Beyond bistability: Biophysics and temporal dynamics of working memory

NMDAR-mediated activation of pannexin1 channels contributes to the detonator properties of hippocampal mossy fiber synapses

Pannexins are large-pore ion channels expressed throughout the mammalian brain that participate in various neuropathologies; however, their physiological roles remain obscure. Here, we report that pannexin1 channels (Panx1) can be synaptically activated under physiological recording conditions in rodent acute hippocampal slices. Specifically, NMDA receptor (NMDAR)-mediated responses at the mossy fiber to CA3 pyramidal cell synapse were followed by a slow postsynaptic inward current that could activate CA3 pyramidal cells but was absent in Panx1 knockout mice. Immunoelectron microscopy revealed that Panx1 was localized near the postsynaptic density. Further, Panx1-mediated currents were potentiated by metabotropic receptors and bidirectionally modulated by burst-timing-dependent plasticity of NMDAR-mediated transmission. Lastly, Panx1 channels were preferentially recruited when NMDAR activation enters a supralinear regime, resulting in temporally delayed burst-firing. Thus, Panx1 can contribute to synaptic amplification and broadening the temporal associativity window for co-activated pyramidal cells, thereby supporting the auto-associative functions of the CA3 region.

Dynamic model assuming mutually inhibitory biomarkers of frailty suggests bistability with contrasting mobility phenotypes

Bistability is a fundamental biological phenomenon associated with "switch-like" behavior reflecting the capacity of a system to exist in either of two stable states. It plays a role in gene regulation, cell fate switch, signal transduction and cell oscillation, with relevance for cognition, hearing, vision, sleep, gait and voiding. Here we consider a potential role for bistability in the existence of specific frailty states or phenotypes as part of disablement pathways. We use mathematical modeling with two frailty biomarkers (insulin growth factor-1, IGF-1 and interleukin-6, IL-6), which mutually inhibit each other. In our model, we demonstrate that small variations around critical IGF-1 or IL-6 blood levels lead to strikingly different mobility outcomes. We employ deterministic modeling of mobility outcomes, calculating the average trends in population health. Our model predicts the bistability of clinical outcomes: the deterministically-computed likelihood of an individual remaining mobile, becoming less mobile, or dying over time either increases to almost 100% or decreases to almost zero. Contrary to statistical models that attempt to estimate the likelihood of final outcomes based on probabilities and correlations, our model predicts functional outcomes over time based on specific hypothesized molecular mechanisms. Instead of estimating probabilities based on stochastic distributions and arbitrary priors, we deterministically simulate model outcomes over a wide range of physiological parameter values within experimentally derived boundaries. Our study is "a proof of principle" as it is based on a major assumption about mutual inhibition of pathways that is oversimplified. However, by making such an assumption, interesting effects can be described qualitatively. As our understanding of molecular mechanisms involved in aging deepens, we believe that such modeling will not only lead to more accurate predictions, but also help move the field from using mostly studies of associations to mechanistically guided approaches.

Distinct hippocampal-prefrontal neural assemblies coordinate memory encoding, maintenance, and recall

Short-term memory enables incorporation of recent experience into subsequent decision-making. This processing recruits both the prefrontal cortex and hippocampus, where neurons encode task cues, rules, and outcomes. However, precisely which information is carried when, and by which neurons, remains unclear. Using population decoding of activity in rat medial prefrontal cortex (mPFC) and dorsal hippocampal CA1, we confirm that mPFC populations lead in maintaining sample information across delays of an operant non-match to sample task, despite individual neurons firing only transiently. During sample encoding, distinct mPFC subpopulations joined distributed CA1-mPFC cell assemblies hallmarked by 4-5 Hz rhythmic modulation; CA1-mPFC assemblies re-emerged during choice episodes but were not 4-5 Hz modulated. Delay-dependent errors arose when attenuated rhythmic assembly activity heralded collapse of sustained mPFC encoding. Our results map component processes of memory-guided decisions onto heterogeneous CA1-mPFC subpopulations and the dynamics of physiologically distinct, distributed cell assemblies.

Residual dynamics resolves recurrent contributions to neural computation

Relating neural activity to behavior requires an understanding of how neural computations arise from the coordinated dynamics of distributed, recurrently connected neural populations. However, inferring the nature of recurrent dynamics from partial recordings of a neural circuit presents considerable challenges. Here we show that some of these challenges can be overcome by a fine-grained analysis of the dynamics of neural residuals-that is, trial-by-trial variability around the mean neural population trajectory for a given task condition. Residual dynamics in macaque prefrontal cortex (PFC) in a saccade-based perceptual decision-making task reveals recurrent dynamics that is time dependent, but consistently stable, and suggests that pronounced rotational structure in PFC trajectories during saccades is driven by inputs from upstream areas. The properties of residual dynamics restrict the possible contributions of PFC to decision-making and saccade generation and suggest a path toward fully characterizing distributed neural computations with large-scale neural recordings and targeted causal perturbations.

