Braininspired ICT memory A perspective from outside SISSACNS

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

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

Integrated Projects funded under the FP 6 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 FP 6 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

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+ - 3 D 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 be 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 experimen CEREBELLUM - Computation and Plasticity in the Cerebellar System: Experiments, Modeling and Database FET Projects related to Neuron on Silicon NACHIP - Develo. Pment of A Neuro-semiconductor Interface wiht recombinant sodium CHannels NEUMIC - Neurons adn Modified CCMOS integrated Circuit interfacing INPRO - Information Processing by Natural Networks NEUROBIT - A bioartificial brain with an artificial bosy: traininga cultured neural tissue to support the purposive behavior of 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

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

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

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

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

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

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

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

Stefan and Jill Leutgeb (2004) find ideals nearly realized in the real brain A B C ’global remapping’ CA 3 firing patterns seem to fall into a discrete number of continuous attractors, with minimal overlap

Karel Jezek (Moser lab, SPACEBRAIN EU project)

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

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

Striatal Networks

Cerebellar Networks

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

Neocortex Hippocampus n (+) + Lamination, Arealization DG input sparsifier CA 1 feed-forward Cerebellum -(-)- Expansion recoding, Private teachers Basal ganglia Massive funnelling Tonic output firing Olfactory bulb ++- Who is cutting-edge, in cerebellar technology? Tectum Spinal cord Computational paradigms 100’s Myrs old that we fail to understand

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

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

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

Latching forward and forward…

Systematic simulations indicate a latching phase transition pl pl

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

Medium Term: to assess how the model can learn, we shall need a toy: (M Katran, M Nikam, S Pirmoradian a basic language including both syntax and semantics with guidance by Giuseppe Longobardi) that should be scaled down to manageable proportions BLISS + semantic correlations Syntactic structure S DP I’ (singular/plural) I’ I VP I (singular or plural form) VP (Neg) (Adv. P) V’ V’ V DP | V S’ | V PP PP Prep DP S’ Compl S DP Det NP | Prop. N NP (Adj. P) N” N” (Adj. P) 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 Prop. N 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 sing inflection of verb) (note singular negation removes Art the | a(an) Adj. P red | blue | green | black | brown | white | yellow | slow | fast | rotten | fresh | cold | warm | hot Adv. P slowly | rapidly | close | far Dem this | that || these | those

Not just with language

Francesco Battaglia thinks he has an algorithm. .

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 w 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 teachers of the man know whether these trees sit in the wonderful cat with the husbands come in friends girls with John find Joe uses a old fork of the great beds of the parents in the strange bed of c 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

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