Decision making is a process of utmost/nimportance in our daily lives, the study of which has/nbeen receiving notable attention for decades. Nevertheless,/nthe neural mechanisms underlying decision making/nare still not fully understood. Computational modeling/nhas revealed itself as a valuable asset to address some of/nthe fundamental questions. Biophysically plausible models,/nin particular, are useful in bridging the different levels/nof description that experimental studies provide, from the/nneural spiking activity recorded at the cellular level to the/nperformance reported at the behavioral level. In this/narticle, we have reviewed some of the recent progress/nmade in the understanding of the neural mechanisms that/nunderlie decision making. We have performed a critical/nevaluation of the available results and address, from a/ncomputational perspective, aspects of both experimentation/nand modeling that so far have eluded comprehension./nTo guide the discussion, we have selected a central/ntheme which revolves around the following question: how/ndoes the spatiotemporal structure of sensory stimuli affect/nthe perceptual decision-making process? This question is a/ntimely one as several issues that still remain unresolved/nstem from this central theme. These include: (i) the role of/nspatiotemporal input fluctuations in perceptual decision/nmaking, (ii) how to extend the current results and models/nderived from two-alternative choice studies to scenarios/nwith multiple competing evidences, and (iii) to establish/nwhether different types of spatiotemporal input fluctuations/naffect decision-making outcomes in distinctive ways./nAnd although we have restricted our discussion mostly to/nvisual decisions, our main conclusions are arguably/ngeneralizable; hence, their possible extension to other/nsensory modalities is one of the points in our discussion.

AI acknowledges funding from the SUR, DEC of the Generalitat de/nCatalunya and FSE. LDM is a Ramon y Cajal Fellow and acknowledges funding/nfrom the Ministry of Science and Innovation through the Ramon y Cajal/nprogramme. She also acknowledges financial support from the research project/nTIN2010-21771-C02-02 funded by the Ministry of Science and Innovation. MP was/nsupported by the CONSOLIDER-INGENIO 2010 Program CSD2007-00012. GD was/nsupported by the ERC Advanced Grant: DYSTRUCTURE (n. 295129), by the Spanish/nResearch Project SAF2010-16085 and by the CONSOLIDER-INGENIO 2010 Program/nCSD2007-00012, and the FP7-ICT BrainScales and Coronet. RR was supported by/ngrants from the Dirección de Personal Académico de la Universidad Nacional/nAutónoma de México and the Consejo Nacional de Ciencia y Tecnología.