Background: Our visual system enables us to recognize visual objects across a wide range of spatial scales. The neural mechanisms underlying these abilities are still poorly understood. Size-or scale-independent representation of visual objects might be supported by processing in primary visual cortex (V1). Neurons in V1 are selective for spatial frequency and thus represent visual information in specific spatial wavebands. We tested whether different receptive field properties of neurons in V1 scale with preferred spatial wavelength. Specifically, we investigated the size of the area that enhances responses, i.e., the grating summation field, the size of the inhibitory surround, and the distance dependence of signal coupling, i.e., the linking field.

. The retinal image of visual objects can vary drastically with changes of viewing angle. Nevertheless, our visual system is capable of recognizing objects fairly invariant of viewing angle. Under natural viewing conditions, different views of the same object tend to occur in temporal proximity, thereby generating temporal correlations in the sequence of retinal images. Such spatial and temporal stimulus correlations can be exploited for learning invariant representations. We propose a biologically plausible mechanism that implements this learning strategy using the principle of self-organizing maps. We developed a network of spiking neurons that uses spatiotemporal correlations in the inputs to map different views of objects onto a topographic representation. After learning, different views of the same object are represented in a connected neighborhood of neurons. Model neurons of a higher processing area that receive unspecific input from a local neighborhood in the map show view-invariant selectivities for visual objects. The findings suggest a functional relevance of cortical topographic maps.

Neural network models for unsupervised pattern recognition learning are challenged when the difference between the patterns of the training set is small. The standard neural network architecture for pattern recognition learning consists of adaptive forward connections and lateral inhibition, which provides competition between output neurons. We propose an additional adaptive inhibitory feedback mechanism, to emphasize the difference between training patterns and improve learning. We present an implementation of adaptive feedback inhibition for spiking neural network models, based on spike timing dependent plasticity (STDP). When the inhibitory feedback connections are adjusted using an anti-Hebbian learning rule, feedback inhibition suppresses the redundant activity of input units which code the overlap between similar stimuli. We show, that learning speed and pattern discriminatability can be increased by adding this mechanism to the standard architecture.

Movement planning based on visual information requires a transformation from a retina-centered into a head-or body-centered frame of reference. It has been shown that such transformations can be achieved via basis function networks [1,2]. We investigated whether basis functions for coordinate transformations can be learned by a biologically plausible neural network. We employed a model network of spiking neurons that learns invariant representations based on spatio-temporal stimulus correlations [3]. The model consists of a three-stage network of leaky integrate-and-fire neurons with biologically realistic conductances. The network has two input layers, corresponding to neurons representing the retinal image and neurons representing the direction of gaze. These inputs are represented in the map layer via excitatory or modulatory connections, respectively, that exhibit Hebbian-like spiketiming dependent plasticity (STDP). Neurons within the map layer are connected via short-range lateral excitatory connections and unspecific lateral inhibition. We trained the network with stimuli corresponding to typical viewing situations when a visual scene is explored by saccadic eye movements, with gaze direction changing on a faster time scale than object positions in space. After learning, each neuron in the map layer was selective for a small subset of the stimulus space, with excitatory and modulatory connections adapted to achieve a topographic map of the inputs. Neurons in the output layer with a localized receptive field in the map layer were selective for positions in head-centered space, invariant to changes in retinal image due to changes in gaze direction. Our results show that coordinate transformations via basis function networks can be learned in a biologically plausible way by exploiting the spatio-temporal correlations between visual stimulation and eye position signals under natural viewing conditions.