Coherent Firing Research Articles

Entorhinal-hippocampal interactions lead to globally coherent representations of space.

The firing maps of grid cells in the entorhinal cortex are thought to provide an efficient metric system capable of supporting spatial inference in all environments. However, whether spatial representations of grid cells are determined by local environment cues or are organized into globally coherent patterns remains undetermined. We propose a navigation model containing a path integration system in the entorhinal cortex and a cognitive map system in the hippocampus. In the path integration system, grid cell network and head direction (HD) cell network integrate movement and visual information, and form attractor states to represent the positions and head directions of the animal. In the cognitive map system, a topological map is constructed capturing the attractor states of the path integration system as nodes and the transitions between attractor states as links. On loop closure, when the animal revisits a familiar place, the topological map is calibrated to minimize odometry errors. The change of the topological map is mapped back to the path integration system, to correct the states of the grid cells and the HD cells. The proposed model was tested on iRat, a rat-like miniature robot, in a realistic maze. Experimental results showed that, after familiarization of the environment, both grid cells and HD cells develop globally coherent firing maps by map calibration and activity correction. These results demonstrate that the hippocampus and the entorhinal cortex work together to form globally coherent metric representations of the environment. The underlying mechanisms of the hippocampal-entorhinal circuit in capturing the structure of the environment from sequences of experience are critical for understanding episodic memory.

Neurodegenerative damage reduces firing coherence in a continuous attractor model of grid cells.

Grid cells in the dorsolateral band of the medial entorhinal cortex (dMEC) display strikingly regular periodic firing patterns on a lattice of positions in two-dimensional (2D) space. This helps animals to encode relative spatial location without reference to external cues. The dMEC is damaged in the early stages of Alzheimer's disease, which affects navigation ability of a disease victim, reducing the synaptic density of neurons in the network. Within an established two-dimensional continuous attractor neural network model of grid cell activity, we introduce neural sheet damage parametrized by radius and by the strength of the synaptic output for neurons in the damaged region. The mean proportionality of the grid field flow rate in the dMEC to the velocity of the model animal is maintained, but there is a broadened distribution of flow rates in the damaged case. This flow-rate-to-velocity proportionality is essential to establish coherent grid firing fields for individual grid cells for a roaming animal. When we examine the coherence of the grid cell firing field by studying Bragg peaks of the Fourier transformed lattice firing field intensity in both damaged and undamaged regions, we find that, for a wide range of damage radius and reduced synaptic strength, for undamaged model grid cells there is an incoherent firing field structure with only a single central peak. In the radius-damage plane this is adjacent to narrow bands of striped lattices (two additional Bragg peaks), which abut an orthorhombic pattern (four additional Bragg peaks), that abuts the undamaged hexagonal region (six additional Bragg peaks). Within the damaged region, grid cells show no Bragg peaks outside the central one, which shows reduced intensity with increasing damage, and outside the damaged region the central Bragg peak strength is largely unaffected. There is a reentrant region of normal grid firing fields for very large damage area. We anticipate that the modified grid cell behavior can be observed in noninvasive functional magnetic resonance imaging (fMRI) of the dMEC.

Dynamic coordination of the perirhinal cortical neurons supports coherent representations between task epochs

Cortical neurons show distinct firing patterns across multiple task epochs characterized by different computations. Recent studies suggest that such distinct patterns underlie dynamic population code achieving computational flexibility, whereas neurons in some cortical areas often show coherent firing patterns across epochs. To understand how coherent single-neuron code contributes to dynamic population code, we analyzed neural responses in the rat perirhinal cortex (PRC) during cue and reward epochs of a two-alternative forced-choice task. We found that the PRC neurons often encoded the opposite choice directions between those epochs. By using principal component analysis as a population-level analysis, we identified neural subspaces associated with each epoch, which reflected coordination across the neurons. The cue and reward epochs shared neural dimensions where the choice directions were consistently discriminated. Interestingly, those dimensions were supported by dynamically changing contributions of the individual neurons. These results demonstrated heterogeneity of coherent single-neuron representations in their contributions to population code.

