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PubMed2003

Self-organizing neural network model of motion processing in the visual cortex during smooth pursuit.

A physiologically based neural network model was constructed to study cortical motion processing during pursuit eye movements. The model consists of three layers of computational units, simulating information processing by direction selective neurons in the primary visual cortex (V1), motion selective neurons in the middle-temporal area, and pursuit selective neurons in the middle-superior-temporal (MST) area. MST units integrate visual and eye-movement related information, and their connections develop during an unsupervised training process. The resulting MST units represent a transition from retinal to real-world reference frame. By analyzing the model connectivity, mechanisms underlying the functions performed by the network are studied.

Brain Mapping↗

PubMed2002

Spatial organization of receptive fields of V1 neurons of alert monkeys: comparison with responses to gratings.

We studied the spatial organization of receptive fields and the responses to gratings of neurons in parafoveal V1 of alert monkeys. Activating regions (ARs) of 228 cells were mapped with increment and decrement bars while compensating for fixational eye movements. For cells with two or more ARs, the overlap between ARs responsive to increments (INC) and ARs responsive to decrements (DEC) was characterized by a quantitative overlap index (OI). The distribution of overlap indices was bimodal. The larger group (78% of cells) was composed of complex cells with strongly overlapping ARs (OI >/= 0.5). The smaller group (14%) was composed of simple cells with minimal spatial overlap of ARs (OI </= 0.3). Simple cells were preferentially located in layers dominated by the magnocellular pathway. A third group of neurons, the monocontrast cells (8%), responded only to one sign of contrast and had more sustained responses to flashed stimuli than other cells. One hundred fourteen neurons were also studied with drifting sinusoidal gratings of various spatial frequencies and window widths. For complex cells, the relative modulation (RM, the ratio of the 1st harmonic to the mean firing rate), ranged from 0.6 +/- 0.4 to 1.1 +/- 0.5 (mean +/- SD), depending on the stimulus conditions and the mode of correction for eye movements. RM was not correlated with the degree of overlap of ARs, indicating that the spatial organization of receptive fields cannot reliably be predicted from RM values. In fact, a subset of complex cells had RM > 1, the traditional criterion for identifying simple cells. However, unlike simple cells, even those complex cells with high RM could exhibit diverse nonlinear responses when the spatial frequency or window size was changed. Furthermore, the responses of complex cells to counterphase gratings were predominantly nonlinear even harmonics. These results show that RM is not a robust test of linearity. Our results indicate that complex cells are the most frequently encountered neurons in primate V1, and their behavior needs to receive more emphasis in models of visual function.

Algorithms↗

PubMed1996

Efficient Biologically-based Pattern-recognizing Networks.

A biologiclly-motivated classifying neural network which is based on the feature extraction scheme found in the visual cortex is suggested. A special process is proposed for grading and automatically selecting the "best" features for specific recognition tasks. Ranking is based on a feature's calculated discriminating ability, such that a given class is separated from each and every other class by a given amount. The outcome is a net with less computational complexity than other neural nets, yet one which is more biologically plausible.The main motivation for constructing a reduced net is that the complex circuitry of the brain deals with a huge number of patterns, while a machine-based recognition system usually deals with a limited number of patterns. Results show that feature reduction is drastic and that very compact nets, of the order of tens of neurons, can be used to classify patterns, even in a noisy environment. Copyright 1996 Elsevier Science Ltd

Journal Article↗