A multi-faceted role of dual-state dopamine signaling in working memory, attentional control, and intelligence

Genetic evidence strongly suggests that individual differences in intelligence will not be reducible to a single dominant cause. However, some of those variations/changes may be traced to tractable, cohesive mechanisms. One such mechanism may be the balance of dopamine D1 (D₁R) and D2 (D₂R) receptors, which regulate intrinsic currents and synaptic transmission in frontal cortical regions. Here, we review evidence from human, animal, and computational studies that suggest that this balance (in density, activity state, and/or availability) is critical to the implementation of executive functions such as attention and working memory, both of which are principal contributors to variations in intelligence. D1 receptors dominate neural responding during stable periods of short-term memory maintenance (requiring attentional focus), while D2 receptors play a more specific role during periods of instability such as changing environmental or memory states (requiring attentional disengagement). Here we bridge these observations with known properties of human intelligence. Starting from theories of intelligence that place executive functions (e.g., working memory and attentional control) at its center, we propose that dual-state dopamine signaling might be a causal contributor to at least some of the variation in intelligence across individuals and its change by experiences/training. Although it is unlikely that such a mechanism can account for more than a modest portion of the total variance in intelligence, our proposal is consistent with an array of available evidence and has a high degree of explanatory value. We suggest future directions and specific empirical tests that can further elucidate these relationships.

Multistability and evolution of chimera states in a network of type II Morris-Lecar neurons with asymmetrical nonlocal inhibitory connections

Formation of synchronous activity patterns is an essential property of neuronal networks that has been of central interest to synchronization theory. Chimera states, where both synchronous and asynchronous activities of neurons co-exist in a single network, are particularly poignant examples of such patterns, whose dynamics and multistability may underlie brain function, such as cognitive tasks. However, dynamical mechanisms of coherent state formation in spiking neuronal networks as well as ways to control these states remain unclear. In this paper, we take a step in this direction by considering the evolution of chimera states in a network of class II excitable Morris-Lecar neurons with asymmetrical nonlocal inhibitory connections. Using the adaptive coherence measure, we are able to partition the network parameter space into regions of various collective behaviors (antiphase synchronous clusters, traveling waves, different types of chimera states as well as a spiking death regime) and have shown multistability between the various regimes. We track the evolution of the chimera states as a function of changed key network parameters and found transitions between various types of chimera states. We further find that the network can demonstrate long transients leading to quasi-persistence of activity patterns in the border regions hinting at near-criticality behaviors.

Nonlinear optimal control of a mean-field model of neural population dynamics

We apply the framework of nonlinear optimal control to a biophysically realistic neural mass model, which consists of two mutually coupled populations of deterministic excitatory and inhibitory neurons. External control signals are realized by time-dependent inputs to both populations. Optimality is defined by two alternative cost functions that trade the deviation of the controlled variable from its target value against the “strength” of the control, which is quantified by the integrated 1- and 2-norms of the control signal. We focus on a bistable region in state space where one low- (“down state”) and one high-activity (“up state”) stable fixed points coexist. With methods of nonlinear optimal control, we search for the most cost-efficient control function to switch between both activity states. For a broad range of parameters, we find that cost-efficient control strategies consist of a pulse of finite duration to push the state variables only minimally into the basin of attraction of the target state. This strategy only breaks down once we impose time constraints that force the system to switch on a time scale comparable to the duration of the control pulse. Penalizing control strength via the integrated 1-norm (2-norm) yields control inputs targeting one or both populations. However, whether control inputs to the excitatory or the inhibitory population dominate, depends on the location in state space relative to the bifurcation lines. Our study highlights the applicability of nonlinear optimal control to understand neuronal processing under constraints better.

The what, where and how of delay activity

Working memory is characterized by neural activity that persists during the retention interval of delay tasks. Despite the ubiquity of this delay activity across tasks, species and experimental techniques, our understanding of this phenomenon remains incomplete. Although initially there was a narrow focus on sustained activation in a small number of brain regions, methodological and analytical advances have allowed researchers to uncover previously unobserved forms of delay activity various parts of the brain. In light of these new findings, this Review reconsiders what delay activity is, where in the brain it is found, what roles it serves and how it may be generated.

Dynamically changing neuronal activity supporting working memory for predictable and unpredictable durations

Diverse neural processes have been proposed as the neural basis of working memory. To investigate whether the medial prefrontal cortex (mPFC) relies on different neural processes to mediate working memory depending on the predictability of delay duration, we examined mPFC neural activity in mice performing a delayed response task with fixed (4 s) or random (between 1-7 s) delay durations. mPFC neural activity was strongly influenced by the predictability of delay duration. Nevertheless, mPFC neurons seldom showed persistent activity spanning the entire delay period and instead showed dynamically-changing delay-period activity under both the fixed-delay and random-delay conditions. mPFC neurons conveyed higher working memory information under the random-delay than fixed-delay conditions, possibly due to a higher demand for stable working memory maintenance. Our results suggest that the rodent mPFC may rely on dynamically-changing neuronal activity to maintain working memory regardless of the predictability of delay duration.

Distinct Dynamics of Striatal and Prefrontal Neural Activity During Temporal Discrimination

The frontal cortex-basal ganglia circuit plays an important role in interval timing. We examined neuronal discharges in the dorsomedial and dorsolateral striatum (DMS and DLS) in rats performing a temporal categorization task and compared them with previously recorded neuronal activity in the medial prefrontal cortex (mPFC). All three structures conveyed significant temporal information, but striatal neurons seldom showed the prolonged, full-interval spanning ramping activity frequently observed in the mPFC. Instead, the majority fired briefly during sample intervals. Also, the precision of neural time decoding became progressively worse with increasing time duration in the mPFC, but not in the striatum. With the caveat that mPFC and striatal units were recorded from different animals, our results suggest that the striatum and mPFC convey temporal information via distinct neural processes.

