Simulations of cortical networks using spatially extended conductance-based neuronal models.

The Hodgkin-Huxley model of action potential generation and propagation, published in the Journal of Physiology in 1952, initiated the field of biophysically detailed computational modelling in neuroscience, which has expanded to encompass a variety of species and components of the nervous system. Here we review the developments in this area with a focus on efforts in the community towards modelling the mammalian neocortex using spatially extended conductance-based neuronal models. The Hodgkin-Huxley formalism and related foundational contributions, such as Rall's cable theory, remain widely used in these efforts to the current day. We argue that at present the field is undergoing a qualitative change due to new very rich datasets describing the composition, connectivity and functional activity of cortical circuits, which are being integrated systematically into large-scale network models. This trend, combined with the accelerating development of convenient software tools supporting such complex modelling projects, is giving rise to highly detailed models of the cortex that are extensively constrained by the data, enabling computational investigation of a multitude of questions about cortical structure and function.

Human anterior insula signals salience and deviations from expectations via bursts of beta oscillations.

Functional imaging studies indicate that the insula encodes the salience of stimuli and deviations from expectations, signals that can mobilize cognitive resources and facilitate learning. However, there is no information about the physiological underpinnings of these phenomena beyond changing BOLD signals. To shed light on this question, we analyzed intracerebral local field potentials (LFPs) in five patients with epilepsy of both genders performing a virtual reality task that featured varying odds of monetary rewards and losses. Upon outcome disclosure, the anterior (but not the posterior) insula generated bursts of beta oscillations whose amplitudes were lower for neutral than positive and negative outcomes, consistent with a salience signal. Moreover, beta burst power was higher when outcomes deviated from expectations, whether the outcome was better or worse than expected, indicating that the insula provides an unsigned prediction error signal. Last, in relation to insular beta bursts, many higher-order cortical areas exhibited robust changes in LFP activity that ranged from spectrally nonspecific or differentiated increases in gamma power to bursts of beta activity that closely resembled the insular beta bursts themselves. Critically, the activity of these other cortical regions was more closely tied in time to insular bursts than task events, suggesting that they are associated with particularly significant cognitive phenomena. Overall, our findings suggest that the insula signals salience and prediction errors via amplitude modulations of beta bursts, which coincide with the near simultaneous recruitment of vast cortical territories.NEW & NOTEWORTHY Functional imaging studies indicate that the anterior insula encodes salience and deviations from expectations. Beyond changing BOLD signals, however, the physiological underpinnings of these signals are unknown. By recording local field potentials in patients with epilepsy, we found that the anterior insula generates large bursts of beta oscillations whose amplitude is modulated by the salience of outcomes and deviations from expectations. Moreover, insular beta bursts coincide with the activation of many high-order cortical areas.

Incorporating 3D-printing technology in the design of head-caps and electrode drives for recording neurons in multiple brain regions.

Recent advances in recording and computing hardware have enabled laboratories to record the electrical activity of multiple brain regions simultaneously. Lagging behind these technical advances, however, are the methods needed to rapidly produce microdrives and head-caps that can flexibly accommodate different recording configurations. Indeed, most available designs target single or adjacent brain regions, and, if multiple sites are targeted, specially constructed head-caps are used. Here, we present a novel design style, for both microdrives and head-caps, which takes advantage of three-dimensional printing technology. This design facilitates targeting of multiple brain regions in various configurations. Moreover, the parts are easily fabricated in large quantities, with only minor hand-tooling and finishing required.

High-frequency oscillations are prominent in the extended amygdala.

