Exact Bayesian bin classification: a fast alternative to Bayesian classification and its application to neural response analysis.

We investigate the general problem of signal classification and, in particular, that of assigning stimulus labels to neural spike trains recorded from single cortical neurons. Finding efficient ways of classifying neural responses is especially important in experiments involving rapid presentation of stimuli. We introduce a fast, exact alternative to Bayesian classification. Instead of estimating the class-conditional densities p(x|y) (where x is a scalar function of the feature[s], y the class label) and converting them to P(y|x) via Bayes' theorem, this probability is evaluated directly and without the need for approximations. This is achieved by integrating over all possible binnings of x with an upper limit on the number of bins. Computational time is quadratic in both the number of observed data points and the number of bins. The algorithm also allows for the computation of feedback signals, which can be used as input to subsequent stages of inference, e.g. neural network training. Responses of single neurons from high-level visual cortex (area STSa) to rapid sequences of complex visual stimuli are analysed. Information latency and response duration increase nonlinearly with presentation duration, suggesting that neural processing speeds adapt to presentation speeds.

Out of sight but not out of mind: the neurophysiology of iconic memory in the superior temporal sulcus.

Iconic memory, the short-lasting visual memory of a briefly flashed stimulus, is an important component of most models of visual perception. Here we investigate what physiological mechanisms underlie this capacity by showing rapid serial visual presentation (RSVP) sequences with and without interstimulus gaps to human observers and macaque monkeys. For gaps of up to 93 ms between consecutive images, human observers and neurones in the temporal cortex of macaque monkeys were found to continue processing a stimulus as if it was still present on the screen. The continued firing of neurones in temporal cortex may therefore underlie iconic memory. Based on these findings, a neurophysiological vision of iconic memory is presented.

The speed of sight.

Macaque monkeys were presented with continuous rapid serial visual presentation (RSVP) sequences of unrelated naturalistic images at rates of 14--222 msec/image, while neurons that responded selectively to complex patterns (e.g., faces) were recorded in temporal cortex. Stimulus selectivity was preserved for 65% of these neurons even at surprisingly fast presentation rates (14 msec/image or 72 images/sec). Five human subjects were asked to detect or remember images under equivalent conditions. Their performance in both tasks was above chance at all rates (14--111 msec/image). The performance of single neurons was comparable to that of humans and responded in a similar way to changes in presentation rate. The implications for the role of temporal cortex cells in perception are discussed.

The 'Ideal Homunculus': decoding neural population signals.

Information processing in the nervous system involves the activity of large populations of neurons. It is possible, however, to interpret the activity of relatively small numbers of cells in terms of meaningful aspects of the environment. 'Bayesian inference' provides a systematic and effective method of combining information from multiple cells to accomplish this. It is not a model of a neural mechanism (neither are alternative methods, such as the population vector approach) but a tool for analysing neural signals. It does not require difficult assumptions about the nature of the dimensions underlying cell selectivity, about the distribution and tuning of cell responses or about the way in which information is transmitted and processed. It can be applied to any parameter of neural activity (for example, firing rate or temporal pattern). In this review, we demonstrate the power of Bayesian analysis using examples of visual responses of neurons in primary visual and temporal cortices. We show that interaction between correlation in mean responses to different stimuli (signal) and correlation in response variability within stimuli (noise) can lead to marked improvement of stimulus discrimination using population responses.

Forming sparse representations by local anti-Hebbian learning.

How does the brain form a useful representation of its environment? It is shown here that a layer of simple Hebbian units connected by modifiable anti-Hebbian feed-back connections can learn to code a set of patterns in such a way that statistical dependency between the elements of the representation is reduced, while information is preserved. The resulting code is sparse, which is favourable if it is to be used as input to a subsequent supervised associative layer. The operation of the network is demonstrated on two simple problems.