Compressed Timeline of Recent Experience in Monkey Lateral Prefrontal Cortex

Cognitive theories suggest that working memory maintains not only the identity of recently presented stimuli but also a sense of the elapsed time since the stimuli were presented. Previous studies of the neural underpinnings of working memory have focused on sustained firing, which can account for maintenance of the stimulus identity, but not for representation of the elapsed time. We analyzed single-unit recordings from the lateral prefrontal cortex of macaque monkeys during performance of a delayed match-to-category task. Each sample stimulus triggered a consistent sequence of neurons, with each neuron in the sequence firing during a circumscribed period. These sequences of neurons encoded both stimulus identity and elapsed time. The encoding of elapsed time became less precise as the sample stimulus receded into the past. These findings suggest that working memory includes a compressed timeline of what happened when, consistent with long-standing cognitive theories of human memory.

Temporal Dynamics of Hippocampal and Medial Prefrontal Cortex Interactions During the Delay Period of a Working Memory-Guided Foraging Task

Connections between the hippocampus (HC) and medial prefrontal cortex (mPFC) are critical for working memory; however, the precise contribution of this pathway is a matter of debate. One suggestion is that it may stabilize retrospective memories of recently encountered task-relevant information. Alternatively, it may be involved in encoding prospective memories, or the internal representation of future goals. To explore these possibilities, simultaneous extracellular recordings were made from mPFC and HC of rats performing the delayed spatial win-shift on a radial maze. Each trial consisted of a training-phase (when 4 randomly chosen arms were open) and test phase (all 8 arms were open but only previously blocked arms contained food) separated by a 60-s delay. Theta power was highest during the delay, and mPFC units were more likely to become entrained to hippocampal theta as the delay progressed. Training and test phase performance were accurately predicted by a linear classifier, and there was a transition in classification for training-phase to test-phase activity patterns throughout the delay on trials where the rats performed well. These data suggest that the HC and mPFC become more strongly synchronized as mPFC circuits preferentially shift from encoding retrospective to prospective information.

Propensity for Bistability of Bursting and Silence in the Leech Heart Interneuron

The coexistence of neuronal activity regimes has been reported under normal and pathological conditions. Such multistability could enhance the flexibility of the nervous system and has many implications for motor control, memory, and decision making. Multistability is commonly promoted by neuromodulation targeting specific membrane ionic currents. Here, we investigated how modulation of different ionic currents could affect the neuronal propensity for bistability. We considered a leech heart interneuron model. It exhibits bistability of bursting and silence in a narrow range of the leak current parameters, conductance (g_leak) and reversal potential (E_leak). We assessed the propensity for bistability of the model by using bifurcation diagrams. On the diagram (g_leak, E_leak), we mapped bursting and silent regimes. For the canonical value of E_leak we determined the range of g_leak which supported the bistability. We use this range as an index of propensity for bistability. We investigated how this index was affected by alterations of ionic currents. We systematically changed their conductances, one at a time, and built corresponding bifurcation diagrams in parameter planes of the maximal conductance of a given current and the leak conductance. We found that conductance of only one current substantially affected the index of propensity; the increase of the maximal conductance of the hyperpolarization-activated cationic current increased the propensity index. The second conductance with the strongest effect was the conductance of the low-threshold fast Ca²⁺ current; its reduction increased the propensity index although the effect was about two times smaller in magnitude. Analyzing the model with both changes applied simultaneously, we found that the diagram (g_leak, E_leak) showed a progressively expanded area of bistability of bursting and silence.

Gamma and beta bursts during working memory readout suggest roles in its volitional control

Working memory (WM) activity is not as stationary or sustained as previously thought. There are brief bursts of gamma (~50–120 Hz) and beta (~20–35 Hz) oscillations, the former linked to stimulus information in spiking. We examined these dynamics in relation to readout and control mechanisms of WM. Monkeys held sequences of two objects in WM to match to subsequent sequences. Changes in beta and gamma bursting suggested their distinct roles. In anticipation of having to use an object for the match decision, there was an increase in gamma and spiking information about that object and reduced beta bursting. This readout signal was only seen before relevant test objects, and was related to premotor activity. When the objects were no longer needed, beta increased and gamma decreased together with object spiking information. Deviations from these dynamics predicted behavioral errors. Thus, beta could regulate gamma and the information in WM.

Previously, the authors have shown that working memory can be maintained by brief gamma oscillation bursts. Here, the authors use a new task to further demonstrate the dynamics of gamma and beta oscillations in working memory readout, independent of behavioral response.

