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Title: Impact of synaptic depression on network activity and implications for neural coding
Author: York, Lawrence Christopher
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2011
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Short-term synaptic depression is the phenomena where repeated stimulation leads to a decreased transmission efficacy. In this thesis, the impact of synaptic depression on the responses and dynamics of network models of visual processing is investigated, and the coding implications are examined. I find that synaptic depression can fundamentally change the operation of previously well - understood networks, and explain temporal nonlinearities present in neural responses to multiple stimuli. Furthermore, I show, more generally, how nonlinear interactions can be beneficial with respect to neural coding. I begin chapter 1 with a short introduction. In chapter 2 of this thesis, the behaviour of a ring attractor network is examined when its recurrent connections are subject to short term synaptic depression. I find that, in the presence of a uniform background current, the activity of the network settles to one of three states: a stationary attractor state, a uniform state or a rotating attractor state. I show that the rotation speed can be adjusted over a large range by changing the background current, opening the possibility to use the network as a variable frequency oscillator or pattern generator, and use mathematical analysis to determine an approximate maximum rotation speed. Using simulations, I then extend the network into two - dimensions, and find a rich range of possible behaviours. Processing in the visual cortex can be non - linear: the response to two objects or other visual stimuli presented simultaneously is often less than the sum of the responses to the individual objects. A maximum function has in some cases been proposed to describe these competitive interactions. More recent data has emphasised that such interactions have temporal aspects as well, namely that the response to an initially presented stimulus can suppress the response to a stimulus presented subsequently, especially if the first stimulus is presented at high contrast. Chapter 3 of this thesis will present a simple neuronal network featuring synaptic depression which can account for much of the temporal aspects of this behaviour, whilst remaining consistent with older data and models. Furthermore, it will show how this model leads to several strong predictions regarding the processing of low contrast stimuli sequences, as well suggesting a link between response latency and suppression strength. The response of the model to a structured sequence of input stimuli also appears to anticipate future stimuli, and we predict that the magnitude of this stimulus anticipation will decrease as contrast is decreased. Following on from investigating the temporal aspects of responses to stimuli pairs, in chapter 4 this thesis examines an abstract model of how coding is impacted by non - linear interactions, for both structured and unstructured stimuli spaces. I find that non- linear methods of responding to pairs of stimuli presented simultaneously can have a beneficial effect on coding capacity, with linearly combined responses generally leading to the highest decoding errors rates. This thesis goes on to examine the interplay between this models noise assumptions and the decoding performance, and finds that many of the assumptions made can be weakened without changing, qualitatively, these findings. In chapter 5, this thesis examines layered networks of noisy spiking neurons with recurrent connectivity and featuring depressing synapses. The contrast dependent latency and spike count statistics of the model are analysed and are found to be strongly dependent on the parameters of the noise. The tuning of parameters for models containing noisy IF neurons is discussed, and an information theoretic approach to tuning is outlined which successfully reproduces earlier work in which noise was tuned to linearise the response of a spiking network. The approach is applied to maximise the ability of the network to filter rapid noise transients at low contrast. I finish the thesis with a short concluding chapter.
Supervisor: van Rossum, Mark. ; Rossum, Mark van. ; Oram, Mike. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: synaptic depression ; STD ; neural networks