We used event-related functional magnetic resonance imaging to investigate the neuroanatomical substrates of phonetic encoding and the generation of articulatory codes from abstract phonological representations. Our focus was on the role of the left Inferior Frontal Gyrus (L IFG) and in particular whether the L IFG is part of the mechanism responsible for sub-lexical phonological processing such as syllabification and segmentation or whether it is directly involved in a sensory-motor mapping and the generation of articulatory codes, either by being the site of storage for the codes or by being involved in the mechanism of generating the codes. To answer this question we compared high vs. low frequency pseudowords, which we expected would only activate areas related to the generation of the articulatory codes, but areas related to phonological segmentation. We found activation of a premotor network consisting of the dorsal Precentral Gyrus, the IFG bilaterally and the Supplementary Motor Area for low vs. high frequency words. Our findings support the role of the posterior L IFG in sensory-motor mapping, but also provide evidence to support a further functional segregation of the posterior part of Broca’s Area, the pars Opercularis. We further discuss the meaning and significance of the findings.

Development of orientation maps in ferret and cat primary visual cortex (V1) has been shown to be stable, in that the earliest measurable maps are similar in form to the eventual adult map, robust, in that similar maps develop in both dark rearing and in a variety of normal visual environments, and yet adaptive, in that the final map pattern reflects the statistics of the specific visual environment. How can these three properties be reconciled? Using mechanistic models of the development of neural connectivity in V1, we show for the first time that realistic stable, robust, and adaptive map development can be achieved by including two low-level mechanisms originally motivated from single-neuron results. Specifically, contrast-gain control in the retinal ganglion cells and the lateral geniculate nucleus reduces variation in the presynaptic drive due to differences in input patterns, while homeostatic plasticity of V1 neuron excitability reduces the postsynaptic variability in firing rates. Together these two mechanisms, thought to be applicable across sensory systems in general, lead to biological maps that develop stably and robustly, yet adapt to the visual environment. The modeling results suggest that topographic map stability is a natural outcome of low-level processes of adaptation and normalization. The resulting model is more realistic, simpler, and far more robust, and is thus a good starting point for future studies of cortical map development.

Accurate estimation of neuronal receptive fields is essential for understanding sensory processing in the early visual system. Yet a full characterization of receptive fields is still incomplete, especially with regard to natural visual stimuli and in complete populations of cortical neurons. While previous work has incorporated known structural properties of the early visual system, such as lateral connectivity, or imposing simple-cell-like receptive field structure, no study has exploited the fact that nearby V1 neurons share common feed-forward input from thalamus and other upstream cortical neurons. We introduce a new method for estimating receptive fields simultaneously for a population of V1 neurons, using a model-based analysis incorporating knowledge of the feed-forward visual hierarchy. We assume that a population of V1 neurons shares a common pool of thalamic inputs, and consists of two layers of simple and complex-like V1 neurons. When fit to recordings of a local population of mouse layer 2/3 V1 neurons, our model offers an accurate description of their response to natural images and significant improvement of prediction power over the current state-of-the-art methods. We show that the responses of a large local population of V1 neurons with locally diverse receptive fields can be described with surprisingly limited number of thalamic inputs, consistent with recent experimental findings. Our structural model not only offers an improved functional characterization of V1 neurons, but also provides a framework for studying the relationship between connectivity and function in visual cortical areas.

Visual aftereffects have been found for a wide variety of stimuli, ranging from oriented lines to human faces, but previous results suggested that face aftereffects were qualitatively different from orientation (tilt) aftereffects. Using computational models, we predicted that these differences were due to the limited range of faces used in previous studies. Here we report psychophysical results verifying this prediction. We used the same paradigm to test tilt aftereffects (TAE) and face gender aftereffects (FAE) and found that they exhibited qualitatively similar aftereffect curves, when a sufficiently large range of test faces was used. Overall, the results suggest that similar adaptation mechanisms may underlie both high-level and low-level visual processing.

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