Every cognitive act entails the participation of multiple brain regions. In visual short-term memory, for example, visual information is initially encoded in sensory brain areas and then communicated to regions that mediate the retention, manipulation and retrieval of information. One prominent hypothesis that addresses the question of how communication between neural ensembles is achieved claims that neuronal oscillations support the timely coordination of neural activity between different brain regions 1,2 . Specifically, neuronal oscillations in the theta frequency band (3-9 Hz) have been suggested to underlie the interaction between neural ensembles during mnemonic processing 3,4 .A line of evidence supporting this hypothesis stems from studies investigating the role of hippocampal theta in memory formation in rodents [5][6][7] . Additional evidence comes from studies measuring surface-based or intracortical electroencephalography (EEG) in human subjects. In these cases, memory performance correlates with an increase in theta power [8][9][10] . Moreover, enhanced theta synchrony between electric potentials recorded from memory-related areas has been observed 11,12 .These findings raise the question of whether theta synchrony measured at the mesoscopic level of LFPs provides a basis for the timely coordination of spiking output between distant cortical areas that have been traditionally associated with the sensory encoding of visual information on the one hand and mnemonic processing on the other. Moreover, is the precision of coordination between these regions associated with changes in memory performance? To answer these questions, we studied neuronal interactions between the extrastriate visual area V4 and the lateral prefrontal cortex (lPF) while monkeys performed a visual memory task.Although neural activity in V4 has been related to color and shape processing of visual objects [13][14][15][16] and visual attention 17 , neural responses in lPF have been traditionally associated with working memory, that is, the short-term maintenance and manipulation of sensory information in memory tasks 18,19 . More recently, however, an increasing number of studies have found that the neural circuitry underlying short-term retention of sensory information likely entails earlier sensory cortical areas as well 20 . In these cases, both prefrontal regions 21 and V4 have been linked to memory-related oscillatory synchrony. Specifically, theta oscillations are enhanced during the delay period of memory tasks in both lPF and V4 (ref. 22), and increased oscillatory theta synchrony is accompanied by a phase-dependent coding of visual stimuli retained in short-term memory 23,24 .We observed enhanced phase locking between local field potentials recorded in V4 and lPF (inter-area LFP phase locking) that occurred in the theta range (~3-9 Hz) during the memory period of a visual short-term memory task. Increased LFP-phase locking was associated with greater locking of spikes to the phase of the theta oscillations in the respective...

This paper addresses the question how generic microcircuits of neurons in different parts of the cortex can attain and maintain different computational specializations. We show that if stochastic variations in the dynamics of local microcircuits are correlated with signals related to functional improvements of the brain (e.g. in the control of behavior), the computational operation of these microcircuits can become optimized for specific tasks such as the generation of specific periodic signals and task-dependent routing of information. Furthermore, we show that working memory can autonomously emerge through reward-modulated Hebbian learning, if needed for specific tasks. Altogether, our results suggest that reward-modulated synaptic plasticity can not only optimize the network parameters for specific computational tasks, but also initiate a functional rewiring that re-programs microcircuits, thereby generating diverse computational functions in different generic cortical microcircuits. On a more general level, this work provides a new perspective for a standard model for computations in generic cortical microcircuits (liquid computing model). It shows that the arguably most problematic assumption of this model, the postulate of a teacher that trains neural readouts through supervised learning, can be eliminated. We show that generic networks of neurons can learn numerous biologically relevant computations through trial and error.

Processing and storage of sensory information is based on the interaction between different neural populations rather than the isolated activity of single neurons. In order to characterize the dynamic interaction and transient cooperation of sub-circuits within a neural network, multivariate autoregressive (MVAR) models have proven to be an important analysis tool. In this study, we apply directed functional coupling based on MVAR models and describe the temporal and spatial changes of functional coupling between simultaneously recorded local field potentials in extrastriate area V4 during visual memory. Specifically, we compare the strength and directional relations of coupling based on generalized partial directed coherence (GPDC) measures while two rhesus monkeys perform a visual short-term memory task. In both monkeys we find increases in theta power during the memory period that are accompanied by changes in directed coupling. These interactions are most prominent in the low frequency range encompassing the theta band (3–12 Hz) and, more importantly, are asymmetric between pairs of recording sites. Furthermore, we find that the degree of interaction decreases as a function of distance between electrode positions, suggesting that these interactions are a predominantly local phenomenon. Taken together, our results show that directed coupling measures based on MVAR models are able to provide important insights into the spatial and temporal formation of local functionally coupled ensembles during visual memory in V4. Moreover, our findings suggest that visual memory is accompanied not only by a temporary increase of oscillatory activity in the theta band, but by a direction-dependent change in theta coupling, which ultimately represents a change in functional connectivity within the neural circuit.