cortex modeling and cortex-inspired computation

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Cortex modeling and cortex-inspired computation. Anders Lansner Dept of Computational Biology KTH and Stockholm University. Synopsis. Methods in neuronal network modeling Large-scale cortex model example Perspectives on modeling and brain-inspired computing. Goals. - PowerPoint PPT Presentation

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Biomimetic signal processingAnders Lansner
Large-scale cortex model example
Albanova Instrumentation Seminar
STOCKHOLM BRAIN INSTITUTE
1985 - …
Enables understanding of the brain
Brain-like/inspired algorithms and architectures
Beyond ”neural networks”, ”neurocomputing”
”Artificial brains” … on silicon
E.g. genetic fluorescent marking + confocal tracing of pathways
Livet et al. Nature Nov 2007
Albanova Instrumentation Seminar
STOCKHOLM BRAIN INSTITUTE
Microcircuits and networks
Albanova Instrumentation Seminar
STOCKHOLM BRAIN INSTITUTE
Ohm’s law:
Albanova Instrumentation Seminar
While considering a patch where such channels are situated H-H realized that one can formulate an electrical circuit diagram that captures its essential electro-chemical properties as a simple RC circuit.
1/R = G = g*y!!! Im = G(V-Em)
The capacitance is fairly constant, but the ”R” is really quite dynamic and most likely depends on voltage as well as on chemical transmitters near the patch.
In fact, there may be several types of ion channels in the patch resulting in a mixture of currents with different properties and this may be represented by a ”parallel conductance model”.
Note that also other excitable cells of e.g. muscle, heart, pancreas can be described in much a similar fashion.
STOCKHOLM BRAIN INSTITUTE
The gate model
Albanova Instrumentation Seminar
Pioneers in this filed are Alan Hodgkin and Andrew Huxley (Nobel prize in medicine 1963).
The ”gate model” they formulated goes like this...
You consider a population of ion channels of a similar kind sitting in a small patch of membrane with the same electrical potential and chemical surroundings.
...
The (V) and (V) are what distinguishes different channels. Ligand gating can be described by much the same formalism.
STOCKHOLM BRAIN INSTITUTE
Albanova Instrumentation Seminar
Hodgkin-Huxley also formulated a useful current equation based on Kirschoffs law. If we consider one place along this structure the current is the sum of at least three components. This is formulated by this equation.
If you have a complex neuron you realize you will get a large number of coupled equations.
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An action potential
Nobel Prize 1963
Albanova Instrumentation Seminar
STOCKHOLM BRAIN INSTITUTE
Postsynaptic receptor kinetics
Huge numbers
Computing power limitation …
Albanova Instrumentation Seminar
STOCKHOLM BRAIN INSTITUTE
Albanova Instrumentation Seminar
STOCKHOLM BRAIN INSTITUTE
”LTP”
Cell assembly = mental object
Top-down driven model of associative memory
Generic “association cortex”, layers 2/3
Modular: Minicolumns, hypercolumns
3 different cell types: Pyramidal cells, Basket cells, Regular Spiking Non-Pyramidal
2 000 – 20 000 000 model neurons
117% 2.5 mV
230% 0.30 mV
Sparse connectivity (stochastic)
Active basket cell
Active RSNP cells
Albanova Instrumentation Seminar
STOCKHOLM BRAIN INSTITUTE
8K nodes, co-processor mode
used 360 MB memory/node
Setup time = 6927 s
>29000 cpu hours
77 % of linear speedup
Currently (inofficial) world record!
Djurfeldt M, Lundqvist M, Johansson C, Rehn M, Ekeberg Ö, and Lansner A (2007): Brain-scale simulation of the neocortex on the Blue Gene/L supercomputer. IBM J R&D (in press)
Albanova Instrumentation Seminar
STOCKHOLM BRAIN INSTITUTE
3 sec simulation
5 s = 600 s on PC
Lundqvist M, Rehn M, Djurfeldt M and Lansner A (2006). Attractor dynamics in a modular network model of the neocortex. Network: Computation in Neural Systems: 17, 253-276
Albanova Instrumentation Seminar
STOCKHOLM BRAIN INSTITUTE
Pattern reconstruction
Cortical long-range recurrent excitation strong enough to support attractor dynamics
Albanova Instrumentation Seminar
STOCKHOLM BRAIN INSTITUTE
Albanova Instrumentation Seminar
STOCKHOLM BRAIN INSTITUTE
Bimodal membrane potential
Jeffrey Anderson, Ilan Lampl, Iva Reichova, Matteo Carandini, and David Ferster. Stimulus dependence of two-state fluctuations of membrane potential in cat visual cortex. Nat. Neurosci., 3(6):617–621, 2000.
Log(pISI)
Bistable activity with irregular firing, similar to in vivo recordings
Ground state stable only in larger networks with many patterns stored
Increase in irregularity in active cortical states is a challenges for persistent activity models
This L2/3 network model
operates in a high-conductance regime of balanced excitatory and inhibitory currents
is stable to synchronization even with blocked NMDAR
Details under investigation
Albanova Instrumentation Seminar
STOCKHOLM BRAIN INSTITUTE
Attractor activation correlates with percentage of correct probe detections
Time scales different but qualitatively similar results
Albanova Instrumentation Seminar
STOCKHOLM BRAIN INSTITUTE
Pyramidals, cortico-cortical connections
Non-orthogonal stored memories
Modelling interacting areas … using parallel NEURON
Scalable abstract connectionist cortex model
Cortical area module, on-line learning, network-of-networks,…
Albanova Instrumentation Seminar
STOCKHOLM BRAIN INSTITUTE
SSF/Stockholm Brain Institute (SBI)
Albanova Instrumentation Seminar
STOCKHOLM BRAIN INSTITUTE
Brain mysteries likely to be largely uncovered at that time
A principled understanding of brain function will emerge
Great benefits!

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