Striatal network modeling in Huntington's Disease.

Medium spiny neurons (MSNs) comprise over 90% of cells in the striatum. In vivo MSNs display coherent burst firing cell assembly activity patterns, even though isolated MSNs do not burst fire intrinsically. This activity is important for the learning and execution of action sequences and is characteristically dysregulated in Huntington's Disease (HD). However, how dysregulation is caused by the various neural pathologies affecting MSNs in HD is unknown. Previous modeling work using simple cell models has shown that cell assembly activity patterns can emerge as a result of MSN inhibitory network interactions. Here, by directly estimating MSN network model parameters from single unit spiking data, we show that a network composed of much more physiologically detailed MSNs provides an excellent quantitative fit to wild type (WT) mouse spiking data, but only when network parameters are appropriate for the striatum. We find the WT MSN network is situated in a regime close to a transition from stable to strongly fluctuating network dynamics. This regime facilitates the generation of low-dimensional slowly varying coherent activity patterns and confers high sensitivity to variations in cortical driving. By re-estimating the model on HD spiking data we discover network parameter modifications are consistent across three very different types of HD mutant mouse models (YAC128, Q175, R6/2). In striking agreement with the known pathophysiology we find feedforward excitatory drive is reduced in HD compared to WT mice, while recurrent inhibition also shows phenotype dependency. We show that these modifications shift the HD MSN network to a sub-optimal regime where higher dimensional incoherent rapidly fluctuating activity predominates. Our results provide insight into a diverse range of experimental findings in HD, including cognitive and motor symptoms, and may suggest new avenues for treatment.

Effects of temporally correlated noise on coherence resonance chimeras in FitzHugh-Nagumo neurons

Chimera state in neuronal network means the coexistence of coherent and incoherent firing patterns. In this paper, the FitzHugh-Nagumo (FHN) neuronal model is employed to investigate the coherent resonance chimeras induced by temporally correlated noises. We show that the oscillation state of neuronal system is influenced by the correlation time of noise, the reduction of correlation time can result in a smaller amplitude noise sufficient to maximize the coherent resonance. However, the coherent resonance chimeras can be observed by changing both noise intensity and correlation time in the nonlocally coupled FHN neural network. By virtue of statistical synchronization factor and coherence measurement, it is found that the region of noise intensity threshold for generating coherent resonance chimeras is increased with the increasing of correlation time. The collective behavior of the system is particularly sensitive to the variation of noise intensity when correlation time is greater than 0.1. Furthermore, we demonstrate that the variation of noise intensity or correlation time can cause coherent resonance multi-chimeras in the neural network.

Neurons of the inferior olive respond to broad classes of sensory input while subject to homeostatic control.

Key points Purkinje cells in the cerebellum integrate input from sensory organs with that from premotor centres.Purkinje cells use a variety of sensory inputs relaying information from the environment to modify motor control.Here we investigated to what extent the climbing fibre inputs to Purkinje cells signal mono‐ or multi‐sensory information, and to what extent this signalling is subject to recent history of activity.We show that individual climbing fibres convey multiple types of sensory information, together providing a rich mosaic projection pattern of sensory signals across the cerebellar cortex.Moreover, firing probability of climbing fibres following sensory stimulation depends strongly on the recent history of activity, showing a tendency to homeostatic dampening. Cerebellar Purkinje cells integrate sensory information with motor efference copies to adapt movements to behavioural and environmental requirements. They produce complex spikes that are triggered by the activity of climbing fibres originating in neurons of the inferior olive. These complex spikes can shape the onset, amplitude and direction of movements and the adaptation of such movements to sensory feedback. Clusters of nearby inferior olive neurons project to parasagittally aligned stripes of Purkinje cells, referred to as ‘microzones’. It is currently unclear to what extent individual Purkinje cells within a single microzone integrate climbing fibre inputs from multiple sources of different sensory origins, and to what extent sensory‐evoked climbing fibre responses depend on the strength and recent history of activation. Here we imaged complex spike responses in cerebellar lobule crus 1 to various types of sensory stimulation in awake mice. We find that different sensory modalities and receptive fields have a mild, but consistent, tendency to converge on individual Purkinje cells, with climbing fibres showing some degree of input‐specificity. Purkinje cells encoding the same stimulus show increased events with coherent complex spike firing and tend to lie close together. Moreover, whereas complex spike firing is only mildly affected by variations in stimulus strength, it depends strongly on the recent history of climbing fibre activity. Our data point towards a mechanism in the olivo‐cerebellar system that regulates complex spike firing during mono‐ or multi‐sensory stimulation around a relatively low set‐point, highlighting an integrative coding scheme of complex spike firing under homeostatic control.

