synthetic pathways to bio-inspired information processing

8/8/2019 Synthetic pathways to bio-inspired information processing

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SEVENTH FRAMEWORK PROGRAME

THEME [ICT-2007.8.0]

BION: Synthetic pathways to bio-

inspired information processing

SEVENTH FRAMEWORK PROGRAME

THEME [ICT-2007.8.0]

BION: Synthetic pathways to bio-

inspired information processing

Victor Erokhin

Department of Physics

University of Parma

Italy

Victor Erokhin

Department of Physics

University of Parma

Italy


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Victor Erokhin2

Agenda / contentAgenda / content

> Objectives

> Consortium

> Starting point

> Elements and networks

> Working plan

> Objectives

> Consortium

> Starting point

> Elements and networks

> Working plan


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BION: ObjectivesBION: Objectives

> The main objective of the project is the realization of a new,highly innovative technology for the production of functionalmolecular assemblies, which can perform advanced tasksof information processing involving learning and decisionmaking, and which can be tailored down to the nanoscale.

> Project milestones

Fabrication and study of the nodes of the matrix, individuallyand in simple forms of the matrix; first application to abiological system.

Fabrication of the complex matrix:Alternative pathways

Transfer of data from biological systems and application ofArtificial Intelligence techniques

> The main objective of the project is the realization of a new,highly innovative technology for the production of functionalmolecular assemblies, which can perform advanced tasksof information processing involving learning and decisionmaking, and which can be tailored down to the nanoscale.

> Project milestones

Fabrication and study of the nodes of the matrix, individuallyand in simple forms of the matrix; first application to abiological system.

Fabrication of the complex matrix:Alternative pathways

Transfer of data from biological systems and application ofArtificial Intelligence techniques


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BION: ConsortiumBION: Consortium

> 4 partners from 3 countries

Coordinator: University of Parma (Italy): Realization of elements andnetworks

University of Pisa (Italy): Synthesis of polymers, nanoparticles andcomposite materials

University of Warwick (UK): Modeling of functioning of nervous

systems, learning algorithms

Max-Planck-Gesellschaft z. Frderung der Wissenschaften e.V.(Germany): Brain anatomy, signal databases

> 4 partners from 3 countries

Coordinator: University of Parma (Italy): Realization of elements andnetworks

University of Pisa (Italy): Synthesis of polymers, nanoparticles andcomposite materials

University of Warwick (UK): Modeling of functioning of nervous

systems, learning algorithms

Max-Planck-Gesellschaft z. Frderung der Wissenschaften e.V.(Germany): Brain anatomy, signal databases


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BION: Starting pointBION: Starting point

> Hebbian rule: When an axon of cell A is near enough to excite cell B andrepeatedly or persistently takes part in firing it, some growth process ormetabolic change takes place in one or both cells such that A's efficiency, asone of the cells firing B, is increased

Integration of processing and memory properties for the network elements

Learning procedure of the developed network must be based on combined learningparadigm (supervised and unsupervised learning)

Very high level of parallel processing

> Hebbian rule: When an axon of cell A is near enough to excite cell B andrepeatedly or persistently takes part in firing it, some growth process ormetabolic change takes place in one or both cells such that A's efficiency, asone of the cells firing B, is increased

Integration of processing and memory properties for the network elements

Learning procedure of the developed network must be based on combined learningparadigm (supervised and unsupervised learning)

Very high level of parallel processing

Key element of the network must have

memristor-like characteristics


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BION: Elements and networksBION: Elements and networks

PANIS DPEOG

IG

ID

-400

-200

0

200

400

-1 -0.5 0 0.5 1

voltage (V)

current(nA)

0

200

400

600

800

1000

-1 -0.5 0 0.5 1

voltage (V)

current(nA)

Gate Differential0

100200300

400500600700

0 1000 2000 3000 4000

time (s)

current(nA)

-250

-200

-150

-100

-50

0

0 500 1000 1500 2000

time (s)

current(nA)

+ -

V. Erokhin, T. Berzina, and M.P. Fontana, J. Appl. Phys., 97, 064501 (2005).


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> IMITATING THE SNAIL LEARNING PROCESS

> MODEL ADAPTIVE NETWORK

> IMITATING THE SNAIL LEARNING PROCESS

> MODEL ADAPTIVE NETWORK

Main input (MI) corresponds to

the touchaction

Teaching input (TI) corresponds

to the taste action

Main input (MI) corresponds to

the touchaction

Teaching input (TI) corresponds

to the taste actionA.Smerieri, T.Berzina,V.Erokhin, and

M.P. Fontana, Mater. Sci. Engineer. C,

28, 18-22 (2008).