Nonlinear Relationship Between Spike-Dependent Calcium Influx and TRPC Channel Activation Enables Robust Persistent Spiking in Neurons of the Anterior Cingulate Cortex

Continuation of spiking after a stimulus ends (i.e. persistent spiking) is thought to support working memory. Muscarinic receptor activation enables persistent spiking among synaptically isolated pyramidal neurons in anterior cingulate cortex (ACC), but a detailed characterization of that spiking is lacking and the underlying mechanisms remain unclear. Here, we show that the rate of persistent spiking in ACC neurons is insensitive to the intensity and number of triggers, but can be modulated by injected current, and that persistent spiking can resume after several seconds of hyperpolarization-imposed quiescence. Using electrophysiology and calcium imaging in brain slices from male rats, we determined that canonical transient receptor potential (TRPC) channels are necessary for persistent spiking and that TRPC-activating calcium enters in a spike-dependent manner via voltage-gated calcium channels. Constrained by these biophysical details, we built a computational model that reproduced the observed pattern of persistent spiking. Nonlinear dynamical analysis of that model revealed that TRPC channels become fully activated by the small rise in intracellular calcium caused by evoked spikes. Calcium continues to rise during persistent spiking, but because TRPC channel activation saturates, firing rate stabilizes. By calcium rising higher than required for maximal TRPC channel activation, TRPC channels are able to remain active during periods of hyperpolarization-imposed quiescence (until calcium drops below saturating levels) such that persistent spiking can resume when hyperpolarization is discontinued. Our results thus reveal that the robust intrinsic bistability exhibited by ACC neurons emerges from the nonlinear positive feedback relationship between spike-dependent calcium influx and TRPC channel activation.SIGNIFICANCE STATEMENT Neurons use action potentials, or spikes, to encode information. Some neurons can store information for short periods (seconds to minutes) by continuing to spike after a stimulus ends, thus enabling working memory. This so-called "persistent" spiking occurs in many brain areas and has been linked to activation of canonical transient receptor potential (TRPC) channels. However, TRPC activation alone is insufficient to explain many aspects of persistent spiking such as resumption of spiking after periods of imposed quiescence. Using experiments and simulations, we show that calcium influx caused by spiking is necessary and sufficient to activate TRPC channels and that the ensuing positive feedback interaction between intracellular calcium and TRPC channel activation can account for many hitherto unexplained aspects of persistent spiking.

A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements

The computational and cognitive properties of neural systems are often thought to be implemented in terms of their (stochastic) network dynamics. Hence, recovering the system dynamics from experimentally observed neuronal time series, like multiple single-unit recordings or neuroimaging data, is an important step toward understanding its computations. Ideally, one would not only seek a (lower-dimensional) state space representation of the dynamics, but would wish to have access to its statistical properties and their generative equations for in-depth analysis. Recurrent neural networks (RNNs) are a computationally powerful and dynamically universal formal framework which has been extensively studied from both the computational and the dynamical systems perspective. Here we develop a semi-analytical maximum-likelihood estimation scheme for piecewise-linear RNNs (PLRNNs) within the statistical framework of state space models, which accounts for noise in both the underlying latent dynamics and the observation process. The Expectation-Maximization algorithm is used to infer the latent state distribution, through a global Laplace approximation, and the PLRNN parameters iteratively. After validating the procedure on toy examples, and using inference through particle filters for comparison, the approach is applied to multiple single-unit recordings from the rodent anterior cingulate cortex (ACC) obtained during performance of a classical working memory task, delayed alternation. Models estimated from kernel-smoothed spike time data were able to capture the essential computational dynamics underlying task performance, including stimulus-selective delay activity. The estimated models were rarely multi-stable, however, but rather were tuned to exhibit slow dynamics in the vicinity of a bifurcation point. In summary, the present work advances a semi-analytical (thus reasonably fast) maximum-likelihood estimation framework for PLRNNs that may enable to recover relevant aspects of the nonlinear dynamics underlying observed neuronal time series, and directly link these to computational properties.

Neural Noise Hypothesis of Developmental Dyslexia

Developmental dyslexia (decoding-based reading disorder; RD) is a complex trait with multifactorial origins at the genetic, neural, and cognitive levels. There is evidence that low-level sensory-processing deficits precede and underlie phonological problems, which are one of the best-documented aspects of RD. RD is also associated with impairments in integrating visual symbols with their corresponding speech sounds. Although causal relationships between sensory processing, print-speech integration, and fluent reading, and their neural bases are debated, these processes all require precise timing mechanisms across distributed brain networks. Neural excitability and neural noise are fundamental to these timing mechanisms. Here, we propose that neural noise stemming from increased neural excitability in cortical networks implicated in reading is one key distal contributor to RD.

Multiscale modeling of brain dynamics: from single neurons and networks to mathematical tools

The extreme complexity of the brain naturally requires mathematical modeling approaches on a large variety of scales; the spectrum ranges from single neuron dynamics over the behavior of groups of neurons to neuronal network activity. Thus, the connection between the microscopic scale (single neuron activity) to macroscopic behavior (emergent behavior of the collective dynamics) and vice versa is a key to understand the brain in its complexity. In this work, we attempt a review of a wide range of approaches, ranging from the modeling of single neuron dynamics to machine learning. The models include biophysical as well as data-driven phenomenological models. The discussed models include Hodgkin-Huxley, FitzHugh-Nagumo, coupled oscillators (Kuramoto oscillators, Rössler oscillators, and the Hindmarsh-Rose neuron), Integrate and Fire, networks of neurons, and neural field equations. In addition to the mathematical models, important mathematical methods in multiscale modeling and reconstruction of the causal connectivity are sketched. The methods include linear and nonlinear tools from statistics, data analysis, and time series analysis up to differential equations, dynamical systems, and bifurcation theory, including Granger causal connectivity analysis, phase synchronization connectivity analysis, principal component analysis (PCA), independent component analysis (ICA), and manifold learning algorithms such as ISOMAP, and diffusion maps and equation-free techniques. WIREs Syst Biol Med 2016, 8:438-458. doi: 10.1002/wsbm.1348 For further resources related to this article, please visit the WIREs website.

