpe (2001) 20-40 ms 30-50 ms 40-50 ms 50-70 ms 70-90 ms 80-100 ms
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What can one spike tell us?
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Kuffler (1953) Discharge patterns and functional organisation of vertebrate retina, Journal of Neurophysiology Increase in stimulus intensity Stimulus onset
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“I don’t think Steve much liked our abstract”.
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Van Rulen and Thorpe (2001) Rate Coding Versus Temporal Order Coding:What the Retinal Ganglion Cells Tell the Visual Cortex, Neural Computation, 13 A simulation test
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Spike timing in sensory receptors
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Onset latencies in somatosensation Johansson & Birznieks (2004) D1 D2 D3 D4 D5 Stimulus onset
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Measuring small delays Fitting a function and taking its maximum value for the estimate. Cosine fit
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Phase offsets can be measured with sub- millisecond precision Schneider and Nikolić, Journal of Neuroscience Methods (2006).
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Large networks 200 μm
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Relative firing time [ms]
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Extraction of the firing sequence Schneider, Havenith and Nikolić, Neural Computation (2006) Relative firing time [ms] Nikolić, Journal of Comp. Neuroscience (2007) Schneider and Nikolić, Journal of Neuroscience Methods (2006)
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Non-parametric detection of temporal order Nikolić, Journal of Comp. Neuroscience (2007).
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Example: Stimulus dependence 1)2)
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Firing sequences change dynamically
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Spike timing in single neurons: Synaptic integration Rall (1964)
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Spike timing in single neurons: Synaptic integration Rall (1964)
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Spike timing in single neurons: Synaptic integration Rall (1964)
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Spike timing in single neurons: Synaptic integration Rall (1964)
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Spike timing in single neurons: Synaptic integration Rall (1964)
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Spike timing in single neurons: Synaptic integration Rall (1964)
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Spike timing in single neurons: Synaptic integration Rall (1964)
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Spike timing in single neurons: Synaptic integration Euler & Denk (2004) Stiefel & Sejnowski (2007)
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Conclusion: Firing sequences Short time delays can serves as a code for carrying stimulus-related information that is as reliable as is the neuronal firing rate. Stronger synchronization increases the reliability of the code.
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Good timing is everything
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Binding problem
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Perceptual integration and organization
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Hierarchical coding by extraction of feature combinations Grandmother cell
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Problems: combinatorial explosion and novel combinations Combinatorial explosion: There are many different grandmothers and each can be seen from many different perspectives. Novel combinations: Some grandmothers are seen for the first time. There is no chance to learn all the possible combination of features that make a grandmother.
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Perceptual organization through synchronization of action potentials
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Split bar experiment (Gray et. al, 1989)
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Perceptual organization through synchronization of action potentials
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Synchrony at different scales
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Conflicting bar experiment Engel, A.K., Koenig, P.& Singer, W. (1991); Kreiter, A.K. & Singer, W. (1996).
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Mechanisms: Tangential connections
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Higher brain areas and awake states An important role of attention Infero-temporal cortex and recognition of faces - In early visual areas - In higher visual areas V4, MT. Mechanisms of synchronization Mechanisms of detection
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Attention and rates in V4 Modulation of rate firing rate responses in V4 Moran & Desimone, 1985
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- Fries et al., 2001 - Investigated strength of synchrony - Spike-triggered averages Attention and synchrony in V4 Delay period Stimulus period
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Infero-temporal cortex FaceNon-face - Hirabayashi & Miyashita - Perception of faces - Synchronization is stronger when faces are perceived.
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Attention in early visual areas Roelfsema et al., 2004 Non-modulation of synchrony in V1
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Mechanisms For large part unknown To a high degree theoretical answers Models, simulations Three types of mechanism are considered: – bottom-up – lateral interactions – top-down
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Bottom-up Common input Input is not shared
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Lateral interactions Tangential connections
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Top-down Lower visual areaHigher visual area Feedback connections
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The brain as a liquid- state machine
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A thinking ocean is the main character in a famous science fiction novel by Stanislav Lem But how can a liquid possibly process information ?
