Introduction Studies of population coding, which explore how the activity of ensembles of neurons represent the external world, normally focus on the accuracy and reliability with which sensory information is represented. However, the encoding strategies used by neural circuits have undoubtedly been shaped by the way the encoded information is used. The point of encoding sensory information is, after all, to generate and guide behavior. The ease and efficiency with which sensory information can be processed to generate motor responses must be an important factor in determining the nature of a neuronal population code. In other words, to understand how populations of neurons encode we cannot overlook how they compute. Gain modulation, which is seen in many cortical areas, is a change in the response amplitude of a neuron that is not accompanied by a modification of response selectivity. Just as population coding is a ubiquitous form of information representation, gain modulati

We demonstrate that spiking neural networks encoding information in the timing of single spikes are capable of computing and learning clusters from realistic data. We show how a spiking neural network based on spike-time coding and Hebbian learning can successfully perform unsupervised clustering on real-world data, and we demonstrate how temporal synchrony in a multi-layer network can induce hierarchical clustering. We develop a temporal encoding of continuously valued data to obtain adjustable clustering capacity and precision with an e#cient use of neurons: input variables are encoded in a population code by neurons with graded and overlapping sensitivity profiles. We also discuss methods for enhancing scale-sensitivity of the network and show how the induced synchronization of neurons within early RBF layers allows for the subsequent detection of complex clusters.

The question of shift and size invariance in the primate visual system is discussed. After a short review of the relevant neurobiology and psychophysics, a more detailed analysis of computational models is given. The two main types of networks considered are the dynamic routing circuit model and invariant feature networks, such as the neocognitron. Some specific open questions in context of these models are raised and possible solutions discussed. 1 Introduction The ease with which we recognize common objects from di#erent distances and perspectives and under di#erent illuminations gives the impression that invariant object recognition is a trivial task. However, an apparently small change in the stimulus can cause a dramatic change in the retinal activity pattern. Assume, for example, you are looking at a zebra and change your gaze by just one width of the zebra stripes. Many responses of retinal sensors will be inverted (change from low to high or vice versa) causing a dramatic...

Left occipitotemporal damage often yields pure alexia, which is characterized by an extreme length effect in reading single words. Based on the SERIOL model, I propose that the overt seriality displayed by pure alexics is necessary to replace the very rapid, automatic seriality normally produced by left occipitotemporal cortex. In supporting this account, I extend the SERIOL model to include the phonological route, make specific proposals as to the nature of hemispheric specializations in visual processing, and discuss the relationship between string processing and visual object recognition. 1