Itinerancy between attractor states in neural systems

Converging evidence from neural, perceptual and simulated data suggests that discrete attractor states form within neural circuits through learning and development. External stimuli may bias neural activity to one attractor state or cause activity to transition between several discrete states. Evidence for such transitions, whose timing can vary across trials, is best accrued through analyses that avoid any trial-averaging of data. One such method, hidden Markov modeling, has been effective in this context, revealing state transitions in many neural circuits during many tasks. Concurrently, modeling efforts have revealed computational benefits of stimulus processing via transitions between attractor states. This review describes the current state of the field, with comments on how its perceived limitations have been addressed.

Big data to smart data in Alzheimer's disease: The brain health modeling initiative to foster actionable knowledge

Massive investment and technological advances in the collection of extensive and longitudinal information on thousands of Alzheimer patients results in large amounts of data. These "big-data" databases can potentially advance CNS research and drug development. However, although necessary, they are not sufficient, and we posit that they must be matched with analytical methods that go beyond retrospective data-driven associations with various clinical phenotypes. Although these empirically derived associations can generate novel and useful hypotheses, they need to be organically integrated in a quantitative understanding of the pathology that can be actionable for drug discovery and development. We argue that mechanism-based modeling and simulation approaches, where existing domain knowledge is formally integrated using complexity science and quantitative systems pharmacology can be combined with data-driven analytics to generate predictive actionable knowledge for drug discovery programs, target validation, and optimization of clinical development.

Gamma and Beta Bursts Underlie Working Memory

Working memory is thought to result from sustained neuron spiking. However, computational models suggest complex dynamics with discrete oscillatory bursts. We analyzed local field potential (LFP) and spiking from the prefrontal cortex (PFC) of monkeys performing a working memory task. There were brief bursts of narrow-band gamma oscillations (45-100 Hz), varied in time and frequency, accompanying encoding and re-activation of sensory information. They appeared at a minority of recording sites associated with spiking reflecting the to-be-remembered items. Beta oscillations (20-35 Hz) also occurred in brief, variable bursts but reflected a default state interrupted by encoding and decoding. Only activity of neurons reflecting encoding/decoding correlated with changes in gamma burst rate. Thus, gamma bursts could gate access to, and prevent sensory interference with, working memory. This supports the hypothesis that working memory is manifested by discrete oscillatory dynamics and spiking, not sustained activity.

Temperament and arousal systems: A new synthesis of differential psychology and functional neurochemistry

This paper critically reviews the unidimensional construct of General Arousal as utilised by models of temperament in differential psychology for example, to underlie 'Extraversion'. Evidence suggests that specialization within monoamine neurotransmitter systems contrasts with the attribution of a "general arousal" of the Ascending Reticular Activating System. Experimental findings show specialized roles of noradrenaline, dopamine, and serotonin systems in hypothetically mediating three complementary forms of arousal that are similar to three functional blocks described in classical models of behaviour within kinesiology, clinical neuropsychology, psychophysiology and temperament research. In spite of functional diversity of monoamine receptors, we suggest that their functionality can be classified using three universal aspects of actions related to expansion, to selection-integration and to maintenance of chosen behavioural alternatives. Monoamine systems also differentially regulate analytic vs. routine aspects of activities at cortical and striatal neural levels. A convergence between main temperament models in terms of traits related to described functional aspects of behavioural arousal also supports the idea of differentiation between these aspects analysed here in a functional perspective.

Neuroimaging studies of striatum in cognition part II: Parkinson's disease

In recent years a gradual shift in the definition of Parkinson's disease (PD) has been established, from a classical akinetic-rigid movement disorder to a multi-system neurodegenerative disease. While the pathophysiology of PD is complex and goes much beyond the nigro-striatal degeneration, the striatum has been shown to be responsible for many cognitive functions. Patients with PD develop impairments in multiple cognitive domains and the PD model is probably the most extensively studied regarding striatum dysfunction and its influence on cognition. Up to 40% of PD patients present cognitive impairment even in the early stages of disease development. Thus, understanding the key patterns of striatum and connecting regions' influence on cognition will help develop more specific approaches to alleviate cognitive impairment and slow down its decline. This review focuses on the contribution of neuroimaging studies in understanding how striatum impairment affects cognition in PD.

Chronic cocaine disrupts mesocortical learning mechanisms

The addictive power of drugs of abuse such as cocaine comes from their ability to hijack natural reward and plasticity mechanisms mediated by dopamine signaling in the brain. Reward learning involves burst firing of midbrain dopamine neurons in response to rewards and cues predictive of reward. The resulting release of dopamine in terminal regions is thought to act as a teaching signaling to areas such as the prefrontal cortex and striatum. In this review, we posit that a pool of extrasynaptic dopaminergic D1-like receptors activated in response to dopamine neuron burst firing serve to enable synaptic plasticity in the prefrontal cortex in response to rewards and their cues. We propose that disruptions in these mechanisms following chronic cocaine use contribute to addiction pathology, in part due to the unique architecture of the mesocortical pathway. By blocking dopamine reuptake in the cortex, cocaine elevates dopamine signaling at these extrasynaptic receptors, prolonging D1-receptor activation and the subsequent activation of intracellular signaling cascades, and thus inducing long-lasting maladaptive plasticity. These cellular adaptations may account for many of the changes in cortical function observed in drug addicts, including an enduring vulnerability to relapse. Therefore, understanding and targeting these neuroadaptations may provide cognitive benefits and help prevent relapse in human drug addicts.

