Non-reward neural mechanisms in the orbitofrontal cortex

Abstract

Single neurons in the primate orbitofrontal cortex respond when an expected reward is not obtained, and behavior must change. The human lateral orbitofrontal cortex is activated when non-reward, or loss occurs. The neuronal computation of this negative reward prediction error is fundamental for the emotional changes associated with non-reward, and with changing behavior. Little is known about the neuronal mechanism. Here we propose a mechanism, which we formalize into a neuronal network model, which is simulated to enable the operation of the mechanism to be investigated. A single attractor network has a Reward population (or pool) of neurons that is activated by Expected Reward, and maintain their firing until, after a time, synaptic depression reduces the firing rate in this neuronal population. If a Reward Outcome is not received, the decreasing firing in the Reward neurons releases the inhibition implemented by inhibitory neurons, and this results in a second population of Non-Reward neurons to start and continue firing encouraged by the spiking-related noise in the network. If a Reward Outcome is received, this keeps the Reward attractor active, and this through the inhibitory neurons prevents the Non-Reward attractor neurons from being activated. If an Expected Reward has been signalled, and the Reward Attractor neurons are active, their firing can be directly inhibited by a Non-Reward Outcome, and the Non-Reward neurons become activated because the inhibition on them is released. The neuronal mechanism in the orbitofrontal cortex for computing negative reward prediction error are important, for this system may be over-reactive in depression, under-reactive in impulsive behavior, and may influence the dopaminergic 'prediction error' neurons.

A non-reward attractor theory of depression

“…The computational model with integrate-andfire neurons shows how adaptation in the Reward attractor neurons after an expected reward input is received can lead to activation of the Non-Reward attractor neurons unless a reward outcome has been received which maintains the Reward attractor state. This thus provides a model of how the responses of lateral orbitofrontal cortex non-reward neurons are produced (Rolls and Deco, 2016). Importantly, the model is based on attractor networks of the type described in this paper that may be over-sensitive or persistent in depression.…”

“…In terms of responses to inputs that increase the expectancy of reward, a short-term attractor system, probably in the orbitofrontal cortex, is likely to be present, to bridge any temporal interval between the expected reward signal and the actual outcome (Rolls and Deco, 2016). This could in principle be oversensitive in mania.…”

“…The detection of this non-reward may be computed in the orbitofrontal cortex using reciprocally inhibiting Reward and Non-Reward attractor neuronal populations in a single network (Rolls and Deco, 2016). The computational model with integrate-andfire neurons shows how adaptation in the Reward attractor neurons after an expected reward input is received can lead to activation of the Non-Reward attractor neurons unless a reward outcome has been received which maintains the Reward attractor state.…”

The roles of the orbitofrontal cortex via the habenula in non-reward and depression, and in the responses of serotonin and dopamine neurons

“…Another 'non-reward' attractor network may be triggered into activity lasting for several seconds if the expected reward is not received (Rolls, 2014). Possible neuronal network mechanisms to compute this in the orbitofrontal cortex have been described (Rolls and Deco, 2016).…”

“…When reward associations with stimuli keep changing, reward-related behavior can switch in one trial, because a rule attractor for which stimulus is currently rewarded holds the current rule in short-term memory (Rolls, 2016a, b;Rolls and Deco, 2016;Thorpe et al, 1983). Again, a cortical attractor network appears to be involved in this type of computation.…”