It is not clear how information related to cognitive or psychological processes is carried by or represented in the responses of single neurons. One provocative proposal is that precisely timed spike patterns play a role in carrying such information. This would require that these spike patterns have the potential for carrying information that would not be available from other measures such as spike count or latency. We examined exactly timed (1-ms precision) triplets and quadruplets of spikes in the stimulus-elicited responses of lateral geniculate nucleus (LGN) and primary visual cortex (V1) neurons of the awake fixating rhesus monkey. Large numbers of these precisely timed spike patterns were found. Information theoretical analysis showed that the precisely timed spike patterns carried only information already available from spike count, suggesting that the number of precisely timed spike

Recognizing emotion from facial expressions draws on diverse psychological processes implemented in a large array of neural structures. Studies using evoked potentials, lesions, and functional imaging have begun to elucidate some of the mechanisms. Early perceptual processing of faces draws on cortices in occipital and temporal lobes that construct detailed representations from the configuration of facial features. Subsequent recognition requires a set of structures, including amygdala and orbitofrontal cortex, that links perceptual representations of the face to the generation of knowledge about the emotion signaled, a complex set of mechanisms using multiple strategies. Although recent studies have provided a wealth of detail regarding these mechanisms in the adult human brain, investigations are also being extended to nonhuman primates, to infants, and to patients with psychiatric disorders.

Numerous studies have suggested that the brain may encode information in the temporal firing pattern of neurons. However, little is known...

Neurons in the visual cortex of monkeys respond selectively to the disparity between the images in the two eyes. Recent recordings have shown that some of the disparity-selective neurons in the primary visual cortex and the posterior parietal cortex are modulated by the distance of fixation. A population of such gain-modulated, disparity-selective neurons forms a set of basis functions of horizontal disparity and distance of fixation that can be used as an intermediate representation for computing egocentric distance. This distributed representation is consistent with psychophysical studies of human depth perception; in contrast, neurons explicitly tuned to distance are not consistent with how we perceive distance. In a population model that includes noise in the firing rates of neurons, the perceived distance is

Abstract. Do cortical neurons operate as integrators or as coincidence detectors? Despite the importance of this question, no definite answer has been given yet, because each of these two views can find its own experimental support. Here we investigated this question using models of morphologically-reconstructed neocortical pyramidal neurons under in vivo like conditions. In agreement with experiments we find that the cell is capable of operating in a continuum between coincidence detection and temporal integration, depending on the characteristics of the synaptic inputs. Moreover, the presence of synaptic background activity at a level comparable to intracellular measurements in vivo can modulate the operating mode of the cell, and act as a switch between temporal integration and coincidence detection. These results suggest that background activity can be viewed as an important determinant of the integrative mode of pyramidal neurons. Thus, background activity not only sharpens cortical responses but it can also be used to tune an entire network between integration and coincidence detection modes. Keywords: cerebral cortex, synaptic background, computational model, operating mode

thalamus as a non-rectifying predictive comparator

Abstract. Algorithms for nonlinear dimensionality reduction (NLDR) find meaningful hidden low-dimensional structures in a high-dimensional space. Current algorithms for NLDR are Isomaps, Local Linear Embedding and Laplacian Eigenmaps. Isomaps are able to reliably recover lowdimensional nonlinear structures in high-dimensional data sets, but suffer from the problem of short-circuiting, which occurs when the neighborhood distance is larger than the distance between the folds in the manifolds. We propose a new variant of Isomap algorithm based on local linear properties of manifolds to increase its robustness to short-circuiting. We demonstrate that the proposed algorithm works better than Isomap algorithm for normal, noisy and sparse data sets. 1

this paper are only little affected by the special choice of neural dynamics as long as the dynamic leads to a synchronization between the neurons. 6.2 B) Connectivity parameters

v I Theoretical Background 1 1 introduction 1 1.1 Prologue: The aim of the work . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 In search of the neural code . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.3 Biologically oriented models . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.3.1 The single cell as a basis for neural code . . . . . . . . . . . . . . . . 1 1.3.2 Temporal modulation of the ring rate . . . . . . . . . . . . . . . . . 2 1.3.3 Population model: (weighted) summed activity across groups of neurons 2 1.4 Mathematical oriented models . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.4.1 Syn-Fire Chains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.4.2 Attractor Neural Network models . . . . . . . . . . . . . . . . . . . . 3 1.5 Dierentiating between models . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.6 Previous work on statistical modeling . . . . . . . . . . . . . . . . . . . . . . 5 1.7 Current work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2 Origin of data 7 2.1 Areas under investigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.1 Frontal cortex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.1.2 Basal ganglia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2 Behavioral modes of the monkey . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2.1 Delay response paradigm . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2.2 Box opening puzzle paradigm . . . . . . . . . . . . . . . . . . . . . . 11 2.3 Surgery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3.1 MPTP treatments . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.4 Periods of interest . . . . ....

: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : xiii I Introduction : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 1 A. Spatial representations and sensori-motor coordination : : : : : : : : : 1 B. The posterior parietal cortex : : : : : : : : : : : : : : : : : : : : : : : 2 C. Neural code for spatial representations : : : : : : : : : : : : : : : : : : 4 1. Dynamic remapping : : : : : : : : : : : : : : : : : : : : : : : : : : 4 2. Gain modulation : : : : : : : : : : : : : : : : : : : : : : : : : : : : 6 3. The Zipser and Andersen Network : : : : : : : : : : : : : : : : : : 6 D. Parallel vectorial representations : : : : : : : : : : : : : : : : : : : : : 9 E. Thesis Outline : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 10 1. Hierarchy in spatial representations : : : : : : : : : : : : : : : : : 10 2. A basis function approach for spatial representation : : : : : : : : 11 II Egocentric spatial representation in early vision : :...