Hippocampal Network Oscillations Rescue Memory Consolidation Deficits Caused by Sleep Loss.

Oscillations in the hippocampal network during sleep are proposed to play a role in memory storage by patterning neuronal ensemble activity. Here we show that following single-trial fear learning, sleep deprivation (which impairs memory consolidation) disrupts coherent firing rhythms in hippocampal area CA1. State-targeted optogenetic inhibition of CA1 parvalbumin-expressing (PV+) interneurons during postlearning NREM sleep, but not REM sleep or wake, disrupts contextual fear memory (CFM) consolidation in a manner similar to sleep deprivation. NREM-targeted inhibition disrupts CA1 network oscillations which predict successful memory storage. Rhythmic optogenetic activation of PV+ interneurons following learning generates CA1 oscillations with coherent principal neuron firing. This patterning of CA1 activity rescues CFM consolidation in sleep-deprived mice. Critically, behavioral and optogenetic manipulations that disrupt CFM also disrupt learning-induced stabilization of CA1 ensembles’ communication patterns in the hours following learning. Conversely, manipulations that promote CFM also promote long-term stability of CA1 communication patterns. We conclude that sleep promotes memory consolidation by generating coherent rhythms of CA1 network activity, which provide consistent communication patterns within neuronal ensembles. Most importantly, we show that this rhythmic patterning of activity is sufficient to promote long-term memory storage in the absence of sleep.

Cortically coordinated NREM thalamocortical oscillations play an essential, instructive role in visual system plasticity

Two long-standing questions in neuroscience are how sleep promotes brain plasticity and why some forms of plasticity occur preferentially during sleep vs. wake. Establishing causal relationships between specific features of sleep (e.g., network oscillations) and sleep-dependent plasticity has been difficult. Here we demonstrate that presentation of a novel visual stimulus (a single oriented grating) causes immediate, instructive changes in the firing of mouse lateral geniculate nucleus (LGN) neurons, leading to increased firing-rate responses to the presented stimulus orientation (relative to other orientations). However, stimulus presentation alone does not affect primary visual cortex (V1) neurons, which show response changes only after a period of subsequent sleep. During poststimulus nonrapid eye movement (NREM) sleep, LGN neuron overall spike-field coherence (SFC) with V1 delta (0.5-4 Hz) and spindle (7-15 Hz) oscillations increased, with neurons most responsive to the presented stimulus showing greater SFC. To test whether coherent communication between LGN and V1 was essential for cortical plasticity, we first tested the role of layer 6 corticothalamic (CT) V1 neurons in coherent firing within the LGN-V1 network. We found that rhythmic optogenetic activation of CT V1 neurons dramatically induced coherent firing in LGN neurons and, to a lesser extent, in V1 neurons in the other cortical layers. Optogenetic interference with CT feedback to LGN during poststimulus NREM sleep (but not REM or wake) disrupts coherence between LGN and V1 and also blocks sleep-dependent response changes in V1. We conclude that NREM oscillations relay information regarding prior sensory experience between the thalamus and cortex to promote cortical plasticity.

