Neural basis of perceptual expectations: insights from transient dynamics of attractor neural networks

Abstract

Sensory information from the external world is intrinsically ambiguous, necessitating prior experience as a constraint on perception to parse stimuli into well-defined categories. Priming, which have been used vastly to study such perceptual influences, is the phenomenon where the perception of a given stimulus, or the prime, affects the perception of a succeeding stimulus, or the target, even when the target is presented after a long delay or the prime is not explicitly perceived [1]. Using classical priming paradigms, it is observed that brief exposure to a stimulusranging from tens to hundreds of milliseconds -biases subjects to perceive the following stimuli either as dissimilar (adaptation aftereffects) or more similar (priming) to the priming stimuli [2,3]. Thus, in a categorization task, the category boundary may move towards or away from the prime. The duration of the prime and the prime-target asynchrony, as well as the type of intervening mask, affect both the strength and the direction of the effect [2,4]. Current evidence has led to the suggestion that a particular type of the observed priming effect may be primarily due to the conflicting acts of lingering activation from the prime and accumulating depletion of synaptic resources [5]. Some other studies looked at the interaction between local shunting adaptation and a near-threshold neural baseline [6]. This neural model explains the observed behavior, without invoking any "high-level" decision making or memory process. However, it has been shown that also high-level, complex processes such as face perception are subject to aftereffects, namely rate firing adaptation. In spite of a large body of empirical data on various behavioral outcomes in such experiments, we still lack an explanatory model capable of predicting the crossover from adaptation aftereffect to priming, as emerging in a generic cortical network. We have developed an analytical approach to study the transient dynamics of networks of threshold-linear model neurons that include, as a necessary ingredient of the relevant computational mechanism, a simple feature of pyramidal cell biophysics: firing rate adaptation. The analysis yields the attractor states of the network and the full spectrum of time constants of the transients associated with different steady states. Studying these transients, in the response to external inputs that are morphed between two stored patterns, and affected by previous activity of the network, could shed light on the possible contribution of attractor dynamics to perceptual boundary shifts. The preliminary results show that firing rate adaptation plays the main role to produce adaptation aftereffects in our network, without which one only observes priming effects, if any. The relative duration of target length and adaptation time scale is one of the crucial terms determining the dynamics. The strength of recurrent connection with respect to feedforward inputs is another relevant parameter. References