Previously, it was reported that various cortical and subcortical structures display high-frequency local field potential (LFP) oscillations in the 110- to 160-Hz range (HFOs), distinct from sharp-wave ripples. In the present study, we characterize HFOs in the extended amygdala. Rats were implanted with tetrode bundles in the bed nucleus of the stria terminalis (BNST), central amygdala (CeA), as well as adjacent regions (pallidum, caudate-putamen, and lateral septum). At all recorded sites, HFO power showed a systematic dependence on behavioral state: highest during quiet wakefulness, intermediate during paradoxical sleep, and lowest during active waking or slow-wave sleep. CO2 asphyxiation as well as anesthesia with isoflurane or urethane abolished HFOs. HFOs stood out relative to all other fast-frequency LFP components because they were highly coherent between distant sites of the extended amygdala, ipsi- and contralaterally. HFOs affected neuronal firing in two ways: firing rate could vary as a function of HFO power (rate modulation) or HFOs could entrain firing on a cycle-to-cycle basis (phase modulation). The incidence of phase-modulated neurons was about twice higher in BNST and CeA (20-40%) than in adjacent regions (≤8%). Among BNST and CeA neurons, many more were phase-modulated than rate-modulated, although about half of the latter were also phase-modulated. Overall, these results indicate that HFOs entrain the activity of a high proportion of neurons in the extended amygdala. A major challenge for future studies will be to identify the mechanisms supporting the high coherence of HFOs within and across hemispheres.

Neuronal correlates of fear conditioning in the bed nucleus of the stria terminalis.

Lesion and inactivation studies indicate that the central amygdala (CeA) participates in the expression of cued and contextual fear, whereas the bed nucleus of the stria terminalis (BNST) is only involved in the latter. The basis for this functional dissociation is unclear because CeA and BNST form similar connections with the amygdala and brainstem fear effectors. To address this question, we recorded neurons in the anterolateral (AL) and anteromedial (AM) regions of BNST in rats subjected to auditory fear conditioning. During habituation, few neurons were responsive to the conditioned stimulus (CS). After fear conditioning, 20% of BNST-AL neurons developed inhibitory responses to the CS. In BNST-AM, 26% of neurons developed positive CS responses. The behavior of BNST-AM and -AL neurons during contextual fear paralleled their CS responsiveness: More BNST-AM neurons fired at higher rates during contextual freezing than movement, whereas the opposite was seen in BNST-AL cells. These findings point to regional differences in the activity of BNST-AL and -AM in relation to learned fear, raising the possibility that they exert opposite influences on fear output networks. However, given the similar behavior of BNST-AM and -AL neurons in relation to cued and contextual fear, it remains unclear why lesion and inactivation of BNST differentially affect these two types of fear. Either neurons in a different BNST sector, not explored here, show a different activity profile in relation to the two forms of fear or inactivation/lesion studies inadvertently affected a structure adjacent to BNST, which is involved in contextual fear.

Characterization of voltage-gated K+ currents contributing to subthreshold membrane potential oscillations in hippocampal CA1 interneurons.

CA1 inhibitory interneurons at the stratum lacunosum-moleculare and radiatum junction (LM/RAD-INs) display subthreshold membrane potential oscillations (MPOs) involving voltage-dependent Na(+) and A-type K(+) currents. LM/RAD-INs also express other voltage-gated K(+) currents, although their properties and role in MPOs remain unclear. Here, we characterized these voltage-gated K(+) currents and investigated their role in MPOs. Using outside-out patch recordings from LM/RAD-IN somata, we distinguished four voltage-gated K(+) currents based on their pharmacology and activation/inactivation properties: a fast delayed rectifier current (I(Kfast)), a slow delayed rectifier current (I(Kslow)), a rapidly inactivating A-type current (I(A)), and a slowly inactivating current (I(D)). Their relative contribution to the total K(+) current was I(A) > I(Kfast) > I(Kslow) = I(D). The presence of I(D) and the relative contributions of K(+) currents in LM/RAD-INs are different from those of other CA1 interneurons, suggesting the presence of differential complement of K(+) currents in subgroups of interneurons. We next determined whether these K(+) currents were sufficient for MPO generation using a single-compartment model of LM/RAD-INs. The model captured the subthreshold voltage dependence of MPOs. Moreover, all K(+) currents were active at subthreshold potentials but I(D), I(A), and the persistent sodium current (I(NaP)) were most active near threshold. Using impedance analysis, we found that I(A) and I(NaP) contribute to MPO generation by modulating peak spectral frequency during MPOs and governing the voltage range over which MPOs occur. Our findings uncover a differential expression of a complement of K(+) channels that underlies intrinsic rhythmic activity in inhibitory interneurons.

