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 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
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 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 - 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
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 - the 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
J ij  constant
(Ising model)
ferromagnetic
J ij  disordered
(e.g., S.K. model)
+ spin-glass state
J ij    i  j
(Hopfield model)
+ memory states

A
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’
CA3 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
(+) +
n
(+) +
Lamination, Arealization
DG input sparsifier
CA1 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
CA3
2
1
lizard
109
CA1
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 
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:
BLISS
(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
+ 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
sing inflection of verb)
(note singular negation removes
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
Not just with language
Francesco Battaglia thinks he has an algorithm..
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
Sahar
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,
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 woma
teachers of the man know whether these trees sit in the wonderful cat with
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 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