1 / 32

Brain-inspired ICT

Brain-inspired ICT. memory. A perspective from outside: SISSA-CNS Trieste Alessandro Treves. Brain-inspired ICT, page II. Noam Chomsky. Lord Adrian. analog. discrete. memory. i.e., attractors ?. Integrated Projects funded under the FP6 Bio-i 3 Proactive Initiative.

alodie
Download Presentation

Brain-inspired ICT

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Brain-inspired ICT memory A perspective from outside: SISSA-CNS Trieste Alessandro Treves

  2. Brain-inspired ICT, page II Noam Chomsky Lord Adrian analog discrete memory i.e., attractors ?

  3. IntegratedProjects funded under the FP6 Bio-i 3 Proactive Initiative CILIA - Customized Intelligent Life-inspired Arrays FACETS (Fast Analog Computing with Emergent Transient States) Daisy (Neocortical Daisy Architectures and Graphical Models for Context-Dependent Processing) +Projects funded under FP6 in the area of Neuro-IT… Joint Activities for future Research Neuro-IT.net - Neuro-IT Net: Thematic Network Neuroinformatics for living artefacts projects AMOUSE - Artificial Mouse ARTESIMIT - Artefact Structural Learning through Imitation INSIGHT 2+ - 3D Shape and material properties and recognition MIRROR - Mirror Neurons based Robot Recognition POETIC - Reconfigurable Poetic Tissue

  4. Neurosciences (Neuro-IT): Funded projects Joint Activities for future Research Neuro-IT.net - Neuro-IT Net: Thematic Network Neuroinformatics for living artefacts projects AMOUSE - Artificial Mouse ARTESIMIT - Artefact Structural Learning through Imitation INSIGHT 2+ - 3D Shape and material properties and recognition MIRROR - Mirror Neurons based Robot Recognition POETIC - Reconfigurable Poetic Tissue SIGNAL - Systemic Intelligence for Growingup Artifacts that Live AMOTH - A fleet of articifical chemosensing moths for distributed environmental monitoring BIBA - Bayesian Inspired Brain and Artefacts: Using probabilistic logic to understand brain function and implement life-like behavioural co-ordination ECOVISION - Artificial vision systems based on early coginitive cortical processing HYDRA - Living Building blocks for self-designing artefacts PALOMA - Progressive and adaptive learning of object manipulation: a biologically inspired multi-network architecture Neuroinformatics projects managed in Directorate General Research MICROCIRCUITS - Cortical, Cerebellar and Spinal Neuronal Networks - Towards an interface of computational and experimental analysis CEREBELLUM - Computation and Plasticity in the Cerebellar System: Experiments, Modeling and Database FET Projects related to Neuron on Silicon NACHIP- DeveloPment of A Neuro-semiconductor Interface wiht recombinant sodium CHannels NEUMIC - Neurons adn Modified CCMOS integrated Circuit interfacing INPRO - Information Processing by Natural Neural Networks NEUROBIT - A bioartificial brain with an artificial bosy: traininga cultured neural tissue to support the purposive behavior of an artificual body Neuroinformatics LPS (Life-Like Perception-systems) projects ALAVLSI Attend-to-learn and learn-to-attend with neuromorphic, analogue VLSI APEREST - Approximately Periodic Representation of Stimuli BIOLOCH - Bio-mimetic Structures for Locomotion in the human body CAVIAR - Convolution AER vision architecture for real-time CICADA - Cricket and spider inspired perception and autonomous decision automata CIRCE - Chiroptera-Inspired Robotic Cephaloid: a Novel Tool for Experiments in Synthetic Biology CYBERHAND - Development of a Cybernetic hand prosthesis LOCUST- Life-like Object Detection For Collision Avoidance Using Spatio-temporal Image MIRRORBOT - Biomimetic multimodal learning in a mirror neuron-based robot ROSANA - Representation of stimuli as neural activity SENSEMAKER - A multi-sensory, task-specific, adaptable perception system SPIKEFORCE - Real-time spiking networks for robot control

  5. Abeles et al have “seen” attractor states rumbling in monkey recordings 1995 - the theoretical expectation had been laid out in Europe++…

