Modeling the neuronal processes underlying short-term working memory remains the focus of many theoretical studies in neuroscience. Here we propose a mathematical model of spiking neuron network (SNN) demonstrating how a piece of information can be maintained as a robust activity pattern for several seconds then completely disappear if no other stimuli come. Such short-term memory traces are preserved due to the activation of astrocytes accompanying the SNN. The astrocytes exhibit calcium transients at a time scale of seconds. These transients further modulate the efficiency of synaptic transmission and, hence, the firing rate of neighboring neurons at diverse timescales through gliotransmitter release. We show how such transients continuously encode frequencies of neuronal discharges and provide robust short-term storage of analogous information. This kind of short-term memory can keep operative information for seconds, then completely forget it to avoid overlapping with forthcoming patterns. The SNN is inter-connected with the astrocytic layer by local inter-cellular diffusive connections. The astrocytes are activated only when the neighboring neurons fire quite synchronously, e.g. when an information pattern is loaded. For illustration, we took greyscale photos of people's faces where the grey level encoded the level of applied current stimulating the neurons. The astrocyte feedback modulates (facilitates) synaptic transmission by varying the frequency of neuronal firing. We show how arbitrary patterns can be loaded, then stored for a certain interval of time, and retrieved if the appropriate clue pattern is applied to the input.
翻译:模拟短期工作记忆背后的神经过程模型仍然是神经科学中许多理论研究的重点。 我们在这里提出一个神经神经网络( SNN) 的数学模型, 以展示如何将某部分信息作为稳健的活动模式维持几秒钟, 如果没有其他刺激因素出现, 就会完全消失。 这种短期的内存痕迹会保留, 原因是随着 SNN 的天体细胞激活。 天体细胞会显示一个时间尺度的时空缩放。 这些瞬态会进一步调整神经同步传输的效率, 从而通过 glilotransministle 释放, 来调整不同时间尺度的相邻神经网络( SNNNNN) 的发射速度。 我们展示这种瞬间信息会持续地记录神经排放的频率, 并提供类似的短期信息。 这种短期内存可以保存数秒, 然后完全忘记它会避免与即将出现的模式重叠。 SNNWN会通过本地的细胞间断层连接与天体层连接。 只有当神经神经神经传输的渐变变变变速度水平被应用时, 才会将电流的易变变换的神经变速度水平应用。 当我们的神经变速度显示时, 我们的神经变变变变变变变的神经变变变变变变的神经的神经变变速度时, 时才会会显示会显示会显示的神经变换的神经变变变变变变变变变的神经变变的轨道水平, 。