Effects of dynamic synapses, neuronal coupling, and time delay on firing of neuron

Neuronal firing plays a key role in the neuronal information transmission, and different neuronal firing patterns are reported, such as spiking, bursting. A number of neuron models are introduced to reproduce the firing patterns of single neuron or neuronal network. The key factors determining the firing pattern gain more and more attention in the study of neuron system, such as noise, network topology. Noise is able to induce sub-or super-threshold coherent neuronal firing easily, and a number of coherence resonances are reported in the noise induced firing. The network topology determines the synchronization of the firing patterns of the neuronal network, and the change of network topology may induce fruitful synchronization transitions. It is well known that synapses exhibit a high variability with a diverse origin during information transmission, such as the stochastic release of neurotransmitters, variations in chemical concentration through synapses, and spatial heterogeneity of synaptic response over dendrite tree. The collective effect of all of these factors might result in the notion of dynamic synapses. In reality, the neuronal network often involves time delay due to the ?nite signal propagation time in biological networks. Recently, neuronal networks with time delay have received considerable attention. Delay-sustained neuronal firing patterns may be relevant to neuronal networks for establishing a concept of collective information processing in the presence of delayed information transmission. According to the above-mentioned motivations, the firing dynamics of the single postsynapic neuron is investigated based on a simple postsynaptic neuron model by using numerical simulation and Fourier transform analysis. In this model, the postsynapic neuron receives dynamic synaptic currents from a population of presynaptic neurons. It is found that the firing rate resonance between the pre-and postsynaptic neuron determines the firing of the postsynaptic neuron. Stimulus currents in specific frequency range are easy to stimulate postsynaptic neuron firing. The random currents released from dynamic synapses determine the postsynaptic firing rate. Then the single postsynaptic neuron is extended to a neuronal network, in which 100 neurons connect to its 4 nearest neighbors regularly and receive delayed synaptic currents from connected neurons. All the neurons in the network receive the same dynamic synaptic currents from the presynaptic neurons. The results show that the synaptic coupling in the network is able to promote the neuron firing in the network, and time delay in the synaptic coupling could reinforce the promotion, but the mode of the promotion is not changed.

Firing regulation of fast-spiking interneurons by autaptic inhibition

Fast-spiking (FS) interneurons in the brain are self-innervated by powerful inhibitory GABAergic autaptic connections. By computational modelling, we investigate how autaptic inhibition regulates the firing response of such interneurons. Our results indicate that autaptic inhibition both boosts the current threshold for action potential generation and modulates the input-output gain of FS interneurons. The autaptic transmission delay is identified as a key parameter that controls the firing patterns and determines multistability regions of FS interneurons. Furthermore, we observe that neuronal noise influences the firing regulation of FS interneurons by autaptic inhibition and extends their dynamic range for encoding inputs. Importantly, autaptic inhibition modulates noise-induced irregular firing of FS interneurons, such that coherent firing appears at an optimal autaptic inhibition level. Our results reveal the functional roles of autaptic inhibition in taming the firing dynamics of FS interneurons.

Modulation Dynamics in the Orofacial Sensorimotor Cortex during Motor Skill Acquisition

The orofacial sensorimotor cortex is known to play a role in motor learning. However, how motor learning changes the dynamics of neuronal activity and whether these changes differ between orofacial primary motor (MIo) and somatosensory (SIo) cortices remain unknown. To address these questions, we used chronically implanted microelectrode arrays to track learning-induced changes in the activity of simultaneously recorded neurons in MIo and SIo as two naive monkeys (Macaca mulatta) were trained in a novel tongue-protrusion task. Over a period of 8-12 d, the monkeys showed behavioral improvements in task performance that were accompanied by rapid and long-lasting changes in neuronal responses in MIo and SIo occurring in parallel: (1) increases in the proportion of task-modulated neurons, (2) increases in the mutual information between tongue-protrusive force and spiking activity, (3) reductions in the across-trial firing rate variability, and (4) transient increases in coherent firing of neuronal pairs. More importantly, the time-resolved mutual information in MIo and SIo exhibited temporal alignment. While showing parallel changes, MIo neurons exhibited a bimodal distribution of peak correlation lag times between spiking activity and force, whereas SIo neurons showed a unimodal distribution. Moreover, coherent activity between pairs of MIo neurons was higher and centered around force onset compared with pairwise coherence of SIo neurons. Overall, the results suggest that the neuroplasticity in MIo and SIo occurring in parallel serves as a substrate for linking sensation and movement during sensorimotor learning, whereas the differing dynamic organizations reflect specific ways to control movement parameters as learning progresses.

