6 research outputs found
Functional Brain Oscillations: How Oscillations Facilitate Information Representation and Code Memories
The overall aim of the modelling works within this thesis is to lend theoretical evidence to empirical findings from the brain oscillations literature. We therefore hope to solidify and expand the notion that precise spike timing through oscillatory mechanisms facilitates communication, learning, information processing and information representation within the brain. The primary hypothesis of this thesis is that it can be shown computationally that neural de-synchronisations can allow information content to emerge. We do this using two neural network models, the first of which shows how differential rates of neuronal firing can indicate when a single item is being actively represented. The second model expands this notion by creating a complimentary timing mechanism, thus enabling the emergence of qualitive temporal information when a pattern of items is being actively represented. The secondary hypothesis of this thesis is that it can be also be shown computationally that oscillations might play a functional role in learning. Both of the models presented within this thesis propose a sparsely coded and fast learning hippocampal region that engages in the binding of novel episodic information. The first model demonstrates how active cortical representations enable learning to occur in their hippocampal counterparts via a phase-dependent learning rule. The second model expands this notion, creating hierarchical temporal sequences to encode the relative temporal position of cortical representations. We demonstrate in both of these models, how cortical brain oscillations might provide a gating function to the representation of information, whilst complimentary hippocampal oscillations might provide distinct phasic reference points for learning
The influence of dopamine on prediction, action and learning
In this thesis I explore functions of the neuromodulator dopamine in the context
of autonomous learning and behaviour. I first investigate dopaminergic influence
within a simulated agent-based model, demonstrating how modulation of
synaptic plasticity can enable reward-mediated learning that is both adaptive and
self-limiting. I describe how this mechanism is driven by the dynamics of agentenvironment
interaction and consequently suggest roles for both complex spontaneous
neuronal activity and specific neuroanatomy in the expression of early, exploratory
behaviour. I then show how the observed response of dopamine neurons
in the mammalian basal ganglia may also be modelled by similar processes involving
dopaminergic neuromodulation and cortical spike-pattern representation within
an architecture of counteracting excitatory and inhibitory neural pathways, reflecting
gross mammalian neuroanatomy. Significantly, I demonstrate how combined
modulation of synaptic plasticity and neuronal excitability enables specific (timely)
spike-patterns to be recognised and selectively responded to by efferent neural populations,
therefore providing a novel spike-timing based implementation of the hypothetical
‘serial-compound’ representation suggested by temporal difference learning.
I subsequently discuss more recent work, focused upon modelling those complex
spike-patterns observed in cortex. Here, I describe neural features likely to contribute
to the expression of such activity and subsequently present novel simulation
software allowing for interactive exploration of these factors, in a more comprehensive
neural model that implements both dynamical synapses and dopaminergic
neuromodulation. I conclude by describing how the work presented ultimately suggests
an integrated theory of autonomous learning, in which direct coupling of agent
and environment supports a predictive coding mechanism, bootstrapped in early
development by a more fundamental process of trial-and-error learning
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
25th annual computational neuroscience meeting: CNS-2016
The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong
25th Annual Computational Neuroscience Meeting: CNS-2016
Abstracts of the 25th Annual Computational Neuroscience
Meeting: CNS-2016
Seogwipo City, Jeju-do, South Korea. 2–7 July 201