Recurrent Excitation Research Articles

Across-Area Synchronization Supports Feature Integration in a Biophysical Network Model of Working Memory.

Working memory function is severely limited. One key limitation that constrains the ability to maintain multiple items in working memory simultaneously is so-called swap errors. These errors occur when an inaccurate response is in fact accurate relative to a non-target stimulus, reflecting the failure to maintain the appropriate association or “binding” between the features that define one object (e.g., color and location). The mechanisms underlying feature binding in working memory remain unknown. Here, we tested the hypothesis that features are bound in memory through synchrony across feature-specific neural assemblies. We built a biophysical neural network model composed of two one-dimensional attractor networks – one for color and one for location – simulating feature storage in different cortical areas. Within each area, gamma oscillations were induced during bump attractor activity through the interplay of fast recurrent excitation and slower feedback inhibition. As a result, different memorized items were held at different phases of the network’s oscillation. These two areas were then reciprocally connected via weak cortico-cortical excitation, accomplishing binding between color and location through the synchronization of pairs of bumps across the two areas. Encoding and decoding of color-location associations was accomplished through rate coding, overcoming a long-standing limitation of binding through synchrony. In some simulations, swap errors arose: “color bumps” abruptly changed their phase relationship with “location bumps.” This model, which leverages the explanatory power of similar attractor models, specifies a plausible mechanism for feature binding and makes specific predictions about swap errors that are testable at behavioral and neurophysiological levels.

Why Firing Rate Distributions Are Important for Understanding Spinal Central Pattern Generators.

Networks in the spinal cord, which are responsible for the generation of rhythmic movements, commonly known as central pattern generators (CPGs), have remained elusive for decades. Although it is well-known that many spinal neurons are rhythmically active, little attention has been given to the distribution of firing rates across the population. Here, we argue that firing rate distributions can provide an important clue to the organization of the CPGs. The data that can be gleaned from the sparse literature indicate a firing rate distribution, which is skewed toward zero with a long tail, akin to a normal distribution on a log-scale, i.e., a “log-normal” distribution. Importantly, such a shape is difficult to unite with the widespread assumption of modules composed of recurrently connected excitatory neurons. Spinal modules with recurrent excitation has the propensity to quickly escalate their firing rate and reach the maximum, hence equalizing the spiking activity across the population. The population distribution of firing rates hence would consist of a narrow peak near the maximum. This is incompatible with experiments, that show wide distributions and a peak close to zero. A way to resolve this puzzle is to include recurrent inhibition internally in each CPG modules. Hence, we investigate the impact of recurrent inhibition in a model and find that the firing rate distributions are closer to the experimentally observed. We therefore propose that recurrent inhibition is a crucial element in motor circuits, and suggest that future models of motor circuits should include recurrent inhibition as a mandatory element.

Cell assembly formation and structure in a piriform cortex model.

The piriform cortex is rich in recurrent excitatory synaptic connections between pyramidal neurons. We asked how such connections could shape cortical responses to olfactory lateral olfactory tract (LOT) inputs. For this, we constructed a computational network model of anterior piriform cortex with 2000 multicompartment, multiconductance neurons (500 semilunar, 1000 layer 2 and 500 layer 3 pyramids; 200 superficial interneurons of two types; 500 deep interneurons of three types; 500 LOT afferents), incorporating published and unpublished data. With a given distribution of LOT firing patterns, and increasing the strength of recurrent excitation, a small number of firing patterns were observed in pyramidal cell networks: first, sparse firings; then temporally and spatially concentrated epochs of action potentials, wherein each neuron fires one or two spikes; then more synchronized events, associated with bursts of action potentials in some pyramidal neurons. We suggest that one function of anterior piriform cortex is to transform ongoing streams of input spikes into temporally focused spike patterns, called here "cell assemblies", that are salient for downstream projection areas.

Dynamics of the Auditory Continuity Illusion.