Neuronal correlates of passive body rotation – an EEG study

During the last years a bilateral network of cortical and subcortical areas in the temporo-parietal cortex around a core region in the posterior insula was delineated by functional imaging studies processing vestibular information [1 – 3]. This vestibular input has to be integrated with inputs from other sensory modalities such as visual and somatosensory. Our hypothesis was that coherent oscillatory firing patterns might be crucial for mediating cross-system integration of the brain. Compared to other sensory systems, however, the data on neural correlates of vestibular function in humans via EEG remain scarce. Aim of this study was to investigate the neural correlates of the vestibular cortical network during passive body rotation by means of EEG.

Electroencephalographic field influence on calcium momentum waves

Macroscopic electroencephalographic (EEG) fields can be an explicit top-down neocortical mechanism that directly drives bottom-up processes that describe memory, attention, and other neuronal processes. The top-down mechanism considered is macrocolumnar EEG firings in neocortex, as described by a statistical mechanics of neocortical interactions (SMNI), developed as a magnetic vector potential A. The bottom-up process considered is Ca2+ waves prominent in synaptic and extracellular processes that are considered to greatly influence neuronal firings. Here, the complimentary effects are considered, i.e., the influence of A on Ca2+ momentum, p. The canonical momentum of a charged particle in an electromagnetic field, Π=p+qA (SI units), is calculated, where the charge of Ca2+ is q=−2e, e is the magnitude of the charge of an electron. Calculations demonstrate that macroscopic EEG A can be quite influential on the momentum p of Ca2+ ions, in both classical and quantum mechanics. Molecular scales of Ca2+ wave dynamics are coupled with A fields developed at macroscopic regional scales measured by coherent neuronal firing activity measured by scalp EEG. The project has three main aspects: fitting A models to EEG data as reported here, building tripartite models to develop A models, and studying long coherence times of Ca2+ waves in the presence of A due to coherent neuronal firings measured by scalp EEG. The SMNI model supports a mechanism wherein the p+qA interaction at tripartite synapses, via a dynamic centering mechanism (DCM) to control background synaptic activity, acts to maintain short-term memory (STM) during states of selective attention.

Sharp transitions of gamma coherence in inhibitory networks occur when a biological context and constraints are imposed

From several experimental and modeling studies, it has become apparent that networks of parvalbumin-positive (PV+), fast-firing interneurons play an essential role in generating population gamma rhythms [1]. Many features of these perisomatically-inhibiting PV+ cell networks influence the generation of gamma oscillations, a number of which have been explored in network models [2,3]. These include the synaptic delay, the decay time constant of the postsynaptic conductance, and whether the reversal potential is hyperpolarizing or shunting. While it is clear that all these features affect whether and how much coherence occurs at gamma frequencies, it is much less clear which features (i.e. parameters and parameter balances) may be essential in various biological contexts. To address this, we used our previously constructed network model of hippocampal CA1 PV+ cells [4]. Network size, connectivity, and cellular components would all be expected to affect the network dynamics. Thus, experimental data from an intact hippocampus in vitro was used to obtain a clear biological context, and importantly, the amount of input that these cells receive during ongoing theta population activities was estimated. Using these cellular components and input characteristics, we found that coherent gamma oscillations could emerge within experimental constraints for particular parameter balances. Here we investigate the effect of appropriately sized and connected networks. Each cell in the network received excitatory input (Iapplied ), which was heterogeneous across cells, and synaptic inhibition from presynaptic interneurons, which had a particular maximal conductance value (gPV). We systematically varied gPV and Iapplied within the experimentally determined ranges, and determined the coherence of the network model population firing and the network frequency for each combination of these values. For each of these network simulation sets, we also varied the connectivity probability as well as network size to explore how these factors affect the network’s ability to produce coherent network firing. We find that when our connection probability is similar to what has been found experimentally, our networks exhibit a sharp transition from random firing to network coherence with only a small change in Iapplied. However, as connectivity in the network is increased beyond experimentally estimated values, this sharp transition disappears. Instead, a larger window of coherence is achieved, but with a smooth transition from random to coherent firing. Similarly, decreasing the network size increases the window of coherent firing, and while these sharp transitions remain for changing network sizes, the parameter regimes in which they occur changes significantly when the network size becomes much smaller than experimental estimates. Our work indicates that CA1 fastspiking PV+ networks are capable of producing gamma population rhythms, and that perturbation in and out of coherent states can occur abruptly given the sharp transitions obtained in our network simulations. Since our simulations approximate a biological context, it may be that gating in and out of coherence is a design property to allow generation of theta/gamma network oscillations.