Oscillatory multiplexing of neural population codes for interval timing and working memory

Interval timing and working memory are critical components of cognition that are supported by neural oscillations in prefrontal-striatal-hippocampal circuits. In this review, the properties of interval timing and working memory are explored in terms of behavioral, anatomical, pharmacological, and neurophysiological findings. We then describe the various neurobiological theories that have been developed to explain these cognitive processes - largely independent of each other. Following this, a coupled excitatory - inhibitory oscillation (EIO) model of temporal processing is proposed to address the shared oscillatory properties of interval timing and working memory. Using this integrative approach, we describe a hybrid model explaining how interval timing and working memory can originate from the same oscillatory processes, but differ in terms of which dimension of the neural oscillation is utilized for the extraction of item, temporal order, and duration information. This extension of the striatal beat-frequency (SBF) model of interval timing (Matell and Meck, 2000, 2004) is based on prefrontal-striatal-hippocampal circuit dynamics and has direct relevance to the pathophysiological distortions observed in time perception and working memory in a variety of psychiatric and neurological conditions.

On the neural implementation of the speed-accuracy trade-off

Decisions are faster and less accurate when conditions favor speed, and are slower and more accurate when they favor accuracy. This phenomenon is referred to as the speed-accuracy trade-off (SAT). Behavioral studies of the SAT have a long history, and the data from these studies are well characterized within the framework of bounded integration. According to this framework, decision makers accumulate noisy evidence until the running total for one of the alternatives reaches a bound. Lower and higher bounds favor speed and accuracy respectively, each at the expense of the other. Studies addressing the neural implementation of these computations are a recent development in neuroscience. In this review, we describe the experimental and theoretical evidence provided by these studies. We structure the review according to the framework of bounded integration, describing evidence for (1) the modulation of the encoding of evidence under conditions favoring speed or accuracy, (2) the modulation of the integration of encoded evidence, and (3) the modulation of the amount of integrated evidence sufficient to make a choice. We discuss commonalities and differences between the proposed neural mechanisms, some of their assumptions and simplifications, and open questions for future work. We close by offering a unifying hypothesis on the present state of play in this nascent research field.

Unilateral electrical stimulation of rat locus coeruleus elicits bilateral response of norepinephrine neurons and sustained activation of medial prefrontal cortex

The brain stem nucleus locus coeruleus (LC) is thought to modulate cortical excitability by norepinephrine (NE) release in LC forebrain targets. The effects of LC burst discharge, typically evoked by a strong excitatory input, on cortical ongoing activity are poorly understood. To address this question, we combined direct electrical stimulation of LC (LC-DES) with extracellular recording in LC and medial prefrontal cortex (mPFC), an important cortical target of LC. LC-DES consisting of single pulses (0.1–0.5 ms, 0.01–0.05 mA) or pulse trains (20–50 Hz, 50–200 ms) evoked short-latency excitatory and inhibitory LC responses bilaterally as well as a delayed rebound excitation occurring ∼100 ms after stimulation offset. The pulse trains, but not single pulses, reliably elicited mPFC activity change, which was proportional to the stimulation strength. The firing rate of ∼50% of mPFC units was significantly modulated by the strongest LC-DES. Responses of mPFC putative pyramidal neurons included fast (∼100 ms), transient (∼100–200 ms) inhibition (10% of units) or excitation (13%) and delayed (∼500 ms), sustained (∼1 s) excitation (26%). The sustained spiking resembled NE-dependent mPFC activity during the delay period of working memory tasks. Concurrently, the low-frequency (0.1–8 Hz) power of the local field potential (LFP) decreased and high-frequency (>20 Hz) power increased. Overall, the DES-induced LC firing pattern resembled the naturalistic biphasic response of LC-NE neurons to alerting stimuli and was associated with a shift in cortical state that may optimize processing of behaviorally relevant events.

Capture of fixation by rotational flow; a deterministic hypothesis regarding scaling and stochasticity in fixational eye movements

Visual scan paths exhibit complex, stochastic dynamics. Even during visual fixation, the eye is in constant motion. Fixational drift and tremor are thought to reflect fluctuations in the persistent neural activity of neural integrators in the oculomotor brainstem, which integrate sequences of transient saccadic velocity signals into a short term memory of eye position. Despite intensive research and much progress, the precise mechanisms by which oculomotor posture is maintained remain elusive. Drift exhibits a stochastic statistical profile which has been modeled using random walk formalisms. Tremor is widely dismissed as noise. Here we focus on the dynamical profile of fixational tremor, and argue that tremor may be a signal which usefully reflects the workings of oculomotor postural control. We identify signatures reminiscent of a certain flavor of transient neurodynamics; toric traveling waves which rotate around a central phase singularity. Spiral waves play an organizational role in dynamical systems at many scales throughout nature, though their potential functional role in brain activity remains a matter of educated speculation. Spiral waves have a repertoire of functionally interesting dynamical properties, including persistence, which suggest that they could in theory contribute to persistent neural activity in the oculomotor postural control system. Whilst speculative, the singularity hypothesis of oculomotor postural control implies testable predictions, and could provide the beginnings of an integrated dynamical framework for eye movements across scales.