Illusions give intriguing insights into perceptual and neural dynamics. In the auditory continuity illusion, two brief tones separated by a silent gap may be heard as one continuous tone if a noise burst with appropriate characteristics fills the gap. This illusion probes the conditions under which listeners link related sounds across time and maintain perceptual continuity in the face of sudden changes in sound mixtures. Conceptual explanations of this illusion have been proposed, but its neural basis is still being investigated. In this work we provide a dynamical systems framework, grounded in principles of neural dynamics, to explain the continuity illusion. We construct an idealized firing rate model of a neural population and analyze the conditions under which firing rate responses persist during the interruption between the two tones. First, we show that sustained inputs and hysteresis dynamics (a mismatch between tone levels needed to activate and inactivate the population) can produce continuous responses. Second, we show that transient inputs and bistable dynamics (coexistence of two stable firing rate levels) can also produce continuous responses. Finally, we combine these input types together to obtain neural dynamics consistent with two requirements for the continuity illusion as articulated in a well-known theory of auditory scene analysis: responses persist through the noise-filled gap if noise provides sufficient evidence that the tone continues and if there is no evidence of discontinuities between the tones and noise. By grounding these notions in a quantitative model that incorporates elements of neural circuits (recurrent excitation, and mutual inhibition, specifically), we identify plausible mechanisms for the continuity illusion. Our findings can help guide future studies of neural correlates of this illusion and inform development of more biophysically-based models of the auditory continuity illusion.

Unraveling the mechanisms of surround suppression in early visual processing.

This paper uses mathematical modeling to study the mechanisms of surround suppression in the primate visual cortex. We present a large-scale neural circuit model consisting of three interconnected components: LGN and two input layers (Layer 4Ca and Layer 6) of the primary visual cortex V1, covering several hundred hypercolumns. Anatomical structures are incorporated and physiological parameters from realistic modeling work are used. The remaining parameters are chosen to produce model outputs that emulate experimentally observed size-tuning curves. Our two main results are: (i) we discovered the character of the long-range connections in Layer 6 responsible for surround effects in the input layers; and (ii) we showed that a net-inhibitory feedback, i.e., feedback that excites I-cells more than E-cells, from Layer 6 to Layer 4 is conducive to producing surround properties consistent with experimental data. These results are obtained through parameter selection and model analysis. The effects of nonlinear recurrent excitation and inhibition are also discussed. A feature that distinguishes our model from previous modeling work on surround suppression is that we have tried to reproduce realistic lengthscales that are crucial for quantitative comparison with data. Due to its size and the large number of unknown parameters, the model is computationally challenging. We demonstrate a strategy that involves first locating baseline values for relevant parameters using a linear model, followed by the introduction of nonlinearities where needed. We find such a methodology effective, and propose it as a possibility in the modeling of complex biological systems.

Theta Oscillations Gate the Transmission of Reliable Sequences in the Medial Entorhinal Cortex.

Stability and precision of sequential activity in the entorhinal cortex (EC) is crucial for encoding spatially guided behavior and memory. These sequences are driven by constantly evolving sensory inputs and persist despite a noisy background. In a realistic computational model of a medial EC (MEC) microcircuit, we show that intrinsic neuronal properties and network mechanisms interact with theta oscillations to generate reliable outputs. In our model, sensory inputs activate interneurons near their most excitable phase during each theta cycle. As the inputs change, different interneurons are recruited and postsynaptic stellate cells are released from inhibition. This causes a sequence of rebound spikes. The rebound time scale of stellate cells, because of an h–current, matches that of theta oscillations. This fortuitous similarity of time scales ensures that stellate spikes get relegated to the least excitable phase of theta and the network encodes the external drive but ignores recurrent excitation. In contrast, in the absence of theta, rebound spikes compete with external inputs and disrupt the sequence that follows. Further, the same mechanism where theta modulates the gain of incoming inputs, can be used to select between competing inputs to create transient functionally connected networks. Our results concur with experimental data that show, subduing theta oscillations disrupts the spatial periodicity of grid cell receptive fields. In the bat MEC where grid cell receptive fields persist even in the absence of continuous theta oscillations, we argue that other low frequency fluctuations play the role of theta.

