Norepinephrine Drives Persistent Activity in Prefrontal Cortex via Synergistic α1 and α2 Adrenoceptors

Optimal norepinephrine levels in the prefrontal cortex (PFC) increase delay-related firing and enhance working memory, whereas stress-related or pathologically high levels of norepinephrine are believed to inhibit working memory via α1 adrenoceptors. However, it has been shown that activation of Gq-coupled and phospholipase C-linked receptors can induce persistent firing, a cellular correlate of working memory, in cortical pyramidal neurons. Therefore, despite its importance in stress and cognition, the exact role of norepinephrine in modulating PFC activity remains elusive. Using electrophysiology and optogenetics, we report here that norepinephrine induces persistent firing in pyramidal neurons of the PFC independent of recurrent fast synaptic excitation. This persistent excitatory effect involves presynaptic α1 adrenoceptors facilitating glutamate release and subsequent activation of postsynaptic mGluR5 receptors, and is enhanced by postsynaptic α2 adrenoceptors inhibiting HCN channel activity. Activation of α2 adrenoceptors or inhibition of HCN channels also enhances cholinergic persistent responses in pyramidal neurons, providing a mechanism of crosstalk between noradrenergic and cholinergic inputs. The present study describes a novel cellular basis for the noradrenergic control of cortical information processing and supports a synergistic combination of intrinsic and network mechanisms for the expression of mnemonic properties in pyramidal neurons.     

Alpha2A-adrenoceptors strengthen working memory networks by inhibiting cAMP-HCN channel signaling in prefrontal cortex

Spatial working memory (WM; i.e., "scratchpad" memory) is constantly updated to guide behavior based on representational knowledge of spatial position. It is maintained by spatially tuned, recurrent excitation within networks of prefrontal cortical (PFC) neurons, evident during delay periods in WM tasks. Stimulation of postsynaptic alpha2A adrenoceptors (alpha2A-ARs) is critical for WM. We report that alpha2A-AR stimulation strengthens WM through inhibition of cAMP, closing Hyperpolarization-activated Cyclic Nucleotide-gated (HCN) channels and strengthening the functional connectivity of PFC networks. Ultrastructurally, HCN channels and alpha2A-ARs were colocalized in dendritic spines in PFC. In electrophysiological studies, either alpha2A-AR stimulation, cAMP inhibition or HCN channel blockade enhanced spatially tuned delay-related firing of PFC neurons. Conversely, delay-related network firing collapsed under conditions of excessive cAMP. In behavioral studies, either blockade or knockdown of HCN1 channels in PFC improved WM performance. These data reveal a powerful mechanism for rapidly altering the strength of WM networks in PFC.     

Top-down control of motor cortex ensembles by dorsomedial prefrontal cortex

Dorsomedial prefrontal cortex is critical for the temporal control of behavior. Dorsomedial prefrontal cortex might alter neuronal activity in areas such as motor cortex to inhibit temporally inappropriate responses. We tested this hypothesis by recording from neuronal ensembles in rodent dorsomedial prefrontal cortex during a delayed-response task. One-third of dorsomedial prefrontal neurons were significantly modulated during the delay period. The activity of many of these neurons was predictive of premature responding. We then reversibly inactivated dorsomedial prefrontal cortex while recording ensemble activity in motor cortex. Inactivation of dorsomedial prefrontal cortex reduced delay-related firing, but not response-related firing, in motor cortex. Finally, we made simultaneous recordings in dorsomedial prefrontal cortex and motor cortex and found strong delay-related temporal correlations between neurons in the two cortical areas. These data suggest that functional interactions between dorsomedial prefrontal cortex and motor cortex might serve as a top-down control signal that inhibits inappropriate responding.     