Endocannabinoid modulation of cortical up-states and NREM sleep

Up-/down-state transitions are a form of network activity observed when sensory input into the cortex is diminished such as during non-REM sleep. Up-states emerge from coordinated signaling between glutamatergic and GABAergic synapses and are modulated by systems that affect the balance between inhibition and excitation. We hypothesized that the endocannabinoid (EC) system, a neuromodulatory system intrinsic to the cortical microcircuitry, is an important regulator of up-states and sleep. To test this hypothesis, up-states were recorded from layer V/VI pyramidal neurons in organotypic cultures of wild-type or CB1R knockout (KO) mouse prefrontal cortex. Activation of the cannabinoid 1 receptor (CB1) with exogenous agonists or by blocking metabolism of endocannabinoids, anandamide or 2-arachidonoyl glycerol, increased up-state amplitude and facilitated action potential discharge during up-states. The CB1 agonist also produced a layer II/III-selective reduction in synaptic GABAergic signaling that may underlie its effects on up-state amplitude and spiking. Application of CB1 antagonists revealed that an endogenous EC tone regulates up-state duration. Paradoxically, the duration of up-states in CB1 KO cultures was increased suggesting that chronic absence of EC signaling alters cortical activity. Consistent with increased cortical excitability, CB1 KO mice exhibited increased wakefulness as a result of reduced NREM sleep and NREM bout duration. Under baseline conditions, NREM delta (0.5–4 Hz) power was not different in CB1 KO mice, but during recovery from forced sleep deprivation, KO mice had reduced NREM delta power and increased sleep fragmentation. Overall, these findings demonstrate that the EC system actively regulates cortical up-states and important features of NREM sleep such as its duration and low frequency cortical oscillations.

Neural correlates of interval timing in rodent prefrontal cortex

Time interval estimation is involved in numerous behavioral processes, but its underlying neural mechanisms remain unclear. In particular, it has been controversial whether time is encoded on a linear or logarithmic scale. Based on our previous finding that inactivation of the medial prefrontal cortex (mPFC) profoundly impairs rat's ability to discriminate time intervals, we investigated how the mPFC processes temporal information by examining activity of mPFC neurons in rats performing a temporal bisection task. Many mPFC neurons conveyed temporal information based on monotonically changing activity profiles over time with negative accelerations, so that their activity profiles were better described by logarithmic than linear functions. Moreover, the precision of time-interval discrimination based on neural activity was lowered in proportion to the elapse of time, but without proportional increase in neural variability, which is well accounted for by logarithmic, but not by linear functions. As a population, mPFC neurons conveyed precise information about the elapse of time with their activity tightly correlated with the animal's choice of target. These results suggest that the mPFC might be part of an internal clock in charge of controlling interval-timing behavior, and that linearly changing neuronal activity on a logarithmic time scale might be one way of representing the elapse of time in the brain.

Trading speed and accuracy by coding time: a coupled-circuit cortical model

Our actions take place in space and time, but despite the role of time in decision theory and the growing acknowledgement that the encoding of time is crucial to behaviour, few studies have considered the interactions between neural codes for objects in space and for elapsed time during perceptual decisions. The speed-accuracy trade-off (SAT) provides a window into spatiotemporal interactions. Our hypothesis is that temporal coding determines the rate at which spatial evidence is integrated, controlling the SAT by gain modulation. Here, we propose that local cortical circuits are inherently suited to the relevant spatial and temporal coding. In simulations of an interval estimation task, we use a generic local-circuit model to encode time by ‘climbing’ activity, seen in cortex during tasks with a timing requirement. The model is a network of simulated pyramidal cells and inhibitory interneurons, connected by conductance synapses. A simple learning rule enables the network to quickly produce new interval estimates, which show signature characteristics of estimates by experimental subjects. Analysis of network dynamics formally characterizes this generic, local-circuit timing mechanism. In simulations of a perceptual decision task, we couple two such networks. Network function is determined only by spatial selectivity and NMDA receptor conductance strength; all other parameters are identical. To trade speed and accuracy, the timing network simply learns longer or shorter intervals, driving the rate of downstream decision processing by spatially non-selective input, an established form of gain modulation. Like the timing network's interval estimates, decision times show signature characteristics of those by experimental subjects. Overall, we propose, demonstrate and analyse a generic mechanism for timing, a generic mechanism for modulation of decision processing by temporal codes, and we make predictions for experimental verification.

