Neocortical activity is permeated with endogenously generated fluctuations, but how these dynamics affect goal-directed behavior remains a mystery. We found that ensemble neural activity in primate visual cortex spontaneously fluctuated between phases of vigorous (On) and faint (Off) spiking synchronously across cortical layers. These On-Off dynamics, reflecting global changes in cortical state, were also modulated at a local scale during selective attention. Moreover, the momentary phase of local ensemble activity predicted behavioral performance. Our results show that cortical state is controlled locally within a cortical map according to cognitive demands and reveal the impact of these local changes in cortical state on goal-directed behavior.

The ability to judge whether sensory stimuli match an internally represented pattern is central to many brain functions. To elucidate the underlying mechanism, we developed a neural circuit model for match/nonmatch decision making. At the core of this model is a “comparison circuit” consisting of two distinct neural populations: match enhancement cells show higher firing response for a match than a nonmatch to the target pattern, and match suppression cells exhibit the opposite trend. We propose that these two neural pools emerge from inhibition-dominated recurrent dynamics and heterogeneous top-down excitation from a working memory circuit. A downstream system learns, through plastic synapses, to extract the necessary information to make match/nonmatch decisions. The model accounts for key physiological observations from behaving monkeys in delayed match-to-sample experiments, including tasks that require more than simple feature-match (e.g. when BB in ABBA sequence must be ignored). A testable prediction is that magnitudes of match enhancement and suppression neural signals are parametrically tuned to the similarity between compared patterns. Furthermore, the same neural signals from the comparison circuit can be used differently in the decision process for different stimulus statistics or tasks; reward-dependent synaptic plasticity enables decision neurons to flexibly adjust the readout scheme to task demands, whereby the most informative neural signals have the highest impact on the decision.

Many neurons exhibit subthreshold membrane-potential resonances, such that the largest voltage responses occur at preferred stimulation frequencies. Because subthreshold resonances are known to influence the rhythmic activity at the network level, it is vital to understand how they affect spike generation on the single-cell level. We therefore investigated both resonant and nonresonant neurons of rat entorhinal cortex. A minimal resonate-and-fire type model based on measured physiological parameters captures fundamental properties of neuronal firing statistics surprisingly well and helps to shed light on the mechanisms that shape spike patterns: 1) subthreshold resonance together with a spike-induced reset of subthreshold oscillations leads to spike clustering and 2) spike-induced dynamics influence the fine structure of interspike interval (ISI) distributions and are responsible for ISI correlations appearing at higher firing rates (> or =3 Hz). Both mechanisms are likely to account for the specific discharge characteristics of various cell types.

The ability to categorize stimuli into discrete behaviourally relevant groups is an essential cognitive function. To elucidate the neural mechanisms underlying categorization, we constructed a cortical circuit model that is capable of learning a motion categorization task through reward-dependent plasticity. Here we show that stable category representations develop in neurons intermediate to sensory and decision layers if they exhibit choice-correlated activity fluctuations (choice probability). In the model, choice probability and task-specific interneuronal correlations emerge from plasticity of top-down projections from decision neurons. Specific model predictions are confirmed by analysis of single-neuron activity from the monkey parietal cortex, which reveals a mixture of directional and categorical tuning, and a positive correlation between category selectivity and choice probability. Beyond demonstrating a circuit mechanism for categorization, the present work suggests a key role of plastic top-down feedback in simultaneously shaping both neural tuning and correlated neural variability.