1. A RECURRENT NETWORK IN THE LATERAL AMYGDALA: A MECHANISM FOR TEMPORAL COINCIDENCE DETECTION V. DOYERE 1, L. R. JOHNSON 2, M. HOU 2, A. PONCE 3, L. GRIBELYUK 2, H. H. ALPHS 2, L. ALBERT 2, J. E. LEDOUX 2 1) Center for Neural Science, New York University, New York, New York, USA 2) Laboratoire de Neurobiologie de l'Apprentissage, de la Mémoire et de la Communication, Université Paris Sud, Orsay, France 370.9 Sponsor: European Brain and Behavior Society (EBBS) INTRODUCTION The architecture of a neural network regulates synaptic plasticity underlying the acquisition, consolidation, and expression of memory encoded by the network. According to the Hebbian postulate, network activity serves to facilitate coincidence detection by sustaining an active sensory or memory trace for subsequent associative pairing. The lateral amygdala, especially its dorsal nucleus (LAd), plays a key role in associative fear memory. Neither the network structure of the LAd, nor its temporal architecture is well understood. Individual LA principal neurons feature and extensive (12.1 ± 1.27 mm) network of excitatory collateral that are directionally organized for feedback/forward processing of CS inputs. Here, we sought to identify the temporal properties of the excitatory network in LA. To do this, we chose to measure and decode LAd extracellular polysynaptic field potentials (FPs) evoked by activation of thalamic afferents. Local FP recording enables the analysis of functional modules of neural ensembles at a range of spatial scales (Pesaran, 2002; Logothetis, 2004). METHODS Off Line and Statistical Analysis The analysis of the first 300 ms of the averaged traces consisted on multiple successive phases: (1) PeakFit: the rationale is to fit obvious peaks, and to find hidden peaks (their presence is suggested by a change in the slope of the 2nd derivative. Shapes of the peaks were adjusted to minimize the number of peaks while increasing the r2 value (minimum accepted of 0.97). Two smoothing levels were used: one for the initial fast peaks (up to 50 ms), one for the late slow peaks. (2) Calculate uncertainty zones: To take into account the fact there is variability due to the different placement of stimulating and recording electrodes, and the sampling process, we calculated for each peak its uncertainty zone, calculated as following: The minimum distance between two consecutive peaks in the same preparation never reached 5% of the latency of the peak for the early peaks (up to 100 ms), and 2.5 % for the late peaks. Thus, we calculated the uncertainty zone with +/- 5% for peaks up to 100 ms, and +/- 2.5% for peaks within 100-300 ms. (3) Make categories: For each preparation (e.g. in Vivo Dorsal), we arranged the peaks in order to create categories across animals. (4) Extraction of representative peaks: For each peak category, we calculated the percentage of animals falling into that category. Then we calculated for each preparation the probability of finding these percentages, given the maximum number of peaks and the number of animals, using the following formula: P = (n! / [(n-(n-a))!]) * (z * p1 * qz-1 )a * (1 * p0 * qz )n-a with :n, number of animals a, number of animals present in the given category z, median number of peaks in the group 67, total number of bins created by the uncertainty zone calculations p=1/67, probability that a peak corresponds to a given bin q=66/67, probability that a peak corresponds to any other bin Supported by: CNRS-NSF, CNRS-PICS, MH58911, MH38774, MH46516 and NARSAD. 1. Different rhythmicities in dorsal and ventral LAd 2. LAd is a temporally structured recurrent network 5. The temporally organized recurrent LAd network is identifiable in the awake animal and is stable over time a b c d e 0 2 4 6 8 InitialExtracted intact cut Mean number of peaks slope = 7.09 slope = 5.34 123 Peak order 4 8 12 16 20 Latency (ms) intact cut 0 slope = 4.75 slope = 5.84 intact cut 143 Peak order 2 4 8 12 16 20 Latency (ms) 0 24 intact cut N1N2N3N4N5 0 8 16 24 32 40 Latency (ms) 4. Intra network plasticity 3. Intra network NMDA sensitivity 6. Recurrent Network Model 7. Coincidence Detection Model 1.Stimulation of ‘CS’ inputs to the LA evokes temporally patterned responses in LAd, both in vivo and in vitro, indicating a functional recurrent network. 2.This network has components local to the LA and is under NMDA receptors control. 3.Network activity depresses following potentiation at thalamic afferents. 4.Importantly the recurrent activity triggered by thalamic inputs is timed for temporal convergence with cortically processed sensory information. We conclude that the LAd network is a mechanism for temporal coincidence detection.  What is EPSP, what is spike related ? Both reflect network activity, but do not represent the same network functioning.  Reverberatory activity at the structure level and/or at the cellular level ?  What function does this network support ? Sensory-sensory association ? Open questions 010203040515253545 msec LAd-d LAd-v Real time FV N1 N2 N3 N4 N5 FV N1 N2N3 N4 N5 N6 Network time R 2 = 0.76 a c b d Baseline amplitude (mV) APV amplitude (mV) 0.2 0.1 0.0 0.10.2 Base APV Change in amplitude 0 0.8 0.4 N2N3N4N5 Peak Peak amplitude (mV) N1N3N4 0.0 PTX75 APV100 Peak N2 -0.4 -0.2 PTX75 APV100 PTX75 APV100 N5 r = 0.82 P<0.0001 CONCLUSIONS Doyere@cns.nyu.edu