A faster way to model neuronal circuitry

Abstract

Artificial neural networks could pave the way for efficiently simulating large-scale models of neuronal networks in the nervous system.

Keywords: NMDA; artificial neural net; computational model; cortex; deep learning; neuroscience; none.

Figure 1.. Illustration of various types of artificial neural networks (ANN) and their associated components.

(A) A basic ANN consists of an input layer (red circles), one or more hidden layers (peach circles), and an output layer (blue circle). In the case of neuronal modelling, the input could be features such as the membrane potential (V_m), and the excitatory (exc) and inhibitory (inh) synaptic inputs. The hidden layers perform computations on the inputs, with the actual operations depending on the type of ANN. Their objective is to identify features in the inputs and use these to correlate a given input and the correct output. An ANN can have multiple outputs: in this example, the output is a prediction of the membrane potential. (B) A deep neural network (DNN) is an ANN with multiple hidden layers. (C) A convolutional neural network (CNN) is a type of DNN that can be trained to extract important features contained in the input data, which can then be used as inputs to the other hidden layers, significantly improving the performance of the overall network. (D) Some details of the feature extraction process of a CNN, which consists of several hidden layers. First, it has multiple filters (F1, F2, F3), each configured to capture specific features. This process can greatly increase the size of the data, so a pooling layer (P1, P2, P3) is then used to reduce this size. The pooling process does not lead to the loss of valuable data; instead, it helps remove noise and consolidate meaningful data. The flattening layer converts the pooled data into a 1-dimensional stream. This serves as an input for the subsequent fully connected layer, which does the final evaluation to produce the output based on the features extracted by the convolution layers. (E) A CNN with a long short-term memory (LSTM) layer. The additional LSTM layer enables the network to benefit from long-term memory, in addition to the existent short-term working memory. (F) The LSTM layer achieves this long-term memory through its ability to relay both the cell state (dashed green arrows) and the output generated by each module (solid maroon arrows) across its several modules, allowing the flow of useful information. This enables the network to better identify context in the input data over longer time periods. CNN-LSTMs have been found useful for predicting time series data.