Chronic loss of inhibition in piriform cortex following brief, daily optogenetic stimulation.

SUMMARYIt is well established that seizures beget seizures, yet the cellular processes that underlie progressive epileptogenesis remain unclear. Here, we use optogenetics to briefly activate targeted populations of mouse piriform cortex (PCx) principal neurons in vivo. After just 3 or 4 days of stimulation, previously subconvulsive stimuli trigger massive, generalized seizures. Highly recurrent allocortices are especially prone to “optokindling.” Optokindling upsets the balance of recurrent excitation and feedback inhibition. To understand how this balance is disrupted, we then selectively reactivate the same neurons in vitro. Surprisingly, we find no evidence of heterosynaptic potentiation; instead, we observe a marked, pathway-specific decrease in feedback inhibition. We find no loss of inhibitory interneurons; rather, decreased GABA synthesis in feedback inhibitory neurons appears to underlie weakened inhibition. Optokindling will allow precise identification of the molecular processes by which brain activity patterns can progressively and pathologically disrupt the balance of cortical excitation and inhibition.

Noradrenergic Suppression of Persistent Firing in Hippocampal CA1 Pyramidal Cells through cAMP-PKA Pathway.

Persistent firing is believed to be a cellular correlate of working memory. While the effects of noradrenaline (NA) on working memory have widely been described, its effect on the cellular mechanisms of persistent firing remains largely unknown. Using in vitro intracellular recordings, we demonstrate that persistent firing is supported by individual neurons in hippocampal CA1 pyramidal cells through cholinergic receptor activation, but is dramatically attenuated by NA. In contrast to the classical theory that recurrent synaptic excitation supports persistent firing, suppression of persistent firing by NA was independent of synaptic transmission, indicating that the mechanism is intrinsic to individual cells. In agreement with detrimental effects of cAMP on working memory, we demonstrate that the suppressive effect of NA was through cAMP-PKA pathway. In addition, activation of β1 and/or β3 adrenergic receptors, which increases cAMP levels, suppressed persistent firing. These results are in line with working memory decline observed during high levels of NA and cAMP, which are implicated in high stress, aging, and schizophrenia.

A Disinhibitory Circuit for Contextual Modulation in Primary Visual Cortex

Context guides perception by influencing stimulus saliency. Accordingly, in visual cortex, responses to a stimulus are modulated by context, the visual scene surrounding the stimulus. Responses are suppressed when stimulus and surround are similar but not when they differ. The underlying mechanisms remain unclear. Here, we use optical recordings, manipulations, and computational modeling to show that disinhibitory circuits consisting of vasoactive intestinal peptide (VIP)-expressing and somatostatin (SOM)-expressing inhibitory neurons modulate responses in mouse visual cortex depending on similarity between stimulus and surround, primarily by modulating recurrent excitation. When stimulus and surround are similar, VIP neurons are inactive, and activity of SOM neurons leads to suppression of excitatory neurons. However, when stimulus and surround differ, VIP neurons are active, inhibiting SOM neurons, which leads to relief of excitatory neurons from suppression. We have identified a canonical cortical disinhibitory circuit that contributes to contextual modulation and may regulate perceptual saliency.

Laminar Differences in the Targeting of Dendritic Spines by Cortical Pyramidal Neurons and Interneurons in Human Dorsolateral Prefrontal Cortex

