Synfire structures and cognition : a complex systems perspective

University of Technology, Sydney. Dept. of Software Engineering.NO FULL TEXT AVAILABLE. Access is restricted indefinitely. The hardcopy may be available for consultation at the UTS Library.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

Dynamic effective connectivity in cortically embedded systems of recurrently coupled synfire chains

As a candidate mechanism of neural representation, large numbers of synfire chains can efficiently be embedded in a balanced recurrent cortical network model. Here we study a model in which multiple synfire chains of variable strength are randomly coupled together to form a recurrent system. The system can be implemented both as a large-scale network of integrate-and-fire neurons and as a reduced model. The latter has binary-state pools as basic units but is otherwise isomorphic to the large-scale model, and provides an efficient tool for studying its behavior. Both the large-scale system and its reduced counterpart are able to sustain ongoing endogenous activity in the form of synfire waves, the proliferation of which is regulated by negative feedback caused by collateral noise. Within this equilibrium, diverse repertoires of ongoing activity are observed, including meta-stability and multiple steady states. These states arise in concert with an effective connectivity structure (ECS). The ECS admits a family of effective connectivity graphs (ECGs), parametrized by the mean global activity level. Of these graphs, the strongly connected components and their associated out-components account to a large extent for the observed steady states of the system. These results imply a notion of dynamic effective connectivity as governing neural computation with synfire chains, and related forms of cortical circuitry with complex topologies

High-capacity embedding of synfire chains in a cortical network model

Synfire chains, sequences of pools linked by feedforward connections, support the propagation of precisely timed spike sequences, or synfire waves. An important question remains, how synfire chains can efficiently be embedded in cortical architecture. We present a model of synfire chain embedding in a cortical scale recurrent network using conductance-based synapses, balanced chains, and variable transmission delays. The network attains substantially higher embedding capacities than previous spiking neuron models and allows all its connections to be used for embedding. The number of waves in the model is regulated by recurrent background noise. We computationally explore the embedding capacity limit, and use a mean field analysis to describe the equilibrium state. Simulations confirm the mean field analysis over broad ranges of pool sizes and connectivity levels; the number of pools embedded in the system trades off against the firing rate and the number of waves. An optimal inhibition level balances the conflicting requirements of stable synfire propagation and limited response to background noise. A simplified analysis shows that the present conductance-based synapses achieve higher contrast between the responses to synfire input and background noise compared to current-based synapses, while regulation of wave numbers is traced to the use of variable transmission delays

High-Capacity Embedding of Synfire Chains in a Cortical Network Model

Synfire chains, sequences of pools linked by feedforward connections, support the propagation of precisely timed spike sequences, or synfire waves. An important question remains, how synfire chains can efficiently be embedded in cortical architecture. We present a model of synfire chain embedding in a cortical scale recurrent network using conductance-based synapses, balanced chains, and variable transmission delays. The network attains substantially higher embedding capacities than previous spiking neuron models and allows all its connections to be used for embedding. The number of waves in the model is regulated by recurrent background noise. We computationally explore the embedding capacity limit, and use a mean field analysis to describe the equilibrium state. Simulations confirm the mean field analysis over broad ranges of pool sizes and connectivity levels; the number of pools embedded in the system trades off against the firing rate and the number of waves. An optimal inhibition level balances the conflicting requirements of stable synfire propagation and limited response to background noise. A simplified analysis shows that the present conductance-based synapses achieve higher contrast between the responses to synfire input and background noise compared to current-based synapses, while regulation of wave numbers is traced to the use of variable transmission delays.Open access articlestatus: publishe

Donders is dead: cortical traveling waves and the limits of mental chronometry in cognitive neuroscience

An assumption nearly all researchers in cognitive neuroscience tacitly adhere to is that of space-time separability. Historically, it forms the basis of Donders' difference method, and to date, it underwrites all difference imaging and trial-averaging of cortical activity, including the customary techniques for analyzing fMRI and EEG/MEG data. We describe the assumption and how it licenses common methods in cognitive neuroscience; in particular, we show how it plays out in signal differencing and averaging, and how it misleads us into seeing the brain as a set of static activity sources. In fact, rather than being static, the domains of cortical activity change from moment to moment: Recent research has suggested the importance of traveling waves of activation in the cortex. Traveling waves have been described at a range of different spatial scales in the cortex; they explain a large proportion of the variance in phase measurements of EEG, MEG and ECoG, and are important for understanding cortical function. Critically, traveling waves are not space-time separable. Their prominence suggests that the correct frame of reference for analyzing cortical activity is the dynamical trajectory of the system, rather than the time and space coordinates of measurements. We illustrate what the failure of space-time separability implies for cortical activation, and what consequences this should have for cognitive neuroscience.status: publishe