Modulation of Network Excitability by Persistent Activity: How Working Memory Affects the Response to Incoming Stimuli

Persistent activity and match effects are widely regarded as neuronal correlates of short-term storage and manipulation of information, with the first serving active maintenance and the latter supporting the comparison between memory contents and incoming sensory information. The mechanistic and functional relationship between these two basic neurophysiological signatures of working memory remains elusive. We propose that match signals are generated as a result of transient changes in local network excitability brought about by persistent activity. Neurons more active will be more excitable, and thus more responsive to external inputs. Accordingly, network responses are jointly determined by the incoming stimulus and the ongoing pattern of persistent activity. Using a spiking model network, we show that this mechanism is able to reproduce most of the experimental phenomenology of match effects as exposed by single-cell recordings during delayed-response tasks. The model provides a unified, parsimonious mechanistic account of the main neuronal correlates of working memory, makes several experimentally testable predictions, and demonstrates a new functional role for persistent activity.     

A beneficial effect of estrogen on working memory in postmenopausal women taking hormone replacement therapy

Recent neurophysiological data suggest that the prefrontal cortex (PFC) may be susceptible to modulation by estrogen. In humans, the PFC mediates a number of cognitive processes that contribute to memory function, particularly working memory. The present study examined whether memory tasks that recruit PFC-dependent information processing might exhibit estrogen sensitivity in women. Performance on several memory tasks, including measures of working memory, was evaluated in three groups of postmenopausal women: (1) women who were tested when taking estrogen only (n = 38, M(age) = 55.1 years), (2) women who were tested when taking estrogen and a progestin concurrently (n = 23, M(age) = 55.9 years), and (3) women who were not taking hormone replacement therapy (n = 35, M(age) = 56.0 years). Estrogen users exhibited significantly better performance on a verbal task and on a spatial task, each with a prominent working memory component, but did not differ from nonusers on control tasks involving simple passive recall. These findings are consistent with the hypothesis that estrogen is active within PFC and is capable of influencing functions dependent on this region. The results of this study raise the possibility that estrogen may play a role in maintaining certain frontal lobe functions in women.     

A 4 Hz oscillation adaptively synchronizes prefrontal, VTA, and hippocampal activities

Network oscillations support transient communication across brain structures. We show here, in rats, that task-related neuronal activity in the medial prefrontal cortex (PFC), the hippocampus, and the ventral tegmental area (VTA), regions critical for working memory, is coordinated by a 4 Hz oscillation. A prominent increase of power and coherence of the 4 Hz oscillation in the PFC and the VTA and its phase modulation of gamma power in both structures was present in the working memory part of the task. Subsets of both PFC and hippocampal neurons predicted the turn choices of the rat. The goal-predicting PFC pyramidal neurons were more strongly phase locked to both 4 Hz and hippocampal theta oscillations than nonpredicting cells. The 4 Hz and theta oscillations were phase coupled and jointly modulated both gamma waves and neuronal spikes in the PFC, the VTA, and the hippocampus. Thus, multiplexed timing mechanisms in the PFC-VTA-hippocampus axis may support processing of information, including working memory.     

Representational switching by dynamical reorganization of attractor structure in a network model of the prefrontal cortex

The prefrontal cortex (PFC) plays a crucial role in flexible cognitive behavior by representing task relevant information with its working memory. The working memory with sustained neural activity is described as a neural dynamical system composed of multiple attractors, each attractor of which corresponds to an active state of a cell assembly, representing a fragment of information. Recent studies have revealed that the PFC not only represents multiple sets of information but also switches multiple representations and transforms a set of information to another set depending on a given task context. This representational switching between different sets of information is possibly generated endogenously by flexible network dynamics but details of underlying mechanisms are unclear. Here we propose a dynamically reorganizable attractor network model based on certain internal changes in synaptic connectivity, or short-term plasticity. We construct a network model based on a spiking neuron model with dynamical synapses, which can qualitatively reproduce experimentally demonstrated representational switching in the PFC when a monkey was performing a goal-oriented action-planning task. The model holds multiple sets of information that are required for action planning before and after representational switching by reconfiguration of functional cell assemblies. Furthermore, we analyzed population dynamics of this model with a mean field model and show that the changes in cell assemblies' configuration correspond to those in attractor structure that can be viewed as a bifurcation process of the dynamical system. This dynamical reorganization of a neural network could be a key to uncovering the mechanism of flexible information processing in the PFC.     

