Rapid serial visual presentation for the determination of neural selectivity in area STSa.

We show that rapid serial visual presentation (RSVP) in combination with a progressive reduction of the stimulus set is an efficient method for describing the selectivity properties of high-level cortical neurons in single-cell electrophysiological recording experiments. Rapid presentation allows the experimental testing of a significantly larger number of stimuli, which can reduce the subjectivity of the results due to stimulus selection and the lack of sufficient control stimuli. We prove the reliability of the rapid presentation and stimulus reduction methods by repeated experiments and the comparison of different testing conditions. Our results from neurons in area STSa of the macaque temporal cortex provide a well-controlled confirmation for the existence of a population of cells that respond selectively to stimuli containing faces. View tuning properties measured using this method also confirmed earlier results. In addition, we found a population of cells that respond reliably to complex non-face stimuli, though their tuning properties are not obvious.

Learning Invariance from Transformation Sequences.

The visual system can reliably identify objects even when the retinal image is transformed considerably by commonly occurring changes in the environment. A local learning rule is proposed, which allows a network to learn to generalize across such transformations. During the learning phase, the network is exposed to temporal sequences of patterns undergoing the transformation. An application of the algorithm is presented in which the network learns invariance to shift in retinal position. Such a principle may be involved in the development of the characteristic shift invariance property of complex cells in the primary visual cortex, and also in the development of more complicated invariance properties of neurons in higher visual areas.

Neural control: closed-loop human brain reading.

Closed-loop experimental testing of single medial temporal lobe neurons in humans reveals top-down effects, opening new possibilities for describing neural representations at the highest level.

Modelling spike trains and extracting response latency with Bayesian binning.

The peristimulus time histogram (PSTH) and the spike density function (SDF) are commonly used in the analysis of neurophysiological data. The PSTH is usually obtained by binning spike trains, the SDF being a (Gaussian) kernel smoothed version of the PSTH. While selection of the bin width or kernel size is often relatively arbitrary there have been recent attempts to remedy this situation (Shimazaki and Shinomoto, 2007c,b,a). We further develop an exact Bayesian generative model approach to estimating PSTHs (Endres et al., 2008) and demonstrate its superiority to competing methods using data from early (LGN) and late (STSa) visual areas. We also highlight the advantages of our scheme's automatic complexity control and generation of error bars. Additionally, our approach allows extraction of excitatory and inhibitory response latency from spike trains in a principled way, both on repeated and single trial data. We show that the method can be applied to data with high background firing rates and inhibitory responses (LGN) as well as to data with low firing rate and excitatory responses (STSa). Furthermore, we demonstrate on simulated data that our latency extraction method works for a range of signal-to-noise ratios and background firing rates. While further studies are needed to examine the sensitivity of our method to, for example, gradual changes in firing rate and adaptation, the current results suggest that Bayesian binning is a powerful method for the estimation of firing rate and the extraction response latency from neuronal spike trains.

Neural coding: non-local but explicit and conceptual.

Recordings from single cells in human medial temporal cortex confirm that sensory processing forms explicit neural representations of the objects and concepts needed for a causal model of the world.

Color sensitivity of cells responsive to complex stimuli in the temporal cortex.

The inferotemporal (IT) cortex of the monkey lies at the head of the ventral visual pathway and is known to mediate object recognition and discrimination. It is often assumed that color plays a minor role in the recognition of objects and faces because discrimination remains highly accurate with black-and-white images. Furthermore it has been suggested that for rapid presentation and reaction tasks, object classification may be based on a first wave of feedforward visual information, which is coarse and achromatic. The fine detail and color information follows later, allowing similar stimuli to be discriminated. To allow these theories to be tested, this study investigates whether the presence of color affects the response of IT neurons to complex stimuli, such as faces, and whether color information is delayed with respect to information about stimulus form in these cells. Color, achromatic, and false-color versions of effective stimuli were presented using a rapid serial visual presentation paradigm, and responses recorded from single cells in IT of the adult monkey. Achromatic images were found to evoke significantly reduced responses compared with color images in the majority of neurons (70%) tested. Differential activity for achromatic and colored stimuli was evident from response onset with no evidence to support the hypothesis that information about object color is delayed with respect to object form. A negative correlation (P < 0.01) was found between cell latency and color sensitivity, with the most color-sensitive cells tending to respond earliest. The results of this study suggest a strong role for color in familiar object recognition and provide no evidence to support the idea of a first wave of form processing in the ventral stream based on purely achromatic information.