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

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

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

4. Neurosciences (Neuro-IT): Funded projects Joint Activities for future Research Neuro-IT.netNeuro-IT.net - Neuro-IT Net: Thematic Network Neuroinformatics for living artefacts projects AMOUSEAMOUSE - Artificial Mouse ARTESIMITARTESIMIT - Artefact Structural Learning through Imitation INSIGHT 2+INSIGHT 2+ - 3D Shape and material properties and recognition MIRRORMIRROR - Mirror Neurons based Robot Recognition POETICPOETIC - Reconfigurable Poetic Tissue SIGNALSIGNAL - Systemic Intelligence for Growingup Artifacts that Live AMOTHAMOTH - A fleet of articifical chemosensing moths for distributed environmental monitoring BIBABIBA - Bayesian Inspired Brain and Artefacts: Using probabilistic logic to understand brain function and implement life-like behavioural co-ordination ECOVISIONECOVISION - Artificial vision systems based on early coginitive cortical processing HYDRAHYDRA - Living Building blocks for self-designing artefacts PALOMAPALOMA - Progressive and adaptive learning of object manipulation: a biologically inspired multi-network architecture Neuroinformatics projects managed in Directorate General Research MICROCIRCUITSMICROCIRCUITS - Cortical, Cerebellar and Spinal Neuronal Networks - Towards an interface of computational and experimental analysis CEREBELLUMCEREBELLUM - Computation and Plasticity in the Cerebellar System: Experiments, Modeling and Database FET Projects related to Neuron on Silicon NACHIP NACHIP - DeveloPment of A Neuro-semiconductor Interface wiht recombinant sodium CHannels NEUMICNEUMIC - Neurons adn Modified CCMOS integrated Circuit interfacing INPROINPRO - Information Processing by Natural Neural Networks NEUROBITNEUROBIT - 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 ALAVLSI Attend-to-learn and learn-to-attend with neuromorphic, analogue VLSI APERESTAPEREST - Approximately Periodic Representation of Stimuli BIOLOCHBIOLOCH - Bio-mimetic Structures for Locomotion in the human body CAVIARCAVIAR - Convolution AER vision architecture for real-time CICADACICADA - Cricket and spider inspired perception and autonomous decision automata CIRCECIRCE - Chiroptera-Inspired Robotic Cephaloid: a Novel Tool for Experiments in Synthetic Biology CYBERHANDCYBERHAND - Development of a Cybernetic hand prosthesis LOCUST LOCUST - Life-like Object Detection For Collision Avoidance Using Spatio-temporal Image MIRRORBOTMIRRORBOT - Biomimetic multimodal learning in a mirror neuron-based robot ROSANAROSANA - Representation of stimuli as neural activity SENSEMAKERSENSEMAKER - A multi-sensory, task-specific, adaptable perception system SPIKEFORCESPIKEFORCE - 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. Systems of spin-like elements may dynamically relax governed by the Hamiltonian towards increasingly complex “discrete” attractor states (Ising model)ferromagnetic (e.g., S.K. model)+ spin-glass state (Hopfield model)+ memory states ABC D

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

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

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

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

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

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

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

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

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

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

21.         mammalian species         10 6 10 7 10 8 10 9  yrs A Simplified History of Cortical Complexity lizard CA1 CA3 DG platypus echidna infinite recursion 1 2 3 Where is Intelligent Design ? Hippocampus Cortex reptilians

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

23. 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 SimulationsSimulations which include a model of neuronal fatigue the Braitenberg-Potts semantic network of

24. Latching forward and forward…

25. Systematic simulations indicate a latching phase transition pl plpl

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

27. 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 BLISS (M Katran, M Nikam, S Pirmoradian with guidance by Giuseppe Longobardi) 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 + semantic correlations 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 VP  (Neg) (AdvP) V’ V’  V DP | V S’ | V PP PP  Prep DP ? ≠ Medium Term:

28. Not just with language

29. Francesco Battaglia thinks he has an algorithm..

30. 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 DP12probability 0.5, 0.5 PPV -> PrepV DP11 | PrepV DP12 probability 0.5, 0.5 PPT -> PrepT DP11 | PrepT DP12probability 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 BLISS: so far, we have implemented only syntax

31. 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

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