Detection of Multiway Gamma Coordination Reveals How Frequency Mixing Shapes Neural Dynamics.

A principle of communication technology, frequency mixing, describes how novel oscillations are generated when rhythmic inputs converge on a nonlinearly activating target. As expected given that neurons are nonlinear integrators, it was demonstrated that neuronal networks exhibit mixing in response to imposed oscillations of known frequencies. However, determining when mixing occurs in spontaneous conditions, where weaker or more variable rhythms prevail, has remained impractical. Here, we show that, by exploiting the predicted phase (rather than frequency) relationships between oscillations, the contributions of mixing can be readily identified, even in small samples of noisy data. Assessment of extracellular data using this approach revealed that frequency mixing is widely expressed in a state- and region-dependent manner and that oscillations emerging from mixing entrain unit activity. Frequency mixing is thus intrinsic to the structure of neural activity and contributes importantly to neural dynamics.

Vigilance-Associated Gamma Oscillations Coordinate the Ensemble Activity of Basolateral Amygdala Neurons.

Principal basolateral amygdala (BL) neurons profoundly influence motivated behaviors, yet few of them are activated by emotionally valenced stimuli. Here, we show that a likely explanation for this paradox is the synchronizing influence of the high-gamma rhythm. High-gamma (75-95 Hz) entrained BL firing more strongly than all other rhythms. It was most pronounced during states of increased vigilance, when rats were apprehensive. Relative to behavioral states, high-gamma produced minor changes in firing rates yet dramatic increases in synchrony. Moreover, connected pairs of cells showed similarly high levels of entrainment and synchronization. Unexpectedly, prefrontal- and accumbens-projecting cells, respectively, showed high and low entrainment by high-gamma, indicating that this rhythm differentially synchronizes the activity of BL neurons projecting to specific sites. Overall, our findings suggest that individual BL neurons encode information not only by changing their firing rates, but also by synchronizing their collective activity, amplifying their impact on target structures.

Differential Recruitment of Competing Valence-Related Amygdala Networks during Anxiety.

The basolateral amygdala (BL) is involved in fear and anxiety, but it is currently unclear how the same network supports these two states. To address this question, we trained rats on appetitive and aversive conditioning in different contexts. Distinct groups of BL neurons displayed increased activity during appetitive (CS-R) versus aversive (CS-S) conditioned stimuli (R cells and S cells, respectively), and they were typically inhibited by the other CS. When the CS-S was presented in the safe context, rats entered a long-lasting, anxiety-like state characterized by increased inter-CS freezing and impaired reward seeking. During this state, a subset of BL cells ("state cells") showed sustained shifts in baseline activity whose time course matched that of the behavioral changes. Many state cells with increased firing rates were S cells, whereas R cells only included state cells with reduced firing rates. Thus, anxiety involves persistent activity changes that are differentially expressed by subsets of valence-specific BL neurons.

Basolateral amygdala nucleus responses to appetitive conditioned stimuli correlate with variations in conditioned behaviour.

In the lateral amygdala (LA), training-induced increases in neuronal responsiveness to conditioned stimuli (CSs) reflect potentiated sensory responses that drive conditioned behaviours (CRs) via LA's targets. The basolateral nucleus of the amygdala (BL) receives LA inputs and projects to various subcortical sites that can drive aversive and appetitive CRs. Consistent with this, BL neurons also develop increased responses to CSs that predict rewarding or aversive outcomes. This increased BL activity is thought to reflect the potentiated sensory responses of LA neurons. Here we contrast the CS-related activity of BL neurons when rats produced the expected CR or not, to show that cells activated by appetitive CSs mainly encode behavioural output, not CS identity. The strong dependence of BL activity on behaviour irrespective of CS identity suggests that feedforward connectivity from LA to BL can be overridden by other BL inputs.