Horcholle-Bossavit Ginette, Quenet Brigitte, Foucart Olivier
UMR CNRS 7084, Ecole Supérieure de Physique et de Chimie Industrielle de la Ville de Paris, 10 rue Vauquelin, 75005 Paris, France.
Biosystems. 2007 May-Jun;89(1-3):244-56. doi: 10.1016/j.biosystems.2006.04.022. Epub 2006 Nov 15.
For the analysis of coding mechanisms in the insect olfactory system, a fully connected network of synchronously updated McCulloch and Pitts neurons (MC-P type) was developed [Quenet, B., Horn, D., 2003. The dynamic neural filter: a binary model of spatio-temporal coding. Neural Comput. 15 (2), 309-329]. Considering the update time as an intrinsic clock, this "Dynamic Neural Filter" (DNF), which maps regions of input space into spatio-temporal sequences of neuronal activity, is able to produce exact binary codes extracted from the synchronized activities recorded at the level of projection neurons (PN) in the locust antennal lobe (AL) in response to different odors [Wehr, M., Laurent, G., 1996. Odor encoding by temporal sequences of firing in oscillating neural assemblies. Nature 384, 162-166]. Here, in a first step, we separate the populations of PN and local inhibitory neurons (LN) and use the DNF as a guide for simulations based on biological plausible neurons (Hodgkin-Huxley: H-H type). We show that a parsimonious network of 10 H-H neurons generates action potentials whose timing represents the required codes. In a second step, we construct a new type of DNF in order to study the population dynamics when different delays are taken into account. We find synaptic matrices which lead to both the emergence of robust oscillations and spatio-temporal patterns, using a formal criterion, based on a Normalized Euclidian Distance (NED), in order to measure the use of the temporal dimension as a coding dimension by the DNF. Similarly to biological PN, the activity of excitatory neurons in the model can be both phase-locked to different cycles of oscillations which remind local field potential (LFP), and nevertheless exhibit dynamic behavior complex enough to be the basis of spatio-temporal codes.
为了分析昆虫嗅觉系统中的编码机制，我们构建了一个由同步更新的麦卡洛克和皮茨神经元组成的全连接网络（MC - P型）[奎内特，B.，霍恩，D.，2003年。动态神经滤波器：一种时空编码的二元模型。神经计算。15(2)，309 - 329]。将更新时间视为一个内在时钟，这种“动态神经滤波器”（DNF）将输入空间区域映射为神经元活动的时空序列，能够产生从蝗虫触角叶（AL）中投射神经元（PN）水平记录的同步活动中提取的精确二元编码，以响应不同气味[韦尔，M.，洛朗，G.，1996年。振荡神经集合中放电的时间序列对气味的编码。自然384，162 - 166]。在此，第一步，我们分离出PN群体和局部抑制性神经元（LN）群体，并将DNF用作基于生物合理神经元（霍奇金 - 赫胥黎：H - H型）的模拟指南。我们表明，一个由10个H - H神经元组成的简约网络能够产生动作电位，其时间代表所需的编码。第二步，我们构建一种新型的DNF，以便研究考虑不同延迟时的群体动态。我们使用基于归一化欧几里得距离（NED）的形式标准找到导致稳健振荡和时空模式出现的突触矩阵，以便测量DNF将时间维度用作编码维度的情况。与生物PN类似，模型中兴奋性神经元的活动既可以锁相到不同的振荡周期，这些周期类似于局部场电位（LFP），又能够表现出足够复杂的动态行为，从而成为时空编码的基础。