Phase locking of single neuron activity to theta oscillations during working memory in monkey extrastriate visual cortex

Working memory has been linked to elevated single neuron discharge in monkeys and to oscillatory changes in the human EEG, but the relation between these effects has remained largely unexplored. We addressed this question by measuring local field potentials and single unit activity simultaneously from multiple electrodes placed in extrastriate visual cortex while monkeys were performing a working memory task. We describe a significant enhancement in theta band energy during the delay period. Theta oscillations had a systematic effect on single neuron activity, with neurons emitting more action potentials near their preferred angle of each theta cycle. Sample-selective delay activity was enhanced if only action potentials emitted near the preferred theta angle were considered. Our results suggest that extrastriate visual cortex is involved in short-term maintenance of information and that theta oscillations provide a mechanism for structuring the recurrent interaction between neurons in different brain regions that underlie working memory.     

The function and organization of lateral prefrontal cortex: a test of competing hypotheses

The present experiment tested three hypotheses regarding the function and organization of lateral prefrontal cortex (PFC). The first account (the information cascade hypothesis) suggests that the anterior-posterior organization of lateral PFC is based on the timing with which cue stimuli reduce uncertainty in the action selection process. The second account (the levels-of-abstraction hypothesis) suggests that the anterior-posterior organization of lateral PFC is based on the degree of abstraction of the task goals. The current study began by investigating these two hypotheses, and identified several areas of lateral PFC that were predicted to be active by both the information cascade and levels-of-abstraction accounts. However, the pattern of activation across experimental conditions was inconsistent with both theoretical accounts. Specifically, an anterior area of mid-dorsolateral PFC exhibited sensitivity to experimental conditions that, according to both accounts, should have selectively engaged only posterior areas of PFC. We therefore investigated a third possible account (the adaptive context maintenance hypothesis) that postulates that both posterior and anterior regions of PFC are reliably engaged in task conditions requiring active maintenance of contextual information, with the temporal dynamics of activity in these regions flexibly tracking the duration of maintenance demands. Activity patterns in lateral PFC were consistent with this third hypothesis: regions across lateral PFC exhibited transient activation when contextual information had to be updated and maintained in a trial-by-trial manner, but sustained activation when contextual information had to be maintained over a series of trials. These findings prompt a reconceptualization of current views regarding the anterior-posterior organization of lateral PFC, but do support other findings regarding the active maintenance role of lateral PFC in sequential working memory paradigms.     

Independent Component Analysis in Spiking Neurons

Although models based on independent component analysis (ICA) have been successful in explaining various properties of sensory coding in the cortex, it remains unclear how networks of spiking neurons using realistic plasticity rules can realize such computation. Here, we propose a biologically plausible mechanism for ICA-like learning with spiking neurons. Our model combines spike-timing dependent plasticity and synaptic scaling with an intrinsic plasticity rule that regulates neuronal excitability to maximize information transmission. We show that a stochastically spiking neuron learns one independent component for inputs encoded either as rates or using spike-spike correlations. Furthermore, different independent components can be recovered, when the activity of different neurons is decorrelated by adaptive lateral inhibition.     

Diverse synchrony of firing reflects diverse cell-assembly coding in the prefrontal cortex

