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Vidna kognicija III Danko Nikolić 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 New lecture Small delays in synchronization → 150 ms → 250 ms The speed of vision 20 ms per stage! 40-50 ms 30-50 ms 1 spike per neuron! 20-40 ms 50-70 ms 70-90 ms 80-100 ms Thorpe & Fabre-Thorpe (2001) What can one spike tell us? What can one spike tell us? Increase in stimulus intensity Kuffler (1953) Discharge patterns and functional organisation of vertebrate retina, Journal of Neurophysiology Stimulus onset “I don’t think Steve much liked our abstract”. A simulation test Van Rulen and Thorpe (2001) Rate Coding Versus Temporal Order Coding:What the Retinal Ganglion Cells Tell the Visual Cortex, Neural Computation, 13 Spike timing in sensory receptors Onset latencies in somatosensation D1 D2 D3 D4 D5 Stimulus onset Johansson & Birznieks (2004) Measuring small delays • Fitting a function and taking its maximum value for the estimate. Cosine fit Phase offsets can be measured with submillisecond precision Schneider and Nikolić, Journal of Neuroscience Methods (2006). Large networks 200 μm 200 μm Relative firing time [ms] Extraction of the firing sequence Relative firing time [ms] Nikolić, Journal of Comp. Neuroscience (2007) Schneider, Havenith and Nikolić, Neural Computation (2006) Schneider and Nikolić, Journal of Neuroscience Methods (2006) Non-parametric detection of temporal order Nikolić, Journal of Comp. Neuroscience (2007). Example: Stimulus dependence 1) 2) A B 20 ms Neuron ID Neuron ID 20 ms Time [ms] Time [ms] Firing sequences change dynamically Spike timing in single neurons: Synaptic integration Rall (1964) Spike timing in single neurons: Synaptic integration Rall (1964) Spike timing in single neurons: Synaptic integration Rall (1964) Spike timing in single neurons: Synaptic integration Rall (1964) Spike timing in single neurons: Synaptic integration Rall (1964) Spike timing in single neurons: Synaptic integration Rall (1964) Spike timing in single neurons: Synaptic integration Rall (1964) Spike timing in single neurons: Synaptic integration Euler & Denk (2004) Stiefel & Sejnowski (2007) 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. Good timing is everything Binding problem Perceptual integration and organization Hierarchical coding by extraction of feature combinations Grandmother cell 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. Perceptual organization through synchronization of action potentials Split bar experiment (Gray et. al, 1989) Perceptual organization through synchronization of action potentials Synchrony at different scales Conflicting bar experiment Engel, A.K., Koenig, P.& Singer, W. (1991); Kreiter, A.K. & Singer, W. (1996). Mechanisms: Tangential connections Higher brain areas and awake states An important role of attention - In early visual areas - In higher visual areas V4, MT. Infero-temporal cortex and recognition of faces Mechanisms of synchronization Mechanisms of detection Attention and rates in V4 • Modulation of rate firing rate responses in V4 • Moran & Desimone, 1985 Attention and synchrony in V4 - Fries et al., 2001 - Investigated strength of synchrony - Spike-triggered averages Delay period Stimulus period Infero-temporal cortex - Hirabayashi & Miyashita - Perception of faces Face Non-face - Synchronization is stronger when faces are perceived. Attention in early visual areas • Roelfsema et al., 2004 • Non-modulation of synchrony in V1 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 Bottom-up Common input Input is not shared Lateral interactions Tangential connections Top-down Lower visual area Higher visual area Feedback connections The brain as a liquidstate machine A thinking ocean is the main character in a famous science fiction novel by Stanislav Lem But how can a liquid possibly process information ? Common mistake: Trying to understand the brain from the perspective of the organization of a digital computer IBM Blue-Gene supercomputer Amsterdam-07 • Two obvious differences in the organization of computers and brains 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. 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.” „zero“ „one“ Amsterdam-07 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 input 1 15 -5 23 31 102 0 liquid • • • • • • • • 1+15 1/-5*23 Log(15*31) Sin(102)+Log(… … … … do it many, many times. readout Synaptic weights input liquid readout Synaptic weights primary visual cortex • • • • Parallel recordings by multiple Michigan probes. Up to 48 channels. Cat visual cortex, area 17 Anesthesia main result persistence of information temporal superposition XOR: code invariance correlations matter precise spike timing SD - jitter [ms] a subject to noise conclusions - 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. - 2nd order correlation: the advantage of using additional non-linear classification methods was limited to the use of pair-wise correlations between neurons. input t non-linear map readout Neuronal Avalanches in Vivo 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 P(size) P(size) Distribution of Avalanche-Sizes Size of avalanches P (size) ~ sizeα α Size of avalanches log P (n) ~ α log (n) = y ~ k x 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? Neuronal Avalanche of Spikes Size = 24 spikes Lifetime = 14 ms Methods • Recording: → Michigan Probes • Recording sites: → 3 cats: Area 17 → 1 cat: Area 17 & Area 21 Unit • Spontaneous activity under anesthesia • 7 datasets → Duration: 100 – 500 s • Units → Per dataset: 105 - 158 → Single units: 68 - 118 → Multi units: 26 - 50 ms Definition of Avalanches avalanche i + 1 (size = 3 spikes) Unit avalanche i (size = 14 spikes) Δt (ms) active bin blank bin 6 ms Power Law P (spikes) 1 0.1 0.01 0.001 0.0001 1 10 #spikes Δt: 4 ms Cat: Col05 – Probe1 100 Power law is independent of Δt • 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 Non - Power Law Dependence on Δt → 2 cases • Exponential-like distribution remains robust • With small Δt → power law / with large Δt → exponential function 1 P (spikes) 0.1 0.01 Cat: Col11 – Probe 1 0.001 Δt = 4 ms 0.0001 1 10 # spikes 100 Exponent -1.5 Exponent -1.7 -1.9 -2.1 -2.3 Cat: Col05 – Probe1 -2.5 1 2 3 4 5 6 7 Δt (ms) → Exponent increases with larger bin-sizes 8 Exponent – Δtavg Δtavg = mean of time intervals between spikes → statistical approach for optimal Δt → Separation and concatenation of avalanches is minimized 2.5 Δtavg 2 1.5 1 0.5 0 0 20 40 60 Δt 80 100 Exponent (Δtavg) ~ -1.8 1 P(spikes) 0.1 0.01 0.001 Col05 I (-1.84) Col05 II (-1.87) 0.0001 Nat03 17 (-1.84) 0.00001 1 10 # Spikes 100 Lifetime Lifetime distribution of avalanches does not follow a power law in any of the probes, irrespective of Δt. Cat: Col05 – Probe 1 P (lifetime) 1 0.1 1 ms 0.01 2 ms 4 ms 8 ms 0.001 1 10 Lifetime [ms] 100 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