Synfire structures and cognition : a complex systems perspective
- Publication Type:
- Thesis
- Issue Date:
- 2005
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 01Front.pdf | contents and abstract | 952.07 kB | |||
| 02Whole.pdf | thesis | 17.99 MB |
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NO FULL TEXT AVAILABLE. Access is restricted indefinitely. ----- This thesis explores the relationship between biologically realistic neural
networks and cognition, from a complex systems perspective. The focus is
on compositional systems of synfire chains in neocortex.
A general framework is proposed for memory, learning and perception in
a synfire compositional system, integrating processes across multiple time
scales, wherein compositions embodying knowledge form a self-maintaining
system of cooperatively co-evolving synaptic weight patterns which can be
reconstructed endogenously through the coupling of wave retrieval and Hebbian
weight dynamics.
A network of conductance-based spiking neurons constituting a superposition
of synfire chains incorporating inhibitory neurons is investigated
using simulations and analysis. Through simulations, the main phases of behaviour
are characterised in relation to key parameters, and cross-synchronisation
of waves on cross-linked chains is studied, thereby demonstrating its
strong dependence on background input: as this increases, both the speed
of synchronisation and the range of temporal offsets that allow for synchronisation
are reduced.
Analysis of storage capacity for the superposition network is conducted
using single-neuron simulations to characterise probability of firing in response
to approximately synchronous excitatory inputs accompanied by
background input due to crosstalk. The latter comprises random streams
of excitatory and inhibitory inputs as received by cortical neurons in vivo,
which for a conductance-based neuron greatly reduces the membrane time
constant and raises the equilibrium potential. The resulting spurious firing
rate is obtained analytically; the need to keep this under control sets the
storage capacity. Optimal parameter choices within a biologically plausible
range give capacities well in excess of unity, indicating that the use of
conductance based neurons (compared to neurons with fixed post-synaptic
potential amplitudes) gives a better trade-off between synfire propagation
and control of spurious firing.
A theoretical approach to the evolution of wave activity in a synfire compositional
system is developed, whereby background input regulates wave
activity by reducing wave survival, cross-synchronisation and wave births
as net activity increases. Composite wave formation is viewed as spreading
activation on a compositional landscape, with background input the control
parameter for the analog of a percolation phase transition. By considering
specific compositional topologies (random graph, small world and recursive
compositionality) the features that emerge are critically poised effective connectivity
and 'seasonal' oscillations. These constitute a cognitive cycle in
which alternative composite wave 'hypotheses' proliferate during quiet conditions
and then consolidate and compete during noisy conditions, with the
most coherent large composite waves emerging as the winners involved in
acts of cognition.
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