α-Calcium calmodulin kinase II modulates the temporal structure of hippocampal bursting patterns

The alpha calcium calmodulin kinase II (α-CaMKII) is known to play a key role in CA1/CA3 synaptic plasticity, hippocampal place cell stability and spatial learning. Additionally, there is evidence from hippocampal electrophysiological slice studies that this kinase has a role in regulating ion channels that control neuronal excitability. Here, we report in vivo single unit studies, with α-CaMKII mutant mice, in which threonine 305 was replaced with an aspartate (α-CaMKII T305D mutants), that indicate that this kinase modulates spike patterns in hippocampal pyramidal neurons. Previous studies showed that α-CaMKII T305D mutants have abnormalities in both hippocampal LTP and hippocampal-dependent learning. We found that besides decreased place cell stability, which could be caused by their LTP impairments, the hippocampal CA1 spike patterns of α-CaMKII T305D mutants were profoundly abnormal. Although overall firing rate, and overall burst frequency were not significantly altered in these mutants, inter-burst intervals, mean number of intra-burst spikes, ratio of intra-burst spikes to total spikes, and mean intra-burst intervals were significantly altered. In particular, the intra burst intervals of place cells in α-CaMKII T305D mutants showed higher variability than controls. These results provide in vivo evidence that besides its well-known function in synaptic plasticity, α-CaMKII, and in particular its inhibitory phosphorylation at threonine 305, also have a role in shaping the temporal structure of hippocampal burst patterns. These results suggest that some of the molecular processes involved in acquiring information may also shape the patterns used to encode this information

Texture Segregation By Visual Cortex: Perceptual Grouping, Attention, and Learning

A neural model is proposed of how laminar interactions in the visual cortex may learn and recognize object texture and form boundaries. The model brings together five interacting processes: region-based texture classification, contour-based boundary grouping, surface filling-in, spatial attention, and object attention. The model shows how form boundaries can determine regions in which surface filling-in occurs; how surface filling-in interacts with spatial attention to generate a form-fitting distribution of spatial attention, or attentional shroud; how the strongest shroud can inhibit weaker shrouds; and how the winning shroud regulates learning of texture categories, and thus the allocation of object attention. The model can discriminate abutted textures with blurred boundaries and is sensitive to texture boundary attributes like discontinuities in orientation and texture flow curvature as well as to relative orientations of texture elements. The model quantitatively fits a large set of human psychophysical data on orientation-based textures. Object boundar output of the model is compared to computer vision algorithms using a set of human segmented photographic images. The model classifies textures and suppresses noise using a multiple scale oriented filterbank and a distributed Adaptive Resonance Theory (dART) classifier. The matched signal between the bottom-up texture inputs and top-down learned texture categories is utilized by oriented competitive and cooperative grouping processes to generate texture boundaries that control surface filling-in and spatial attention. Topdown modulatory attentional feedback from boundary and surface representations to early filtering stages results in enhanced texture boundaries and more efficient learning of texture within attended surface regions. Surface-based attention also provides a self-supervising training signal for learning new textures. Importance of the surface-based attentional feedback in texture learning and classification is tested using a set of textured images from the Brodatz micro-texture album. Benchmark studies vary from 95.1% to 98.6% with attention, and from 90.6% to 93.2% without attention.Air Force Office of Scientific Research (F49620-01-1-0397, F49620-01-1-0423); National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

Interactive Responses of a Thalamic Neuron to Formalin Induced Lasting Pain in Behaving Mice

Thalamocortical (TC) neurons are known to relay incoming sensory information to the cortex via firing in tonic or burst mode. However, it is still unclear how respective firing modes of a single thalamic relay neuron contribute to pain perception under consciousness. Some studies report that bursting could increase pain in hyperalgesic conditions while others suggest the contrary. However, since previous studies were done under either neuropathic pain conditions or often under anesthesia, the mechanism of thalamic pain modulation under awake conditions is not well understood. We therefore characterized the thalamic firing patterns of behaving mice in response to nociceptive pain induced by inflammation. Our results demonstrated that nociceptive pain responses were positively correlated with tonic firing and negatively correlated with burst firing of individual TC neurons. Furthermore, burst properties such as intra-burst-interval (IntraBI) also turned out to be reliably correlated with the changes of nociceptive pain responses. In addition, brain stimulation experiments revealed that only bursts with specific bursting patterns could significantly abolish behavioral nociceptive responses. The results indicate that specific patterns of bursting activity in thalamocortical relay neurons play a critical role in controlling long-lasting inflammatory pain in awake and behaving mice

Global Dynamics of Online Group Conversations

Public online groups allow individuals to carry out conver- sations of common interests. Study of such group conversa- tions provides a unique opportunity to study patterns of hu- man conversations without violating individual privacy. The observational studies conducted in this paper are an attempt to identify the main correlates of continued growth of con- versations, thereby clearing the path to developing predictive models user participation. We study temporal evolution of online group discussions. Surprisingly, we find that individual discussion groups dis- play distinctively q-exponential shaped inter-message times to reply distributions, unlike the power law distributions seen in email conversations. We show, using simulations, that the heavy-tailed distribution of time to reply, which we also ob- serve when all data is combined, originate from mixtures of q-exponentials. We also find that popular threads come to be so from the very beginning as opposed to evolving to be more popular as they grow. This raises new possibilities for devel- oping generative models of thread growth

A computational model of rodent spatial learning and some behavioral experiments

This paper describes a computational model of spatial learning and localization in rodents. The model is based on the suggestion (based on a large body of experimental data) that rodents learn metric spatial representations of their environments by associating sensory inputs with dead-reckoning based position estimates in the hippocampal place cells. Both these sources of information have some uncertainty associated with them because of errors in sensing, range estimation, and path integration. The proposed model incorporates explicit mechanisms for information fusion from uncertain sources. We demonstrate that the proposed model adequately reproduces several key results of behavioral experiments with animals

Modelling Action Cascades in Social Networks

The central idea in designing various marketing strategies for online social networks is to identify the influencers in the network. The influential individuals induce ``word-of-mouth" effects in the network. These individuals are responsible for triggering long cascades of influence that convince their peers to perform a similar action (buying a product, for instance). Targeting these influentials usually leads to a vast spread of the information across the network. Hence it is important to identify such individuals in a network. One way to measure an individual's influencing capability on its peers is by its reach for a certain action. We formulate identifying the influencers in a network as a problem of predicting the average depth of cascades an individual can trigger. We first empirically identify factors that play crucial role in triggering long cascades. Based on the analysis, we build a model for predicting the cascades triggered by a user for an action. The model uses features like influencing capabilities of the user and their friends, influencing capabilities of the particular action and other user and network characteristics. Experiments show that the model effectively improves the predictions over several baselines