  Brain can recognize different objects as ones that it has experienced before.
The recognition accuracy and its processing time depend on task properties such
as viewing condition, level of noise and etc. Recognition accuracy can be well
explained by different models. However, less attention has been paid to the
processing time and the ones that do, are not biologically plausible. By
extracting features temporally as well as utilizing an accumulation to bound
decision making model, an object recognition model accounting for both
recognition time and accuracy is proposed. To temporally extract informative
features in support of possible classes of stimuli, a hierarchical spiking
neural network, called spiking HMAX is modified. In the decision making part of
the model the extracted information accumulates over time using accumulator
units. The input category is determined as soon as any of the accumulators
reaches a threshold, called decision bound. Results show that not only does the
model follow human accuracy in a psychophysics task better than the classic
spiking HMAX model, but also it predicts human response time in each choice.
Results provide enough evidence that temporal representation of features are
informative since they can improve the accuracy of a biological plausible
decision maker over time. This is also in line with the well-known idea of
speed accuracy trade-off in decision making studies.
