Neurophysiological evidence due to Schall, Newsome and others indicates that decision proc-esses in certain cortical areas (e.g. FEF and LIP) involve the integration of noisy evidence. Within this paradigm, we ask which neuronal architectures and parameter values would allow an animal to make the fastest and most accurate decisions. Since evolutionary pressure promotes such optimality (e.g. in prey capture and predator avoidance), it is plausible that biological decision networks realise or approximate optimal performance. We consider a simple decision model proposed by Usher & McClelland consisting of two populations of neu-rons integrating evidence in support of two alterna-tives, and we analyze the dynamics of this model. We show that in order to implement the optimal decision algorithm (sequential probability ratio test) the linearised network must satisfy the follow-ing two constraints: (i) it must accumulate the dif-ference between evidence in support of each alter-native, as would be implemented by mutual inhibi-tion between the populations; and (ii) the strength of mutual inhibition must be equal to the leak of activity from each population.
|Translated title of the contribution||How a biological decision network can implement a statistically optimal test|
|Title of host publication||Unknown|
|Pages||3 - 8|
|Number of pages||5|
|Publication status||Published - Jul 2005|