Activation of specific neural circuits in different layers of the primate dorsolateral prefrontal cortex (DLPFC) is essential for working memory, a core cognitive function. Recurrent excitation between pyramidal neurons in middle and deep layers of the DLPFC contributes to the laminar-specific activity associated with different working memory subprocesses. Excitation between cortical pyramidal neurons is mediated by glutamatergic synapses on dendritic spines, but whether the relative abundance of spines receiving cortical inputs differs between middle and deep cortical layers in human DLPFC is unknown. Additionally, GABAergic inputs to spines sculpt pyramidal neuron activity. Whether dendritic spines that receive a glutamatergic input from a cortical pyramidal neuron are targeted by GABAergic interneurons in the human DLPFC is unknown. Using triple-label fluorescence confocal microscopy, we found that 1) the density of spines receiving an input from a cortical pyramidal neuron is greater in the middle than in the deep laminar zone, 2) dendritic spines dually innervated by a cortical pyramidal neuron and an interneuron are present in the human DLPFC, and 3) the density of spines dually innervated by a cortical pyramidal neuron and an interneuron is also greater in the middle than in the deep laminar zone. Ultrastructural analyses support the presence of spines that receive a cortical pyramidal neuron synapse and an interneuron synapse in human and monkey DLPFC. These data support the notion that the DLPFC middle laminar zone is particularly endowed with a microcircuit structure that supports the gating, integrating and fine-tuning of synaptic information in recurrent excitatory microcircuits.

Guanfacine’s mechanism of action in treating prefrontal cortical disorders: Successful translation across species

The selective norepinephrine (NE) α2A-adrenoceptor (α2A-AR) agonist, guanfacine (Intuniv™), is FDA-approved for treating Attention Deficit Hyperactivity Disorder (ADHD) based on research in animals, a translational success story. Guanfacine is also widely used off-label in additional mental disorders that involve impaired functioning of the prefrontal cortex (PFC), including stress-related disorders such as substance abuse, schizotypic cognitive deficits, and traumatic brain injury. The PFC subserves high order cognitive and executive functions including working memory, abstract reasoning, insight and judgment, and top-down control of attention, action and emotion. These abilities arise from PFC microcircuits with extensive recurrent excitation through NMDAR synapses. There is powerful modulation of these synapses, where cAMP-PKA opening of nearby potassium (K+) channels can rapidly and dynamically alter synaptic strength to coordinate arousal state with cognitive state, e.g. to take PFC “offline” during uncontrollable stress. A variety of evidence shows that guanfacine acts within the PFC via post-synaptic α2A-AR on dendritic spines to inhibit cAMP-PKA-K+ channel signaling, thus strengthening network connectivity, enhancing PFC neuronal firing, and improving PFC cognitive functions. Although guanfacine’s beneficial effects are present in rodent, they are especially evident in primates, where the PFC greatly expands and differentiates. In addition to therapeutic actions in PFC, stress-related disorders may also benefit from additional α2-AR actions, such as weakening plasticity in the amygdala, reducing NE release, and anti-inflammatory actions by deactivating microglia. Altogether, these NE α2-AR actions optimize top-down control by PFC networks, which may explain guanfacine’s benefits in a variety of mental disorders.

Non-equilibrium landscape and flux reveal the stability-flexibility-energy tradeoff in working memory.

Uncovering the underlying biophysical principles of emergent collective computational abilities, such as working memory, in neural circuits is one of the most essential concerns in modern neuroscience. Working memory system is often desired to be robust against noises. Such systems can be highly flexible for adapting environmental demands. How neural circuits reconfigure themselves according to the cognitive task requirement remains unclear. Previous studies explored the robustness and the flexibility in working memory by tracing individual dynamical trajectories in a limited time scale, where the accuracy of the results depends on the volume of the collected statistical data. Inspired by thermodynamics and statistical mechanics in physical systems, we developed a non-equilibrium landscape and flux framework for studying the neural network dynamics. Applying this approach to a biophysically based working memory model, we investigated how changes in the recurrent excitation mediated by slow NMDA receptors within a selective population and mutual inhibition mediated by GABAergic interneurons between populations affect the robustness against noises. This is realized through quantifying the underlying non-equilibrium potential landscape topography and the kinetics of state switching. We found that an optimal compromise for a working memory circuit between the robustness and the flexibility can be achieved through the emergence of an intermediate state between the working memory states. An optimal combination of both increased self-excitation and inhibition can enhance the flexibility to external signals without significantly reducing the robustness to the random fluctuations. Furthermore, we found that the enhanced performance in working memory is supported by larger energy consumption. Our approach can facilitate the design of new network structure for cognitive functions with the optimal balance between performance and cost. Our work also provides a new paradigm for exploring the underlying mechanisms of many cognitive functions based on non-equilibrium physics.