In the present paper, we focus on the coding by cell assemblies in the prefrontal cortex (PFC) and discuss the diversity of the coding, which results in stable and dynamic representations and the processing of various information in that higher brain region. The key activity that reflects cell-assembly coding is the synchrony of the firing of multiple neurons when animals are performing cognitive and memory tasks. First, we introduce some studies that have shown task-related synchrony of neuronal firing in the monkey PFC. These studies have reported fixed and several types of dynamic synchronous firing during working memory, long-term visual memory, and goal selection. The results of these studies have indicated that cell assemblies in the PFC can contribute to both the stability and the dynamics of various types of information. Second, we refer to rat studies and introduce the findings of cellular interactions that contribute to synchrony in working memory, learning-induced changes in synchrony in spatial tasks, and interactions of the PFC and hippocampus in dynamic synchrony. These studies have proposed neuronal mechanisms of cell-assembly coding in the PFC and its critical role in the learning of task demands in problematic situations. Based on the monkey and rat studies, we conclude that cell-assembly coding in the PFC is diverse and has various facets, which allow multipotentiality in the higher brain region. Finally, we discuss the problem of the sizes of cell assembly, how diverse the sizes are in the PFC, and the technical problems in their investigation. We introduce a unique spike-sorting method that can detect small and local cell assemblies that consist of closely neighboring neurons. Then, we describe the findings of our study that showed that the monkey PFC has both small and large cell assemblies, which have different roles in information coding in the working brain.     

Predictive features of persistent activity emergence in regular spiking and intrinsic bursting model neurons

Proper functioning of working memory involves the expression of stimulus-selective persistent activity in pyramidal neurons of the prefrontal cortex (PFC), which refers to neural activity that persists for seconds beyond the end of the stimulus. The mechanisms which PFC pyramidal neurons use to discriminate between preferred vs. neutral inputs at the cellular level are largely unknown. Moreover, the presence of pyramidal cell subtypes with different firing patterns, such as regular spiking and intrinsic bursting, raises the question as to what their distinct role might be in persistent firing in the PFC. Here, we use a compartmental modeling approach to search for discriminatory features in the properties of incoming stimuli to a PFC pyramidal neuron and/or its response that signal which of these stimuli will result in persistent activity emergence. Furthermore, we use our modeling approach to study cell-type specific differences in persistent activity properties, via implementing a regular spiking (RS) and an intrinsic bursting (IB) model neuron. We identify synaptic location within the basal dendrites as a feature of stimulus selectivity. Specifically, persistent activity-inducing stimuli consist of activated synapses that are located more distally from the soma compared to non-inducing stimuli, in both model cells. In addition, the action potential (AP) latency and the first few inter-spike-intervals of the neuronal response can be used to reliably detect inducing vs. non-inducing inputs, suggesting a potential mechanism by which downstream neurons can rapidly decode the upcoming emergence of persistent activity. While the two model neurons did not differ in the coding features of persistent activity emergence, the properties of persistent activity, such as the firing pattern and the duration of temporally-restricted persistent activity were distinct. Collectively, our results pinpoint to specific features of the neuronal response to a given stimulus that code for its ability to induce persistent activity and predict differential roles of RS and IB neurons in persistent activity expression.     

Effective visual working memory capacity: an emergent effect from the neural dynamics in an attractor network

The study of working memory capacity is of outmost importance in cognitive psychology as working memory is at the basis of general cognitive function. Although the working memory capacity limit has been thoroughly studied, its origin still remains a matter of strong debate. Only recently has the role of visual saliency in modulating working memory storage capacity been assessed experimentally and proved to provide valuable insights into working memory function. In the computational arena, attractor networks have successfully accounted for psychophysical and neurophysiological data in numerous working memory tasks given their ability to produce a sustained elevated firing rate during a delay period. Here we investigate the mechanisms underlying working memory capacity by means of a biophysically-realistic attractor network with spiking neurons while accounting for two recent experimental observations: 1) the presence of a visually salient item reduces the number of items that can be held in working memory, and 2) visually salient items are commonly kept in memory at the cost of not keeping as many non-salient items.OUR MODEL SUGGESTS THAT WORKING MEMORY CAPACITY IS DETERMINED BY TWO FUNDAMENTAL PROCESSES: encoding of visual items into working memory and maintenance of the encoded items upon their removal from the visual display. While maintenance critically depends on the constraints that lateral inhibition imposes to the mnemonic activity, encoding is limited by the ability of the stimulated neural assemblies to reach a sufficiently high level of excitation, a process governed by the dynamics of competition and cooperation among neuronal pools. Encoding is therefore contingent upon the visual working memory task and has led us to introduce the concept of effective working memory capacity (eWMC) in contrast to the maximal upper capacity limit only reached under ideal conditions.     

