Figure 3. (A) Quantifying a neural spike train as a scalar or vector.

Neural activity consists of intermittent spikes known as action potentials. A series of spikes is known as a neural spike train. data spike train can be quantified as the total number of spikes over a given time period giving a scalar output. Alternatively, time can be divided into small time intervals such that the number of spikes occurring in each time interval is 1 or 0, enabling the spike train to be quantified as a binary vector output. As the total time frame is made longer, the vector becomes longer, and it becomes increasingly harder to adequately sample all possibilities in the entire vector space. (B) Bicoid and hunchback gradient in the Drosophila melanogaster embryo. In the developing embryo of Drosophila melanogaster, pre-deposited bicoid maternal mRNA is translated into a bicoid protein gradient along the anterior-posterior axis. Because bicoid is a cooperative transcriptional activator of hunchback, the smooth bicoid gradient leads to expression of hunchback in a much sharper concentration gradient which delineates the anterior and posterior halves of the embryo. (C) Schematic of the TNF signaling network. Individually, the capacities of the TNF-ATF-2 and the TNF-α NF-κB pathways are only ~0.9 bits of information. Combined, the network of pathways has only a marginally increased capacity of ~1.05 bits. Further investigation found that the capacity was limited at the receptor level at ~1.25 bits implying that the maximum capacity of the TNF network is ~1.25 bits regardless of the number of pathways or branch fidelity.