Abstract
Animals must respond selectively to specific combinations of salient environmental stimuli in order to survive in complex environments. A task with these features, biconditional discrimination, requires responses to select pairs of stimuli that are opposite to responses to those stimuli in another combination. We investigate the characteristics of synaptic plasticity and network connectivity needed to produce stimulus-pair neural responses within randomly connected model networks of spiking neurons trained in biconditional discrimination. Using reward-based plasticity for synapses from the random associative network onto a winner-takes-all decision-making network representing perceptual decision-making, we find that reliably correct decision making requires upstream neurons with strong stimulus-pair selectivity. By chance, selective neurons were present in initial networks; appropriate plasticity mechanisms improved task performance by enhancing the initial diversity of responses. We find long-term potentiation of inhibition to be the most beneficial plasticity rule by suppressing weak responses to produce reliably correct decisions across an extensive range of networks.
Highlights
Most environmental stimuli, to which an animal must develop an appropriate response, comprise multiple features and subfeatures that are common to many other stimuli
The results of our studies based on the biconditional discrimination task can be applied to a number of associative learning tasks that employ XOR logic such as visual association [3,6], transitive inference tasks [11], and many others [4]
How the brain associates distinct stimuli to produce specific responses to particular combinations of stimuli is of fundamental importance in neuroscience
Summary
To which an animal must develop an appropriate response, comprise multiple features and subfeatures that are common to many other stimuli Since these other stimuli could engender an alternative response by the animal, it is essential that an animal is able to recognize specific combinations of stimulus features in order to distinguish and respond effectively to differing stimuli that share many features. Associative learning tasks that employ XOR logic include pair-associative learning [3,6], transitive inference [7], and biconditional discrimination tasks [8,9], among others These tasks vary in design and sensory modality, but they all share one requirement, the development of stimulus-pair selectivity to solve the task. The difficulty in XOR tasks (Figure 1A) arises from the requirement for an animal to produce a response to stimulus-pairs (e.g. A+B) selectively, in a manner that differs from its response to the individual stimuli that comprise them (e.g. A or B). The results of our studies based on the biconditional discrimination task can be applied to a number of associative learning tasks that employ XOR logic such as visual association [3,6], transitive inference tasks [11], and many others [4]
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