  6. (Ising model) ferromagnetic (e.g., S.K. model) + spin-glass state (Hopfield model) + memory states Systems of spin-like elements may dynamically relax governed by the Hamiltonian towards increasingly complex “discrete” attractor states A B C D

  7. ArealizationandMemory in the Cortex monkey Main theoretical perspectives: a) Content-based b) Hierarchical c) Statistical/modular

  8. Object 1 in position 1 Object 2 in position 1 Object 1 in position 2 Neocortex poses the complication of topographic maps.. ..use the sheet to code position... Discrete attractors, with units arranged in a cortical network

  9. THE HIPPOCAMPUS H a structure which remains stable and self-similar across mammalian species human opossum DG H monkey

  10. the (early) David Marr view, the basis for reverse engineering the hippocampus (diagram by Jaap Murre, 1996)

  11. context position identity Object 1 in position 1 Object 2 in position 1 Object 1 in position 2 Freedom from topography stimulates creativity ..use multiple charts to code environments... ..use the sheet to code position... Discrete attractors, with units arranged in a cortical network

  12. Stefan and Jill Leutgeb (2004) find ideals nearly realized in the real brain A B C ’globalremapping’ CA3 firing patterns seem to fall into a discrete number of continuous attractors, with minimal overlap

  13. Karel Jezek (Moser lab, SPACEBRAIN EU project)

  14. AMOUSE …where the heck am I? …here ? or maybe there?

  15. The statistical/modular perspective The Braitenberg model N pyramidal cells √N compartments √N cells each A pical synapses B asal synapses

  16. Striatal Networks

  17. Cerebellar Networks

  18. Climbing fibers: The one case of an almost private teacher in the brain

  19. Neocortex (+)n+ Lamination, Arealization Who is cutting-edge, in cerebellar technology? Hippocampus (+)n+ DG input sparsifier CA1 feed-forward Cerebellum ++- Expansion recoding, Private teachers Basal ganglia -(-)- Massive funnelling Tonic output firing Tectum Olfactory bulb Spinal cord Computational paradigms 100’s Myrs old that we fail to understand

  20. 106 107 3 1 2 yrs platypus echidna DG 108 CA3 CA1 lizard 109 A Simplified History of Cortical Complexity infinite recursion         mammalian species         Where is Intelligent Design ? Hippocampus Cortex reptilians

  21. The statistical/modular perspective The Braitenberg model N pyramidal cells √N compartments √N cells each A pical synapses B asal synapses

  22. Simulations which include a model of neuronal fatigue show that the Braitenberg-Potts semantic network can hop from global attractor to global attractor: Latching dynamics Simulations which include a model of neuronal fatigue Simulations of the Braitenberg-Potts semantic network

  23. Latchingforward and forward…

  24. Systematic simulations indicate a latching phase transition pl pl

  25. +L +L How might a capacity for indefinite latching have evolved? p p semantics  semantics  AM AM C S long-range conn  (local conn ) Storage capacity (max p to allow cued retrieval) pc  CS 2 Latching onset (min p to ensure recursive process) pl S ? a spontaneous transition to infinite recursion?

  26. Medium Term: to assess how the model can learn, we shall need a toy: a basic language including both syntax and semantics that should be scaled down to manageable proportions BLISS (M Katran, M Nikam, S Pirmoradian with guidance by Giuseppe Longobardi) + semantic correlations Syntactic structure S  DP I’ (singular/plural) I’  I VP I  (singular or plural form) VP  (Neg) (AdvP) V’ V’  V DP | V S’ | V PP PP  Prep DP S’  Compl S DP  Det NP | PropN NP  (AdjP) N” N”  (AdjP) N’ N’  N (PP) | N S’ Det  Art | Dem N  boy | girl | cat | dog | tiger | jackal | horse | cow | meat | hay | milk | wood | meadow | stick | fork | bowl | cart | table | house || boys | girls | cats | dogs | tigers | jackals | horses | cows | stables | sticks | forks | bowls | carts | tables | houses PropN  John | Mary || John and Mary V  chases | feeds | sees | hears | walks | lives | eats | dies | kills | brings | pulls | is || chase | feed | see | hear | walk | live | eat | die | kill | bring | pull | are | Compl  that | whether Prep  in | with | to | of | under Neg  does not || do not (note singular negation removes sing inflection of verb) Art  the | a(an) AdjP  red | blue | green | black | brown | white | yellow | slow | fast | rotten | fresh | cold | warm | hot AdvP  slowly | rapidly | close | far Dem  this | that || these | those ? ≠ VP  (Neg) (AdvP) V’ V’  V DP | V S’ | V PP PP  Prep DP