Dynamics of population code for working memory in the prefrontal cortex

Some neurons (delay cells) in the prefrontal cortex elevate their activities throughout the time period during which the animal is required to remember past events and prepare future behavior, suggesting that working memory is mediated by continuous neural activity. It is unknown, however, how working memory is represented within a population of prefrontal cortical neurons. We recorded from neuronal ensembles in the prefrontal cortex as rats learned a new delayed alternation task. Ensemble activities changed in parallel with behavioral learning so that they increasingly allowed correct decoding of previous and future goal choices. In well-trained rats, considerable decoding was possible based on only a few neurons and after removing continuously active delay cells. These results show that neural activity in the prefrontal cortex changes dynamically during new task learning so that working memory is robustly represented and that working memory can be mediated by sequential activation of different neural populations.     

D1 receptors physically interact with N-type calcium channels to regulate channel distribution and dendritic calcium entry

Dopamine signaling through D1 receptors in the prefrontal cortex (PFC) plays a critical role in the maintenance of higher cognitive functions, such as working memory. At the cellular level, these functions are predicated to involve alterations in neuronal calcium levels. The dendrites of PFC neurons express D1 receptors and N-type calcium channels, yet little information exists regarding their coupling. Here, we show that D1 receptors potently inhibit N-type channels in dendrites of rat PFC neurons. Using coimmunoprecipitation, we demonstrate the existence of a D1 receptor-N-type channel signaling complex in this region, and we provide evidence for a direct receptor-channel interaction. Finally, we demonstrate the importance of this complex to receptor-channel colocalization in heterologous systems and in PFC neurons. Our data indicate that the N-type calcium channel is an important physiological target of D1 receptors and reveal a mechanism for D1 receptor-mediated regulation of cognitive function in the PFC.     

A Spiking Neural Network Model of Model-Free Reinforcement Learning with High-Dimensional Sensory Input and Perceptual Ambiguity

A theoretical framework of reinforcement learning plays an important role in understanding action selection in animals. Spiking neural networks provide a theoretically grounded means to test computational hypotheses on neurally plausible algorithms of reinforcement learning through numerical simulation. However, most of these models cannot handle observations which are noisy, or occurred in the past, even though these are inevitable and constraining features of learning in real environments. This class of problem is formally known as partially observable reinforcement learning (PORL) problems. It provides a generalization of reinforcement learning to partially observable domains. In addition, observations in the real world tend to be rich and high-dimensional. In this work, we use a spiking neural network model to approximate the free energy of a restricted Boltzmann machine and apply it to the solution of PORL problems with high-dimensional observations. Our spiking network model solves maze tasks with perceptually ambiguous high-dimensional observations without knowledge of the true environment. An extended model with working memory also solves history-dependent tasks. The way spiking neural networks handle PORL problems may provide a glimpse into the underlying laws of neural information processing which can only be discovered through such a top-down approach.     

Stimulation of prefrontal cortex at physiologically relevant frequencies inhibits dopamine release in the nucleus accumbens

The prefrontal cortex (PFC) is thought to provide an excitatory influence on the output of mesoaccumbens dopamine neurons. The evidence for this influence primarily arises from findings in the rat that chemical or high-intensity and high-frequency (60-200 Hz) electrical stimulations of PFC increase burst activity of midbrain dopamine neurons, and augment terminal release of dopamine in the nucleus accumbens. However, PFC neurons in animals that are engaged in PFC-dependent cognitive tasks increase their firing frequency from a baseline of 1-3 Hz to 7-10 Hz, suggesting that the commonly used high-frequency stimulation parameters of the PFC may not be relevant to the behavioral states that are associated with PFC activation. We investigated the influence of PFC activation at lower physiologically relevant frequencies on the release of dopamine in the nucleus accumbens. Using rapid (5-min) microdialysis measures of extracellular dopamine in the nucleus accumbens, we found that although PFC stimulation at 60 Hz produces the expected increases in accumbal dopamine release, the same amplitude of PFC stimulation at 10 Hz significantly decreased these levels. These results indicate that activation of PFC, at frequencies that are associated with increased cognitive demand on this region, inhibits the mesoaccumbens dopamine system.     

