Learning how to avoid danger and pursue reward depends on negative emotions motivating aversive learning and positive emotions motivating appetitive learning. The amygdala is a key component of the brain emotional system; however, an understanding of how various emotions are differentially processed in the amygdala has yet to be achieved. We report that matrix metalloproteinase-9 (MMP-9, extracellularly operating enzyme) in the central nucleus of the amygdala (CeA) is crucial for appetitive, but not for aversive, learning in mice. The knock-out of MMP-9 impairs appetitively motivated conditioning, but not an aversive one. MMP-9 is present at the excitatory synapses in the CeA with its activity greatly enhanced after the appetitive training. Finally, blocking extracellular MMP-9 activity with its inhibitor TIMP-1 provides evidence that local MMP-9 activity in the CeA is crucial for the appetitive, but not for aversive, learning.

The spiking activity of cortical neurons is highly variable. This variability is generally correlated among nearby neurons, an effect commonly interpreted to reflect the coactivation of neurons due to anatomically shared inputs. Recent findings, however, indicate that correlations can be dynamically modulated, suggesting that the underlying mechanisms are not well understood. Here, we investigate the hypothesis that correlations are dominated by neuronal coinactivation: the occurrence of brief silent periods during which all neurons in the local network stop firing. We recorded spiking activity from large populations of neurons in the auditory cortex of anesthetized rats across different brain states. During spontaneous activity, the reduction of correlation accompanying brain state desynchronization was largely explained by a decrease in the density of the silent periods. The presentation of a stimulus caused an initial drop of correlations followed by a rebound, a time course that was mimicked by the instantaneous silence density. We built a rate network model with fluctuation-driven transitions between a silent and an active attractor and assumed that neurons fired Poisson spike trains with a rate following the model dynamics. Variations of the network external input altered the transition rate into the silent attractor and reproduced the relation between correlation and silence density found in the data, both in spontaneous and evoked conditions. This suggests that the observed changes in correlation, occurring gradually with brain state variations or abruptly with sensory stimulation, are due to changes in the likeliness of the microcircuit to transiently cease firing.neuronal variability | noise correlations | brain state | auditory cortex | stochastic network dynamics N euronal noise correlations are defined as common fluctuations in the spiking activity of neurons under conditions of constant sensory input or motor output. Traditionally, they have been thought to arise from the dense connectivity of the cortex, such that neighboring neurons sharing a fraction of their inputs should also share a fraction of their output variability (1). Several observations are consistent with this hypothesis: pairwise correlations in the cortex decrease with cell pair distance (2) or with the difference in stimulus selectivity (3), dependencies that could follow from a variation in shared input given the anatomy of cortical circuits. Recent findings, however, challenge this simple interpretation. Recordings in the primate visual cortex have shown that attention or task context can change correlation structure (4-6) and that the magnitude of averaged correlation can be very low (7). In anesthetized rodents correlations decrease with brain state desynchronization (8, 9) or when animals switch from quiet wakefulness to active whisking during waking (10). Moreover, the commonly observed drop of spiking variability following stimulus onset (11-13) seems to occur jointly with a transient decrease in correlation (2, 14, 15). Th...

No abstract

In the present study, we used a new training paradigm in the intelliCage automatic behavioral assessment system to investigate cognitive functions of the transgenic mice harboring London mutation of the human amyloid precursor protein (APP.V717I). Three groups of animals: 5-, 12-and 18-24-month old were subjected to both Water Maze training and the IntelliCage-based appetitive conditioning. The spatial memory deficit was observed in all three groups of transgenic mice in both behavioral paradigms. However, the APP mice were capable to learn normally when co-housed with the wild-type (WT) littermates, in contrast to clearly impaired learning observed when the transgenic mice were housed alone. Furthermore, in the transgenic mice kept in the Intellicage alone, the cognitive deficit of the young animals was modulated by the circadian rhythm, namely was prominent only during the active phase of the day. The novel approach to study the transgenic mice cognitive abilities presented in this paper offers new insight into cognitive dysfunctions of the Alzheimer's disease mouse model. AbstractIn the present study we used a new training paradigm in the IntelliCage automatic behavioral assessment system to investigate cognitive functions of the transgenic mice harboring London mutation of the human amyloid precursor protein (APP.V717I). Three groups of animals: 5-, 12-and 18-24-month old were subjected to both Water Maze training and the IntelliCage-based appetitive conditioning. The spatial memory deficit was observed in all three groups of transgenic mice in both behavioral paradigms. However, the APP mice were capable to learn normally when co-housed with the wild-type (WT) littermates, in contrast to clearly impaired learning observed when the transgenic mice were housed alone.Furthermore, in the transgenic mice kept in the Intellicage alone, the cognitive deficit of the young animals was modulated by the circadian rhythm, namely was prominent only during the active phase of the day. The novel approach to study the transgenic mice cognitive abilities presented in this paper offers new insight into cognitive dysfunctions of the Alzheimer's disease mouse model. 3