Experimentally constrained CA1 fast-firing parvalbumin-positive interneuron network models exhibit sharp transitions into coherent high frequency rhythms

The coupling of high frequency oscillations (HFOs; >100 Hz) and theta oscillations (3–12 Hz) in the CA1 region of rats increases during REM sleep, indicating that it may play a role in memory processing. However, it is unclear whether the CA1 region itself is capable of providing major contributions to the generation of HFOs, or if they are strictly driven through input projections. Parvalbumin-positive (PV+) interneurons may play an essential role in these oscillations due to their extensive connections with neighboring pyramidal cells, and their characteristic fast-spiking. Thus, we created mathematical network models to investigate the conditions under which networks of CA1 fast-spiking PV+ interneurons are capable of producing high frequency population rhythms. We used whole-cell patch clamp recordings of fast-spiking, PV+ cells in the CA1 region of an intact hippocampal preparation in vitro to derive cellular properties, from which we constrained an Izhikevich-type model. Novel, biologically constrained network models were constructed with these individual cell models, and we investigated networks across a range of experimentally determined excitatory inputs and inhibitory synaptic strengths. For each network, we determined network frequency and coherence. Network simulations produce coherent firing at high frequencies (>90 Hz) for parameter ranges in which PV-PV inhibitory synaptic conductances are necessarily small and external excitatory inputs are relatively large. Interestingly, our networks produce sharp transitions between random and coherent firing, and this sharpness is lost when connectivity is increased beyond biological estimates. Our work suggests that CA1 networks may be designed with mechanisms for quickly gating in and out of high frequency coherent population rhythms, which may be essential in the generation of nested theta/high frequency rhythms.

Distinct patterns of coherent firing for feature binding and selective attention in neurons of the visual cortex

Distinct patterns of coherent firing for feature binding and selective attention in neurons of the visual cortex

