# A Perspective on Information Optimality in a Neural Circuit and Other Biological Systems
## 4 Discussion
### Information Processing as an Algorithm in Biological Systems
#### 4.2.4 Neural Systems in Animals  
Lastly, the neural network of animals can be considered as yet another example of an information-based system. It represents an algorithm that processes information as input and for output. For example, visual input is received across a two-dimensional surface, but visual perception is in three dimensions. Therefore, the neural network is processing the information for generating a perception in multiple dimensions [68]. For example, a visual scene may be initially processed as segments for subsequent detection of objects and boundaries in the scene [69].  
Visual processing typically occurs at the millisecond time-scale [70], so there is a requirement of a process for constructing a visual scene from prior perception [71], otherwise the perception is a past scene. These processes are dependent on visual input, and in the case of humans, the optic nerve transmits around \(10^{8}\) bits of information per second [72]. This value is comparable to the maximum capacity of a computer network. This rate of information flow leads to a visual perception, and this information is conserved in a neural system, like that of energy in a closed system [73], so the downstream processes are creating visual representations that do not exceed the information content that originates from the visual input (Figure 6). The assumption of this statement is that the system is closed to other sources of information.  
In cognition, information processing is not caused by a biological enzyme, or by the strength of a protein interaction, but the electrochemical-based communication of the neural network. To robustly recognize visual objects [74], this analog of an algorithmic system is expected to use the informational processes of combination and recombination, along with other mechanisms, to generate large diversity of informational patterns and to match an observation to objects stored in the memory (Figure 7). The process of matching, and therefore knowledge, is also dependent on the labeling of objects, and building on that knowledge by relating them to other objects. It may be represented as a graph of nodes and connections. Where the neural network is very large, then it is possible to model a large number of objects and their associations [75].  
Figure 6: Abstract diagram of layers of a neural network that contain information. It is a closed system and the information flows from input to output. The information content from one layer to the next can stay the same or decrease, but not increase, because information moves by a physical process, and therefore is constrained by the Laws of Thermodynamics.  
Figure 7: Plot of combinatorial explosion, the growth in number of possible combinations with respect to number of elements. For comparison, a plot of exponential growth is shown.  
As in the previous examples, the neural system is formed by an evolutionary process that favors a form of information optimality [76, 77]. In addition, the neural system may be considered as an analog, particularly at the level of information flow, of the deep-learning system, as developed by computer scientists [78, 79, 80]. They both may be described as neural networks which mediate the flow of information.