Stimulus-specific hypothalamic encoding of a persistent defensive state.

SummaryPersistent neural activity has been described in cortical, hippocampal, and motor networks as mediating working memory of transiently encountered stimuli1,2. Internal emotion states such as fear also exhibit persistence following exposure to an inciting stimulus3, but whether slow neural dynamics are involved is not well-studied. SF1+/Nr5a1+ neurons in the dorsomedial and central subdivisions of the ventromedial hypothalamus (VMHdm/c) are necessary for defensive responses to predators4–7. Optogenetic activation of VMHdmSF1 neurons elicits defensive behaviours that outlast stimulation5,8, suggesting the induction of a persistent internal state of fear or anxiety. Here we show that in response to naturalistic threatening stimuli, VMHdmSF1 neurons exhibit persistent activity lasting many tens of seconds. This persistent activity was correlated with, and required for, persistent defensive behavior in an open-field assay, and was dependent on neurotransmitter release from VMHdmSF1 neurons. Stimulation and calcium imaging experiments in acute slices revealed local excitatory connectivity between VMHdmSF1 neurons. Microendoscopic calcium imaging of VMHdmSF1 neurons revealed that persistent activity at the population level reflects heterogeneous dynamics among individual cells. Unexpectedly, distinct but overlapping VMHdmSF1 subpopulations were persistently activated by different modalities of threatening stimuli. Computational modeling suggests that neither recurrent excitation nor slow-acting neuromodulators alone can account for persistent activity that maintains stimulus identity. Our results identify stimulus-specific slow neural dynamics in the hypothalamus, on a time scale orders of magnitude longer than that supporting working memory in the cortex9,10, as a contributing mechanism underlying a persistent emotion state. (238 words)

Presynaptic inhibition rapidly stabilises recurrent excitation in the face of plasticity.

Hebbian plasticity, a mechanism believed to be the substrate of learning and memory, detects and further enhances correlated neural activity. Because this constitutes an unstable positive feedback loop, it requires additional homeostatic control. Computational work suggests that in recurrent networks, the homeostatic mechanisms observed in experiments are too slow to compensate instabilities arising from Hebbian plasticity and need to be complemented by rapid compensatory processes. We suggest presynaptic inhibition as a candidate that rapidly provides stability by compensating recurrent excitation induced by Hebbian changes. Presynaptic inhibition is mediated by presynaptic GABA receptors that effectively and reversibly attenuate transmitter release. Activation of these receptors can be triggered by excess network activity, hence providing a stabilising negative feedback loop that weakens recurrent interactions on sub-second timescales. We study the stabilising effect of presynaptic inhibition in recurrent networks, in which presynaptic inhibition is implemented as a multiplicative reduction of recurrent synaptic weights in response to increasing inhibitory activity. We show that networks with presynaptic inhibition display a gradual increase of firing rates with growing excitatory weights, in contrast to traditional excitatory-inhibitory networks. This alleviates the positive feedback loop between Hebbian plasticity and network activity and thereby allows homeostasis to act on timescales similar to those observed in experiments. Our results generalise to spiking networks with a biophysically more detailed implementation of the presynaptic inhibition mechanism. In conclusion, presynaptic inhibition provides a powerful compensatory mechanism that rapidly reduces effective recurrent interactions and thereby stabilises Hebbian learning.

Ketamine anaesthesia induces gain enhancement via recurrent excitation in granular input layers of the auditory cortex.

