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Tesi di dottorato in scienze matematiche e fisiche >
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http://hdl.handle.net/2108/761
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Full metadata record
| DC Field | Value | Language |
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| contributor.advisor | Del Giudice, Paolo | - |
| contributor.advisor | Salina, Gaetano | - |
| contributor.advisor | Indiveri, Giacomo | - |
| contributor.advisor | Douglas, Rodney | - |
| contributor.author | Giulioni, Massimiliano | - |
| date.accessioned | 2009-01-15T13:44:34Z | - |
| date.available | 2009-01-15T13:44:34Z | - |
| date.issued | 2009-01-15T13:44:34Z | - |
| identifier.uri | http://hdl.handle.net/2108/761 | - |
| description | Co-dottorato Italia-Svizzera | en |
| description.abstract | The brain is an incredible system with a computational power that goes further beyond those
of our standard computer. It consists of a network of 1011 neurons connected by about 1014
synapses: a massive parallel architecture that suggests that brain performs computation
according to completely new strategies which we are far from understanding.
To study the nervous system a reasonable starting point is to model its basic units,
neurons and synapses, extract the key features, and try to put them together in simple
controllable networks. The research group I have been working in focuses its attention on
the network dynamics and chooses to model neurons and synapses at a functional level: in
this work I consider network of integrate-and-fire neurons connected through synapses that
are plastic and bistable. A synapses is said to be plastic when, according to some kind of
internal dynamics, it is able to change the “strength”, the efficacy, of the connection between
the pre- and post-synaptic neuron. The adjective bistable refers to the number of stable
states of efficacy that a synapse can have; we consider synapses with two stable states:
potentiated (high efficacy) or depressed (low efficacy). The considered synaptic model is
also endowed with a new stop-learning mechanism particularly relevant when dealing with
highly correlated patterns.
The ability of this kind of systems of reproducing in simulation behaviors observed in
biological networks, give sense to an attempt of implementing in hardware the studied
network. This thesis situates at this point: the goal of this work is to design, control and
test hybrid analog-digital, biologically inspired, hardware systems that behave in agreement
with the theoretical and simulations predictions. This class of devices typically goes under
the name of neuromorphic VLSI (Very-Large-Scale Integration). Neuromorphic engineering
was born from the idea of designing bio-mimetic devices and represents a useful research
strategy that contributes to inspire new models, stimulates the theoretical research and that
proposes an effective way of implementing stand-alone power-efficient devices.
In this work I present two chips, a prototype and a larger device, that are a step towards
endowing VLSI, neuromorphic systems with autonomous learning capabilities adequate for
not too simple statistics of the stimuli to be learnt. The main novel features of these
chips are the implemented type of synaptic plasticity and the configurability of the synaptic
connectivity. The reported experimental results demonstrate that the circuits behave in
agreement with theoretical predictions and the advantages of the stop-learning synaptic
plasticity when highly correlated patterns have to be learnt. The high degree of flexibility
of these chips in the definition of the synaptic connectivity is relevant in the perspective of
using such devices as building blocks of parallel, distributed multi-chip architectures that
will allow to scale up the network dimensions to systems with interesting computational
abilities capable to interact with real-world stimuli. | en |
| description.tableofcontents | 1 Introduction - 2 Models for a compact VLSI implementation - 2.1 Neurons - 2.1.1 Hodgkin and Huxley model - 2.1.2 A VLSI implementation of the Hodgkin and Huxley model - 2.1.3 Two-dimensional neuron models - 2.1.4 Morris-Lecar model - 2.1.5 FitzHugh-Nagumo model - 2.1.6 IF model - 2.1.7 IF model on Silicon - 2.2 Synapses - 2.2.1 Fixed synapses in a simple VLSI network - 2.2.2 Plastic synapses - 2.2.3 Effective model of a plastic bistable synapse - 2.2.4 VLSI implementation of the effective synaptic model - 2.2.5 The Calcium self-regulating mechanism - 2.3 Conclusions - 3 CLANN - 3.1 Introduction: main ideas - 3.2 Architecture -
3.3 Signal flow - 3.4 Neuron and Synapse, block level - 3.5 Measuring parameters
through neural and synaptic dynamics - 3.6 LTP/LTD probabilities: measurements vs chip-oriented simulation - 3.7 Learning overlapping patterns - 3.8 Summary and Discussion - C.1 Circuits details and layout - C.1.1 Synapse - C.1.2 Neuron - C.1.3 Calcium - C.1.4 Shaper and other circuits -
4 FLANN - 4.1 Architecture - 4.2 Signal flow - 4.3 Block level description - 4.4 Synapse and shaper: circuits and layout - 4.4.1 Synapse - 4.4.2 Shaper - 4.4.3 Synapse layout - 4.5 Calcium circuit - 4.5.1 Differential pair integrator - 4.5.2 Comparators - 4.5.3 Current conveyors - 4.6 New AER input circuit - 4.7 Preliminary characterization tests:
synaptic efficacy - 4.8 Conclusions - Conclusions | en |
| format.extent | 2756521 bytes | - |
| format.mimetype | application/pdf | - |
| language.iso | en | en |
| subject | spiking neurons | en |
| subject | neural networks | en |
| subject.classification | FIS/01 Fisica sperimentale | en |
| title | Networks of spiking neurons and plastic synapses: implementation and control | en |
| type | Doctoral thesis | en |
| degree.name | Dottorato in fisica | en |
| degree.level | Dottorato | en |
| degree.discipline | Facoltà di Scienze Matematiche Fisiche e Naturali | en |
| degree.grantor | Università degli studi di Roma Tor Vergata | en |
| date.dateofdefense | A.A. 2007/2008 | en |
| Appears in Collections: | Tesi di dottorato in scienze matematiche e fisiche
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Files in This Item:
| File |
Description |
Size | Format |
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| PhD_thesis.pdf | Thesis | 2691Kb | Adobe PDF | View/Open |
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