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.

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.

Texture segregation by visual cortex: perceptual grouping, attention, and learning.

A neural model called dARTEX is proposed of how laminar interactions in the visual cortex may learn and recognize object texture and form boundaries. The model unifies 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 the Ben-Shahar and Zucker [Ben-Shahar, O. & Zucker, S. (2004). Sensitivity to curvatures in orientation-based texture segmentation. Vision Research, 44, 257-277] human psychophysical data on orientation-based textures. Surface-based attentional shrouds improve texture learning and classification: Brodatz texture classification rate varies from 95.1% to 98.6% with correct attention, and from 74.1% to 75.5% without attention. Object boundary output of the model in response to photographic images is compared to computer vision algorithms and human segmentations.