Ketamine is a common anaesthetic agent used in research and more recently as medication in treatment of depression. It has known effects on inhibition of interneurons and cortical stimulus-locked responses, but the underlying functional network mechanisms are still elusive. Analysing population activity across all layers within the auditory cortex, we found that doses of this anaesthetic induce a stronger activation and stimulus-locked response to pure-tone stimuli. This cortical response is driven by gain enhancement of thalamocortical input processing selectively within granular layers due to an increased recurrent excitation. Time-frequency analysis indicates a higher broadband magnitude response and prolonged phase coherence in granular layers, possibly pointing to disinhibition of this recurrent excitation. These results further the understanding of ketamine's functional mechanisms, which will improve the ability to interpret physiological studies moving from anaesthetized to awake paradigms and may lead to the development of better ketamine-based depression treatments with lower side effects. Ketamine is commonly used as an anaesthetic agent and has more recently gained attention as an antidepressant. It has been linked to increased stimulus-locked excitability, inhibition of interneurons and modulation of intrinsic neuronal oscillations. However, the functional network mechanisms are still elusive. A better understanding of these anaesthetic network effects may improve upon previous interpretations of seminal studies conducted under anaesthesia and have widespread relevance for neuroscience with awake and anaesthetized subjects as well as in medicine. Here, we investigated the effects of anaesthetic doses of ketamine (15mgkg-1 h-1 i.p.) on the network activity after pure-tone stimulation within the auditory cortex of male Mongolian gerbils (Meriones unguiculatus). We used laminar current source density (CSD) analysis and subsequent layer-specific continuous wavelet analysis to investigate spatiotemporal response dynamics on cortical columnar processing in awake and ketamine-anaesthetized animals. We found thalamocortical input processing within granular layers III/IV to be significantly increased under ketamine. This layer-dependent gain enhancement under ketamine was not due to changes in cross-trial phase coherence but was rather attributed to a broadband increase in magnitude reflecting an increase in recurrent excitation. A time-frequency analysis was indicative of a prolonged period of stimulus-induced excitation possibly due to a reduced coupling of excitation and inhibition in granular input circuits - in line with the common hypothesis of cortical disinhibition via suppression of GABAergic interneurons.

Recurrent circuit dynamics underlie persistent activity in the macaque frontoparietal network.

During delayed oculomotor response tasks, neurons in the lateral intraparietal area (LIP) and the frontal eye fields (FEF) exhibit persistent activity that reflects the active maintenance of behaviorally relevant information. Despite many computational models of the mechanisms of persistent activity, there is a lack of circuit-level data from the primate to inform the theories. To fill this gap, we simultaneously recorded ensembles of neurons in both LIP and FEF while macaques performed a memory-guided saccade task. A population encoding model revealed strong and symmetric long-timescale recurrent excitation between LIP and FEF. Unexpectedly, LIP exhibited stronger local functional connectivity than FEF, and many neurons in LIP had longer network and intrinsic timescales. The differences in connectivity could be explained by the strength of recurrent dynamics in attractor networks. These findings reveal reciprocal multi-area circuit dynamics in the frontoparietal network during persistent activity and lay the groundwork for quantitative comparisons to theoretical models.

Contrast response in a comprehensive network model of macaque V1.

The response to contrast is one of the most important functions of the macaque primary visual cortex, V1, but up to now there has not been an adequate theory for it. To fill this gap in our understanding of cortical function, we built and analyzed a new large-scale, biologically constrained model of the input layer, 4Cα, of macaque V1. We called the new model CSY2. We challenged CSY2 with a three-parameter family of visual stimuli that varied in contrast, orientation, and spatial frequency. CSY2 accurately simulated experimental data and made many new predictions. It accounted for 1) the shapes of firing-rate-versus-contrast functions, 2) orientation and spatial frequency tuning versus contrast, and 3) the approximate contrast-invariance of cortical activity maps. Post-analysis revealed that the mechanisms that were needed to produce the successful simulations of contrast response included strong recurrent excitation and inhibition that find dynamic equilibria across the cortical surface, dynamic feedback between L6 and L4, and synaptic dynamics like inhibitory synaptic depression.

