Information is integrated across the visual field to transform local features into a global percept. We now know that V1 neurons provide more spatial integration than originally thought due to the existence of their nonclassical inhibitory surrounds. To understand spatial integration in the visual cortex, we have studied the nature and extent of center and surround influences on neuronal respon...

In macaque ventral premotor cortex, we recorded the activity of neurons that responded to both visual and tactile stimuli. For these bimodal cells, the visual receptive field extended from the tactile receptive field into the adjacent space. Their tactile receptive fields were organized topographically, with the arms represented medially, the face represented in the middle, and the inside of th...

The fine structure of the receptive fields of the lateral suprasylvian area neurons was investigated in the pretrigeminal cat preparation. A majority of the receptive fields consisted of subregions with different qualitative characteristics according to their response to moving visual stimuli. There was an asymmetry in the spatial distribution of inhibitory mechanisms over the receptive field. ...

To examine the basis of frequency receptive fields in auditory cortex (ACx), we have recorded intracellular (whole cell) and extracellular (local field potential, LFP) responses to tones in anesthetized rats. Frequency receptive fields derived from excitatory postsynaptic potentials (EPSPs) and LFPs from the same location resembled each other in terms of characteristic frequency (CF) and breadt...

Formal veri cation for complex concurrent systems is a computationally intensive and, in some cases, intractable process. The complexity is an inherent part of the veri cation process due to the system complexity that is an exponential function of the sizes of its components. However, some properties can be enforced by automatically synchronizing the components, thus eliminating the need for ve...

This paper proposes a new method for the design, through simulated evolution, of biologically inspired receptive fields in feedforward neural networks (NNs). The method is intended to enhance pattern recognition performance by creating new neural architectures specifically tuned for a particular pattern recognition problem. It proposes a combined neural architecture composed of two networks in ...