Top-down control-signal dynamics in anterior cingulate and prefrontal cortex neurons following task switching

The prefrontal cortex (PFC) and anterior cingulate cortex (ACC) have both been implicated in cognitive control, but their relative roles remain unclear. Here we recorded the activity of single neurons in both areas while monkeys performed a task that required them to switch between trials in which they had to look toward a flashed stimulus (prosaccades) and trials in which they had to look away from the stimulus (antisaccades). We found that ACC neurons had a higher level of task selectivity than PFC neurons during the preparatory period on trials immediately following a task switch. In ACC neurons, task selectivity was strongest after the task switch and declined throughout the task block, whereas task selectivity remained constant in the PFC. These results demonstrate that the ACC is recruited when cognitive demands increase and suggest a role for both areas in task maintenance and the implementation of top-down control.     

Embedding optimization reveals long-lasting history dependence in neural spiking activity

Information processing can leave distinct footprints on the statistics of neural spiking. For example, efficient coding minimizes the statistical dependencies on the spiking history, while temporal integration of information may require the maintenance of information over different timescales. To investigate these footprints, we developed a novel approach to quantify history dependence within the spiking of a single neuron, using the mutual information between the entire past and current spiking. This measure captures how much past information is necessary to predict current spiking. In contrast, classical time-lagged measures of temporal dependence like the autocorrelation capture how long—potentially redundant—past information can still be read out. Strikingly, we find for model neurons that our method disentangles the strength and timescale of history dependence, whereas the two are mixed in classical approaches. When applying the method to experimental data, which are necessarily of limited size, a reliable estimation of mutual information is only possible for a coarse temporal binning of past spiking, a so-called past embedding. To still account for the vastly different spiking statistics and potentially long history dependence of living neurons, we developed an embedding-optimization approach that does not only vary the number and size, but also an exponential stretching of past bins. For extra-cellular spike recordings, we found that the strength and timescale of history dependence indeed can vary independently across experimental preparations. While hippocampus indicated strong and long history dependence, in visual cortex it was weak and short, while in vitro the history dependence was strong but short. This work enables an information-theoretic characterization of history dependence in recorded spike trains, which captures a footprint of information processing that is beyond time-lagged measures of temporal dependence. To facilitate the application of the method, we provide practical guidelines and a toolbox.     

Maps of space in human frontoparietal cortex

Prefrontal cortex (PFC) and posterior parietal cortex (PPC) are neural substrates for spatial cognition. We here review studies in which we tested the hypothesis that human frontoparietal cortex may function as a priority map. According to priority map theory, objects or locations in the visual world are represented by neural activity that is proportional to their attentional priority. Using functional magnetic resonance imaging (fMRI), we first identified topographic maps in PFC and PPC as candidate priority maps of space. We then measured fMRI activity in candidate priority maps during the delay periods of a covert attention task, a spatial working memory task, and a motor planning task to test whether the activity depended on the particular spatial cognition. Our hypothesis was that some, but not all, candidate priority maps in PFC and PPC would be agnostic with regard to what was being prioritized, in that their activity would reflect the location in space across tasks rather than a particular kind of spatial cognition (e.g., covert attention). To test whether patterns of delay period activity were interchangeable during the spatial cognitive tasks, we used multivariate classifiers. We found that decoders trained to predict the locations on one task (e.g., working memory) cross-predicted the locations on the other tasks (e.g., covert attention and motor planning) in superior precentral sulcus (sPCS) and in a region of intraparietal sulcus (IPS2), suggesting that these patterns of maintenance activity may be interchangeable across the tasks. Such properties make sPCS in frontal cortex and IPS2 in parietal cortex viable priority map candidates, and suggest that these areas may be the human homologs of the monkey frontal eye field (FEF) and lateral intraparietal area (LIP).