This title appears in the Scientific Report :
2018
Please use the identifier:
http://hdl.handle.net/2128/19337 in citations.
State-dependent processing with Spiking Neural Networks
State-dependent processing with Spiking Neural Networks
Cognitive and behavioral processes are indissociable from their biophysical substrateand the characteristics of the underlying processing elements. As such, neural compu-tation and the properties of functional neurodynamics ought to be understood primarilyas complex biophysical phenomena: nested int...
| Personal Name(s): | Duarte, Renato (Corresponding author) |
|---|---|
| Contributing Institute: |
Computational and Systems Neuroscience; INM-6 Jara-Institut Brain structure-function relationships; INM-10 Computational and Systems Neuroscience; IAS-6 |
| Imprint: |
2018
|
| Physical Description: |
191 |
| Dissertation Note: |
Dissertation, Albert-Ludvigs Universitat Freiburg, 2018 |
| Document Type: |
Dissertation / PhD Thesis |
| Research Program: |
Supercomputing and Modelling for the Human Brain W2/W3 Professorinnen Programm der Helmholtzgemeinschaft (Dys-)function and Plasticity Theory, modelling and simulation |
| Link: |
OpenAccess |
| Publikationsportal JuSER |
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| 502 | |a Dissertation, Albert-Ludvigs Universitat Freiburg, 2018 |c Albert-Ludvigs Universitat Freiburg |b Dissertation |d 2018 |o 2018-05-03 | ||
| 520 | |a Cognitive and behavioral processes are indissociable from their biophysical substrateand the characteristics of the underlying processing elements. As such, neural compu-tation and the properties of functional neurodynamics ought to be understood primarilyas complex biophysical phenomena: nested interactions, spanning multiple temporaland spatial scales and distributed across massively parallel modular hierarchies.The observable dynamics, both at a mesoscopic and microscopic scale, are theresult of complex nonlinear interactions, the ensemble actions of very large and het-erogeneous neuronal populations. These, shaped by evolutionary and developmentalconstraints and permanently subjected to functional re-organization, constitute veryproficient adaptive processing systems. To a first approximation, this complex circuitrycan be seen as large excitable reservoirs, whose symmetry-breaking inhomogeneities(present at multiple levels) naturally give rise to rich high-dimensional dynamics thatsupports cognitive function and computation. Furthermore, the intrinsic recurrent dy-namics endows the system with fading memory, but places critical constraints on pro-cessing precision.Neural computation ought to be studied in context, accounting for the emergenceof ‘mental’ phenomena and thus guided and constrained by findings from the cognitiveand behavioral sciences. These can provide tasks and computational specifications, aswell as performance constraints while the necessary parallels between structure andfunction are gradually and systematically established. In this context, it is reason-able to study the implementation of different aspects of cognitive function that expressthemselves as temporal sequences given that they are ubiquitous in multiple cognitivedomains (from sensorimotor sequencing to language processing). Interestingly, thecortical architecture and its underlying functional relations also appear to be highlysensitive to serial order and temporal structure. The ability to perceive statisticallyrepeating spatiotemporal patterns and abstract the underlying rules may constitute adomain-general mechanism for the acquisition of predictive relations, as it allows forefficient generalization over compact representations. The ever-changing network ofsynaptic (and neuronal intrinsic) properties determines, on short to medium timescales,the ability of a circuit to process time-varying input in an active, predictive manner,dynamically patterned as transient sequences of network states which, through the or-chestration of multiple plasticity mechanisms, come to reflect an increasingly restrictedand accurate internal model of the relevant knowledge structure.Throughout this thesis, we propose to model the underlying systems (at variousdegrees of biological plausibility) in functional contexts, in order to gain knowledgeabout the system itself and the computational relevance of its internal features. Weattempt to shed light on the nature of on-line integration of information, while evalu-ating the character of on-line processing memory and finite precision computation insystems where the current state continuously interacts with and modifies the processingcharacteristics. | ||
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