Cortical Neural Computation

by discrete emergent functional units (Discrete Results)

     One of the most challenging problems we face in neuroscience is to understand how the cortex performs computations. There is increasing evidence that the power of the cortical processing is produced by populations of neurons forming dynamic neuronal ensembles. Theoretical proposals and multineuronal experimental studies have revealed that ensembles of neurons can form emergent functional units. However, how these ensembles are implicated in cortical computations is still a mystery.


     It is still unknown how spatially distributed neuronal activity can be temporally integrated to contribute to cortical computations. A theoretical explanation integrating  spatial and temporal aspects of cortical processing is still lacking.


     Recently, we have suggested that complex neural computations underlying cortical processing could be temporally discrete. But how does the cortex perform this computation?  We propose that cortical processing is produced by the computation of discrete emergent functional units that we have called Discrete Results (Discrete Results Hypothesis). This novel concept has the ability to match the spatial and temporal aspects of cortical processing.


     These functional units of computation integrate the physiological and computational aspects of cortical processing defining the traditional idea of cells ensemble limiting their spatio-temporal dimension and differentiating their membership and relations between the members. Moreover, the Discrete Results hypothesis constitutes a conceptual advance with special relevance for neuroscience and computer sciences.



Discrete Results: Spatio-Temporal Functional Units

     Our Discrete Results Hypothesis suggests that the computational principle of the cortex lies in the precise temporal coordination of spikes of spatially distributed neurons.


     The Discrete Result concept has the ability to explain how complex neural computations underlying cortical processing could be temporally discrete. Consequently, we propose that sensory information would need to be quantized to be computed by the cerebral cortex. Therefore, processing of sensory information must be temporally discrete and information flow in the cortex must be quantized allowing for the formation of Discrete Results. Therefore, in sensory processing, they can be defined as each neural computational functional unit resulting in quantization of the continuous flow of sensory information. Increasing the number of Discrete Results per temporal unit allows resolution enhancement. It could be dynamically adjusted by sensory input or by top-down influence to meet the finest processing resolution depending on perceptual, task or attentional demands.

Neural Computation by Dynamic Sequence of Discrete Results Functional Units

      We propose that precise sequences of Discrete Results are the mechanism used by the cortex to perform computations. The computation of the Discrete Results sequence is the mechanism used by the cortex to extract, code, memorize and transmit neural information. This proposal is a neuronal population mechanism to compute and code. Dynamic sequences of Discrete Results generate representations. Different sequences codify different contents.


     The rhythmic functioning of the synchronized inhibitory networks creates a sequence of Discrete Results. Computations between successive Discrete Results in the sequence produce the power of the cortical processing. Experimental data provide support for this hypothesis. Sequential activity of multineuronal spiking has been well described in the cortex. Moreover, cortical processing by dynamic sequences of Discrete Results could be the neural source of some rhythmic signals observed at population level.


     This hypothesis of neural processing could be applied to other structures and nuclei of the brain.

Castejon C and Nuñez A (2016) Cortical Neural Computation by Discrete Results Hypothesis.

Front. Neural Circuits 10:81. doi: 10.3389/fncir.2016.00081


Copyright © 2016 Castejon and Nuñez.