Temporal coding of reward-guided choice in the posterior parietal cortex

Abstract

Making a decision involves computations across distributed cortical and subcortical networks. How such distributed processing is performed remains unclear. We test how the encoding of choice in a key decision-making node, the posterior parietal cortex (PPC), depends on the temporal structure of the surrounding population activity. We recorded spiking and local field potential (LFP) activity in the PPC while two rhesus macaques performed a decision-making task. We quantified the mutual information that neurons carried about an upcoming choice and its dependence on LFP activity. The spiking of PPC neurons was correlated with LFP phases at three distinct time scales in the theta, beta, and gamma frequency bands. Importantly, activity at these time scales encoded upcoming decisions differently. Choice information contained in neural firing varied with the phase of beta and gamma activity. For gamma activity, maximum choice information occurred at the same phase as the maximum spike count. However, for beta activity, choice information and spike count were greatest at different phases. In contrast, theta activity did not modulate the encoding properties of PPC units directly but was correlated with beta and gamma activity through cross-frequency coupling. We propose that the relative timing of local spiking and choice information reveals temporal reference frames for computations in either local or large-scale decision networks. Differences between the timing of task information and activity patterns may be a general signature of distributed processing across large-scale networks.decision making | phase-of-firing | neural code | saccade | reach T he posterior parietal cortex (PPC) integrates sensory signals for impending actions. Firing rates of neurons in the PPC encode movement intention and the temporal evolution of movement choices (1-13) as well as decision variables such as expected rewards, the subjective desirability during reward-guided decisions (14-18), and the certainty in perceptual decisions (19). Decisions are made within a network that extends across many regions of the brain (20-25), so efficient and flexible mechanisms are required to enable distributed computations. Dynamic and frequency-specific correlations in activity between brain areas are ubiquitous and offer potential physiological mechanisms supporting distributed computations in decision networks (13,21,(26)(27)(28)(29)(30)(31)(32)(33)(34). We hypothesize that the encoding of decisions in the PPC should depend on the temporal structure present in neuronal activity. Previous studies have shown that coherently active ensembles of cells in the PPC predict reaction times (9) and movement choices (13, 21) better than neurons without coherent dynamics. However, whether and how the coding of decisions depends on the temporally structured firing of PPC neurons remains unknown. We examined temporal structure in the encoding of look-reach decisions by PPC neurons. ResultsWe analyzed the activity of 149 units [97 units (31 single units and 66 multiun...

Phase of Firing Coding of Learning Variables across Prefrontal Cortex, Anterior Cingulate Cortex and Striatum during Feature Learning

“…We found that the beta phase allowing maximal encoding of Prediction Errors was offset ~27 ms on average from the phase at which most spikes synchronized to the local fields. Such a dissociation of spike-phase and encoding-phase has been reported previously for the beta frequency band in parietal cortex, where maximal information of joint saccadic and joystick choice directions were best predicted by spike counts at ~50 degree away from the preferred beta spike phase (Hawellek et al, 2016). Such phase offsets underlying maximal encoding in parietal cortex as well as in ACC, LFC and STR in our study provide constraints on the possible circuit mechanisms that permit temporal segregation of inputs streams through phase specific oscillatory dynamics (Akam and Kullmann, 2014).…”

“…We tested this hypothesis by quantifying how much Outcome, Prediction Error, and Outcome History information is available to neurons at different phase bins relative to their mean phase. If the phase-of-firing conveys information, then differences in spike counts between conditions should vary systematically across phases, as opposed to a pure firing rate code that predicts equal information for spike counts across phase bins ( Figure 4A) (Hawellek et al, 2016;Womelsdorf et al, 2012). Figure 4B shows an example neuron exhibiting phase-of-firing coding (with spikes from ACC and beta phases from STR).…”

A theta rhythm in macaque visual cortex and its attentional modulation

“…As mentioned above, the ≈7-8 Hz EEG component, whose phase predicts human detection performance, is strongest over frontal areas (51). Also, spike and LFP recordings in macaque parietal cortex have recently revealed a similar theta rhythm (42,55). If such theta-rhythmic topdown influences were to be found, it will be interesting to understand how they fit with the predominantly bottom-up directed theta influences observed between visual areas (33).…”

A roadmap to integrate astrocytes into Systems Neuroscience

“…The effect of astrocytes on fast waves may be due to cross-frequency coupling, a mechanism whereby global slow oscillations modulate local fast oscillations, usually their amplitude (Canolty & Knight, 2010), which happens to be the predominant effect of astrocytes on fast waves (Perea et al, 2016;Sardinha et al, 2017). By regulating fast waves, astrocytes will have an impact on neuronal encoding, because fast rhythms provide temporal reference frames for local and large-scale computations (Hawellek, Wong, & Pesaran, 2016). Dimensionality reduction (below) may reveal specific astrocytic Ca 2+ regimes associated with coincidence detection, oscillations, and brain state transitions.…”