  27. Not just with language

  28. Francesco Battaglia thinks he has an algorithm..

  29. BLISS: so far, we have implemented only syntax S1 -> DP11 VP11 | DP12 VP12 probability 0.2 , 0.8 VP11 -> VR11 | Neg11 VR12 probability 0.8000 , 0.2000 VP12 -> VR12 | Neg12 VR12 probability 0.8000 , 0.2000 VR11 -> Vt11 DP | Vi11 | Vi11 PPV | Vtd11 SRd | Vti11 SRi | Vtdtv11 DP PPT probability 0.25, 0.05, 0.15, 0.2, 0.2, 0.15 VR12 -> Vt12 DP | Vi12 | Vi12 PPV | Vtd12 SRd | Vti12 SRi | Vtdtv12 DP PPT probability 0.25, 0.05, 0.15, 0.2, 0.2, 0.15 PP -> Prep DP11 | Prep DP12 probability 0.5, 0.5 PPV -> PrepV DP11 | PrepV DP12 probability 0.5, 0.5 PPT -> PrepT DP11 | PrepT DP12 probability 0.5, 0.5 SRd -> Conjd S1 probability 1 SRi -> Conji S1 probability 1 DP -> DP11 | DP12 probability 0.75 , 0.25 DP11 -> Det11 NP11 | PropN11 probability 0.8 , 0.2 DP12 -> Det12 NP12 | NP12 | PropN12 probability 0.495, 0.5 , 0.005 NP11 -> N11 | AdjP N11 | N11 PP | AdjP N11 PP probability 0.4 , 0.2, 0.2, 0.2 NP12 -> N12 | AdjP N12 | N12 PP | AdjP N12 PP probability 0.4 , 0.2, 0.2, 0.2 Det11 -> Art11 | Dem11 probability 0.83 , 0.17 Det12 -> Art12 | Dem12 probability 0.83 , 0.17 N11 -> man | woman | boy | girl | child | book | friend | mother | table | house | paper | letter | teacher| parent | window | wife | bed | cow | tree | garden | hotel | lady | cat | dog | horse | brother | husband | daughter | meat | milk | wood | fork | bowl | cart | farm | kitchen probability 0.053851, 0.051611, 0.050752, 0.049736, 0.048145, 0.046406, 0.04494, 0.044608, 0.043687, 0.035414, 0.034214, 0.032266, 0.030239, 0.029286, 0.028074, 0.02795, ., Sahar

  30. the red paper sits with hot cats on the strong windows wonderful friends don't sit for a short kitchen the hot houses on cows don't find John teachers find a horse those rotten pens in the cow live in a meat the dogs don't wonder whether pens of the meat of that girl of the house walk with long farms strong mothers give tables in that black meat to the red carts good boys hear Phoebe girls die for a strange dog kitchens with Mary give great husbands to the good houses these papers in mothers send the mother on the bed to the dogs in houses short dogs on farms of the hotels give Phoebe to the parents a cold lady with the women wonders whether Joe returns in a strange woman with the friends teachers of the man know whether these trees sit in the wonderful cat with tables the husbands come in friends girls with John find Joe Joe uses a old fork of the great beds of the parents in the strange bed of children of the garden John wonders whether the parents on trees with the bowls don't give a cart strange wives run in tables John sends Mary to Phoebe men send the hot dog to the horse

  31. How much constrained is syntactic dynamics? creativity We ask that with BLISS.. memory …we take the same measure from latching POTTS nets carabinieri

More Related