Changes in the Proportion of Inhibitory Interneuron Types from Sensory to Executive Areas of the Primate Neocortex: Implications for the Origins of Working Memory Representations.

Neuronal spiking activity encoding working memory (WM) is robust in primate association cortices but weak or absent in early sensory cortices. This may be linked to changes in the proportion of neuronal types across areas that influence circuits' ability to generate recurrent excitation. We recorded neuronal activity from areas middle temporal (MT), medial superior temporal (MST), and the lateral prefrontal cortex (LPFC) of monkeys performing a WM task and classified neurons as narrow (NS) and broad spiking (BS). The ratio NS/BS decreased from MT > MST > LPFC. We analyzed the Allen Institute database of ex vivo mice/human intracellular recordings to interpret our data. Our analysis suggests that NS neurons correspond to parvalbumin (PV) or somatostatin (SST) interneurons while BS neurons are pyramidal (P) cells or vasoactive intestinal peptide (VIP) interneurons. We labeled neurons in monkey tissue sections of MT/MST and LPFC and found that the proportion of PV in cortical layers 2/3 decreased, while the proportion of CR cells increased from MT/MST to LPFC. Assuming that primate CR/CB/PV cells perform similar computations as mice VIP/SST/PV cells, our results suggest that changes in the proportion of CR and PV neurons in layers 2/3 cells may favor the emergence of activity encoding WM in association areas.

Muscarinic M1 Receptors Modulate Working Memory Performance and Activity via KCNQ Potassium Channels in the Primate Prefrontal Cortex

Working memory relies on the dorsolateral prefrontal cortex (dlPFC), where microcircuits of pyramidal neurons enable persistent firing in the absence of sensory input, maintaining information through recurrent excitation. This activity relies on acetylcholine, although the molecular mechanisms for this dependence are not thoroughly understood. This study investigated the role of muscarinic M1 receptors (M1Rs) in the dlPFC using iontophoresis coupled with single-unit recordings from aging monkeys with naturally occurring cholinergic depletion. We found that M1R stimulation produced an inverted-U dose response on cell firing and behavioral performance when given systemically to aged monkeys. Immunoelectron microscopy localized KCNQ isoforms (Kv7.2, Kv7.3, and Kv7.5) on layer III dendrites and spines, similar to M1Rs. Iontophoretic manipulation of KCNQ channels altered cell firing and reversed the effects of M1R compounds, suggesting that KCNQ channels are one mechanism for M1R actions in the dlPFC. These results indicate that M1Rs may be an appropriate target to treat cognitive disorders with cholinergic alterations.

Recurrent interactions in local cortical circuits.

Most cortical synapses are local and excitatory. Local recurrent circuits could implement amplification, allowing pattern completion and other computations1-4. Cortical circuits contain subnetworks that consist of neurons with similar receptive fields and increased connectivity relative to the network average5,6. Cortical neurons that encode different types of information are spatially intermingled and distributed over large brain volumes5-7, and this complexity has hindered attempts to probe the function of these subnetworks by perturbing them individually8. Here we use computational modelling, optical recordings and manipulations to probe the function of recurrent coupling in layer 2/3 of the mouse vibrissal somatosensory cortex during active tactile discrimination. A neural circuit model of layer 2/3 revealed that recurrent excitation enhances sensory signals by amplification, but only for subnetworks with increased connectivity. Model networks with high amplification were sensitive to damage: loss of a few members of the subnetwork degraded stimulus encoding. We tested this prediction by mapping neuronal selectivity7 and photoablating9,10 neurons with specific selectivity. Ablation of a small proportion of layer 2/3 neurons (10-20, less than 5% of the total) representing touch markedly reduced responses in the spared touch representation, but not in other representations. Ablations most strongly affected neurons with stimulus responses that were similar to those of the ablated population, which is also consistent with network models. Recurrence among cortical neurons with similar selectivity therefore drives input-specific amplification during behaviour.