A.Smerieri, T.Berzina,V.Erokhin, and

M.P. Fontana, Mater. Sci. Engineer. C,

28, 18-22 (2008).

Out 1 (nA) Out 2 (nA)

Before training 120 32After training 65 124

V. Erokhin, T. Berzina, and M.P. Fontana, Cryst. Rep., 52, 159-166 (2007)V. Erokhin, T. Berzina, and M.P. Fontana, Cryst. Rep., 52, 159-166 (2007)


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> PEO PANI fibrillar networks after vacuum treatment> PEO PANI fibrillar networks after vacuum treatment

0

200

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800

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s& V. Erokhin, T. Berzina, P. Camorani, and M.P. Fontana,

SoftMatter, 2, 870-874 (2006).

V. Erokhin, T. Berzina, P. Camorani, and M.P. Fontana,

SoftMatter, 2, 870-874 (2006).


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BION: Working PlanBION: Working Plan

> Organization of the work

WP 1. Fabrication and optimization of electrochemically controlledsimple networks

WP 2. Training of the network, and first application to a biologicalsystem

WP 3. Fabrication of the complex statistical matrix and tailoringusing as models specific biological systems

WP 4. Discrimination and learning in the biologically inspiredsupramolecular device

> Organization of the work

WP 1. Fabrication and optimization of electrochemically controlledsimple networks

WP 2. Training of the network, and first application to a biologicalsystem

WP 3. Fabrication of the complex statistical matrix and tailoringusing as models specific biological systems

WP 4. Discrimination and learning in the biologically inspiredsupramolecular device


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BION: WP1BION: WP1

> Fabrication and optimization of electrochemically controlledsimple networks

Variation of materials properties (doping agents, solid electrolytecomposition, device configuration) and fabrication techniques (LB,polyelectrolyte self-assembling, spin coating, solution casting).

Mimicking bio-objects working in pulse mode.

Fabrication and study of statistical networks

> Fabrication and optimization of electrochemically controlledsimple networks

Variation of materials properties (doping agents, solid electrolytecomposition, device configuration) and fabrication techniques (LB,polyelectrolyte self-assembling, spin coating, solution casting).

Mimicking bio-objects working in pulse mode.

Fabrication and study of statistical networks

Between 2and 3 (nA)

Between 2and 4 (nA)

Before training 20 20After training 200 20

Training 30 min: 2-3: +1V; 2-4: -0.2 VTraining 30 min: 2-3: +1V; 2-4: -0.2 V

Sequential application of treining andtesting proceduresSequential application of treining andtesting procedures

A. Smerieri, T. Berzina, V. Erokhin, and M.P. Fontana, J. Appl. Phys., accepted.A. Smerieri, T. Berzina, V. Erokhin, and M.P. Fontana, J. Appl. Phys., accepted.


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BION: WP2BION: WP2

> Training of the network, and first application to a biologicalsystem

> Training of the network, and first application to a biologicalsystem

Sample output of the CPGSample output of the CPG


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BION: WP3BION: WP3

> Fabrication of the complex statistical matrix and bio-inspired tailoring

> Fabrication of the complex statistical matrix and bio-inspired tailoring

NH 2

Au

S

S

S

S

SS

S

S

S

S

S

S

S

S

NH 2

H2N A )

NH2

NH2

H2N

N

N

NH

N

N

N

N

NH

N

N


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BION: WP4BION: WP4

> Discrimination and learning in the biologically inspiredsupramolecular device

Development of a new, bottom-up technology for the fabrication ofsophisticated functional supramolecular structures which can beminiaturised in principle down to the nanoscale, which are capableof decision making and complex signal analysis.

Fabrication of a synthetic system which by mimic biological sensoryand cognitive systems can be used as a new, revolutionaryinstrument for neuroscience.

> Discrimination and learning in the biologically inspiredsupramolecular device

Development of a new, bottom-up technology for the fabrication ofsophisticated functional supramolecular structures which can beminiaturised in principle down to the nanoscale, which are capableof decision making and complex signal analysis.

Fabrication of a synthetic system which by mimic biological sensoryand cognitive systems can be used as a new, revolutionaryinstrument for neuroscience.


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Thank youThank you

Dr. Victor Erokhin

Department of Physics University of Parma

Viale Usberti 7A, Parma 43100 Italy

Tel. +39 0521 905239

Fax +39 0521 905223E-mail [email protected]

Dr. Victor Erokhin

Department of Physics University of Parma

Viale Usberti 7A, Parma 43100 Italy

Tel. +39 0521 905239

Fax +39 0521 905223E-mail [email protected]

synthetic pathways to bio-inspired information processing

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