Electroencephalographic Field Influence on Calcium Momentum Waves

Macroscopic electroencephalographic (EEG) fields can be an explicit top-down neocortical mechanism that directly drives bottom-up processes that describe memory, attention, and other neuronal processes. The top-down mechanism considered are macrocolumnar EEG firings in neocortex, as described by a statistical mechanics of neocortical interactions (SMNI), developed as a magnetic vector potential $\mathbf{A}$. The bottom-up process considered are $\mathrm{Ca}^{2 }$ waves prominent in synaptic and extracellular processes that are considered to greatly influence neuronal firings. Here, the complimentary effects are considered, i.e., the influence of $\mathbf{A}$ on $\mathrm{Ca}^{2 }$ momentum, $\mathbf{p}$. The canonical momentum of a charged particle in an electromagnetic field, $\mathbf{\Pi} = \mathbf{p} q \mathbf{A}$ (SI units), is calculated, where the charge of $\mathrm{Ca}^{2 }$ is $q = - 2 e$, $e$ is the magnitude of the charge of an electron. Calculations demonstrate that macroscopic EEG $\mathbf{A}$ can be quite influential on the momentum $\mathbf{p}$ of $\mathrm{Ca}^{2 }$ ions, in both classical and quantum mechanics. Molecular scales of $\mathrm{Ca}^{2 }$ wave dynamics are coupled with $\mathbf{A}$ fields developed at macroscopic regional scales measured by coherent neuronal firing activity measured by scalp EEG. The project has three main aspects: fitting $\mathbf{A}$ models to EEG data as reported here, building tripartite models to develop $\mathbf{A}$ models, and studying long coherence times of $\mathrm{Ca}^{2 }$ waves in the presence of $\mathbf{A}$ due to coherent neuronal firings measured by scalp EEG. The SMNI model supports a mechanism wherein the $\mathbf{p} q \mathbf{A}$ interaction at tripartite synapses, via a dynamic centering mechanism (DCM) to control background synaptic activity, acts to maintain short-term memory (STM) during states of selective attention.

Effect of the Topology and Delayed Interactions in Neuronal Networks Synchronization

As important as the intrinsic properties of an individual nervous cell stands the network of neurons in which it is embedded and by virtue of which it acquires great part of its responsiveness and functionality. In this study we have explored how the topological properties and conduction delays of several classes of neural networks affect the capacity of their constituent cells to establish well-defined temporal relations among firing of their action potentials. This ability of a population of neurons to produce and maintain a millisecond-precise coordinated firing (either evoked by external stimuli or internally generated) is central to neural codes exploiting precise spike timing for the representation and communication of information. Our results, based on extensive simulations of conductance-based type of neurons in an oscillatory regime, indicate that only certain topologies of networks allow for a coordinated firing at a local and long-range scale simultaneously. Besides network architecture, axonal conduction delays are also observed to be another important factor in the generation of coherent spiking. We report that such communication latencies not only set the phase difference between the oscillatory activity of remote neural populations but determine whether the interconnected cells can set in any coherent firing at all. In this context, we have also investigated how the balance between the network synchronizing effects and the dispersive drift caused by inhomogeneities in natural firing frequencies across neurons is resolved. Finally, we show that the observed roles of conduction delays and frequency dispersion are not particular to canonical networks but experimentally measured anatomical networks such as the macaque cortical network can display the same type of behavior.

Executive Functions and Prefrontal Cortex: A Matter of Persistence?

Executive function is thought to originates from the dynamics of frontal cortical networks. We examined the dynamic properties of the blood oxygen level dependent time-series measured with functional MRI (fMRI) within the prefrontal cortex (PFC) to test the hypothesis that temporally persistent neural activity underlies performance in three tasks of executive function. A numerical estimate of signal persistence, the Hurst exponent, postulated to represent the coherent firing of cortical networks, was determined and correlated with task performance. Increasing persistence in the lateral PFC was shown to correlate with improved performance during an n-back task. Conversely, we observed a correlation between persistence and increasing commission error – indicating a failure to inhibit a prepotent response – during a Go/No-Go task. We propose that persistence within the PFC reflects dynamic network formation and these findings underline the importance of frequency analysis of fMRI time-series in the study of executive functions.

Neural basis of visual selective attention.

Attentional modulation along the object-recognition pathway of the cortical visual system of primates has been shown to consist of enhanced representation of the retinal input at a specific location in space, or of objects located anywhere in the visual field which possess a critical object feature. Moreover, selective attention mechanisms allow the visual system to resolve competition among multiple objects in a crowded scene in favor of the object that is relevant for the current behavior. Finally, selective attention affects the spontaneous activity of neurons as well as their visually driven responses, and it does so not only by modulating the spiking activity of individual neurons, but also by modulating the degree of coherent firing within the critical neuronal populations. WIREs Cogni Sci 2011 2 392-407 DOI: 10.1002/wcs.117 For further resources related to this article, please visit the WIREs website.