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Daniel A Butts

Publications and source records attributed to Daniel A Butts.

2 recordsLinked to original sources

How much information is associated with a particular stimulus?

Although the Shannon mutual information can be used to reveal general features of the neural code, it cannot directly address which symbols of the code are significant. Further insight can be gained by using information measures that are specific to particular stimuli or responses. The specific information is a previously proposed measure of the amount of information associated with a particular response; however, as I show, it does not properly characterize the amount of information associated with particular stimuli. Instead, I propose a new measure: the stimulus-specific information (SSI), defined to be the average specific information of responses given the presence of a particular stimulus. Like other information theoretic measures, the SSI does not rely on assumptions about the neural code, and is robust to non-linearities of the system. To demonstrate its applicability, the SSI is applied to data from simulated visual neurons, and identifies stimuli consistent with the neuron's linear kernel. While the SSI reveals the essential linearity of the visual neurons, it also successfully identifies the well-encoded stimuli in a modified example where linear analysis techniques fail. Thus, I demonstrate that the SSI is an appropriate measure of the information associated with particular stimuli, and provides a new unbiased method of analysing the significant stimuli of a neural code.

Action Potentials↗

Retinal waves: implications for synaptic learning rules during development.

Neural activity is often required for the final stages of synaptic refinement during brain development. It is thought that learning rules acting at the individual synapse level, which specify how pre- and postsynaptic activity lead to changes in synaptic efficacy, underlie such activity-dependent development. How such rules might function in vivo can be addressed in the retinogeniculate system because the input activity from the retina and its importance in development are both known. In fact, detailed studies of retinal waves have revealed their complex spatiotemporal properties, providing insights into the mechanisms that use such activity to guide development. First of all, the information useful for development is contained in the retinal waves and can be quantified, placing constraints on synaptic learning rules that use this information. Furthermore, knowing the distribution of activity over the entire set of inputs makes it possible to address a necessary component of developmental refinement: rules governing competition between synaptic inputs. In this way, the detailed knowledge of retinal input and lateral geniculate nucleus development provides a unique opportunity to relate the rules of synaptic plasticity directly to their role in development.

Animals↗