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Common mistake: Trying to understand the brain from the perspective of the organization of a digital computer Amsterdam-07 IBM Blue-Gene supercomputer
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Two obvious differences in the organization of computers and brains Amsterdam-07 A computer requires a program (it implements a Turing machine). A brain does not have a program: Instead it has to rely on learning. It may be viewed as a genetically encoded network of learning-agents.
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Amsterdam-07 A literal interpretation of liquid computing Fernando and Sojakka: Pattern recognition in a bucket: A real liquid brain, ECAL 2003: “This paper demonstrates that the waves produced on the surface of water can be used as a medium for a “Liquid State Machine”. We made a bucket of water, vibrated it with lego motors, filmed the waves with a webcam and put it through a perceptron on matlab and got it to solve the XOR problem and do speech recognition.” „one“ „zero“
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Why did we call this style of computing liquid computing ? A cortical circuit is viewed here as a special case of a Liquid State Machine
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1 15 -5 23 31 102 0 1+15 1/-5*23 Log(15*31) Sin(102)+Log(… … … … do it many, many times. readoutliquidinput
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time inputliquidreadout
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primary visual cortex Parallel recordings by multiple Michigan probes. Up to 48 channels. Cat visual cortex, area 17 Anesthesia
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main result
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persistence of information
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temporal superposition XOR:
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code invariance
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correlations matter
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precise spike timing SD - jitter [ms]
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a subject to noise
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conclusions input non-linear map readout t - memory: information about previously shown images is available for a prolonged period of time. - superposition: information about previously and currently shown stimuli is available simultaneously. - non-linearity: information about non-linear transformations of input properties can be extracted by linear classifiers. - rates and timing: information is coded partially in neuronal firing rates and partially in the precise timing of neuronal spiking activity. - 2 nd order correlation: the advantage of using additional non-linear classification methods was limited to the use of pair-wise correlations between neurons.
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Neuronal Avalanches in Vivo
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European avalanche-size table 1 - Slough Small snow slide that cannot bury a person. length <50 m volume <100 m³ 2 - Small Stops within the slope. length <100 m volume <1,000 m³ 3 - Medium Runs to the bottom of the slope. length <1,000 m volume <10,000 m³ 4 – Large Runs over flat areas, may reach the valley bottom. length >1,000 m volume >10,000 m³
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Distribution of Avalanche-Sizes P (size) ~ size α log P (n) ~ α log (n) = y ~ k x α
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Power law in complex systems: Earthquakes, forest fires, evolution of species Size of US cities, citations of papers Does the brain generate neuronal avalanches with power law statistics ? →How do we imagine such “neuronal” avalanches? →How do we observe them in the brain?
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Neuronal Avalanche of Spikes Size = 24 spikesLifetime = 14 ms
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Methods Recording: → Michigan Probes Recording sites: → 3 cats: Area 17 → 1 cat: Area 17 & Area 21
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Spontaneous activity under anesthesia 7 datasets → Duration: 100 – 500 s Units → Per dataset: 105 - 158 → Single units: 68 - 118 → Multi units: 26 - 50
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Definition of Avalanches Δ t (ms) 6 ms
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Power Law Cat: Col05 – Probe1 Δ t: 4 ms
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Power law is independent of Δ t Δt was varied between 1 and 10 ms →Number of extracted avalanches decreases with larger Δt →Number of larger avalanches increases relative to smaller ones Power law remains stable irrespective of Δt ! Exponent of power law increases with Δt
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Non - Power Law Cat: Col11 – Probe 1 Δt = 4 ms Dependence on Δt → 2 cases Exponential-like distribution remains robust With small Δt → power law / with large Δt → exponential function
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Exponent Cat: Col05 – Probe1 Δ t (ms) → Exponent increases with larger bin-sizes
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Exponent – Δ t avg Δt avg = mean of time intervals between spikes → statistical approach for optimal Δ t → Separation and concatenation of avalanches is minimized
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Exponent ( Δ t avg ) ~ -1.8
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Lifetime Cat: Col05 – Probe 1 Lifetime distribution of avalanches does not follow a power law in any of the probes, irrespective of Δt.
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Conclusions Neuronal avalanches defined by spikes → Power law in some size-distributions → Exponent ~ -1.8 → No Power law in other size - distributions → No Power law in lifetime distributions Interpretation → Self-organized criticality (SOC) → Critical branching processes
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