Studies in neuroscience have characterized how the brain represents objects in space and how these objects are selected for detailed perceptual processing. The selection process entails a decision about which object is favoured by the available evidence over time. This period of time is typically in the range of hundreds of milliseconds and is widely believed to be crucial for decisions, allowing neurons to filter noise in the evidence. Despite the widespread belief that time plays this role in decisions and the growing recognition that the brain estimates elapsed time during perceptual tasks, few studies have considered how the encoding of time effects decision making. We propose that neurons encode time in this range by the same general mechanisms used to select objects for detailed processing, and that these temporal representations determine how long evidence is filtered. To this end, we simulate a perceptual decision by coupling two instances of a neural network widely used to simulate localized regions of the cerebral cortex. One network encodes the passage of time and the other makes decisions based on noisy evidence. The former influences the performance of the latter, reproducing signature characteristics of temporal estimates and perceptual decisions.

Emergence in the central nervous system

"Emergence" is an idea that has received much attention in consciousness literature, but it is difficult to find characterizations of that concept which are both specific and useful. I will precisely define and characterize a type of epistemic ("weak") emergence and show that it is a property of some neural circuits throughout the CNS, on micro-, meso- and macroscopic levels. I will argue that possession of this property can result in profoundly altered neural dynamics on multiple levels in cortex and other systems. I will first describe emergent neural entities (ENEs) abstractly. I will then show how ENEs function specifically and concretely, and demonstrate some implications of this type of emergence for the CNS.

Neural correlates and neural computations in posterior parietal cortex during perceptual decision-making

A recent line of work has found remarkable success in relating perceptual decision-making and the spiking activity in the macaque lateral intraparietal area (LIP). In this review, we focus on questions about the neural computations in LIP that are not answered by demonstrations of neural correlates of psychological processes. We highlight three areas of limitations in our current understanding of the precise neural computations that might underlie neural correlates of decisions: (1) empirical questions not yet answered by existing data; (2) implementation issues related to how neural circuits could actually implement the mechanisms suggested by both extracellular neurophysiology and psychophysics; and (3) ecological constraints related to the use of well-controlled laboratory tasks and whether they provide an accurate window on sensorimotor computation. These issues motivate the adoption of a more general "encoding-decoding framework" that will be fruitful for more detailed contemplation of how neural computations in LIP relate to the formation of perceptual decisions.

Stress-induced impairment of a working memory task: role of spiking rate and spiking history predicted discharge

Stress, pervasive in society, contributes to over half of all work place accidents a year and over time can contribute to a variety of psychiatric disorders including depression, schizophrenia, and post-traumatic stress disorder. Stress impairs higher cognitive processes, dependent on the prefrontal cortex (PFC) and that involve maintenance and integration of information over extended periods, including working memory and attention. Substantial evidence has demonstrated a relationship between patterns of PFC neuron spiking activity (action-potential discharge) and components of delayed-response tasks used to probe PFC-dependent cognitive function in rats and monkeys. During delay periods of these tasks, persistent spiking activity is posited to be essential for the maintenance of information for working memory and attention. However, the degree to which stress-induced impairment in PFC-dependent cognition involves changes in task-related spiking rates or the ability for PFC neurons to retain information over time remains unknown. In the current study, spiking activity was recorded from the medial PFC of rats performing a delayed-response task of working memory during acute noise stress (93 db). Spike history-predicted discharge (SHPD) for PFC neurons was quantified as a measure of the degree to which ongoing neuronal discharge can be predicted by past spiking activity and reflects the degree to which past information is retained by these neurons over time. We found that PFC neuron discharge is predicted by their past spiking patterns for nearly one second. Acute stress impaired SHPD, selectively during delay intervals of the task, and simultaneously impaired task performance. Despite the reduction in delay-related SHPD, stress increased delay-related spiking rates. These findings suggest that neural codes utilizing SHPD within PFC networks likely reflects an additional important neurophysiological mechanism for maintenance of past information over time. Stress-related impairment of this mechanism is posited to contribute to the cognition-impairing actions of stress.

Long-lasting small-amplitude TRP-mediated dendritic depolarizations in CA1 pyramidal neurons are intrinsically stable and originate from distal tuft regions

In several regions of the nervous system, neurons display bi- or multistable intrinsic properties. Such stable states may be subthreshold and long-lasting, and can appear as a sustained afterdepolarization. In hippocampal CA1 pyramidal neurons, small-amplitude (1 mV) long-lasting (seconds) afterdepolarizations have been reported and are thought to depend on calcium-activated nonselective (CAN) currents recently identified as transient receptor potential (TRP) channels. Continuing our previous experimental and computational work on synaptically metabotropic glutamate receptor (mGluR)-activated TRP currents, we here explore small-amplitude long-lasting depolarizations in a detailed multicompartmental model of a CA1 pyramidal neuron. We confirm a previous hypothesis suggesting that the depolarization results from an interplay of TRP and voltage-gated calcium channels, and contribute to the understanding of the depolarization in several ways. Specifically, we show that: (i) the long-lasting depolarization may be intrinsically stable to weak excitatory and inhibitory input, (ii) the phenomenon is essentially located in distal apical dendrites, (iii) induction is facilitated if simultaneous input arrives at several dendritic branches, and if calcium- and/or mGluR-evoked signals undergo summation, suggesting that both spatial and temporal synaptic summation might be required for the depolarization to occur and (iv) we also show that the integration of inputs to oblique dendrites is strongly modulated by the presence of small-amplitude long-lasting depolarizations in distal tuft dendrites. To conclude, we suggest that small-amplitude long-lasting dendritic depolarizations may contribute to sustaining neural information during behavioural tasks in cases where information is separated in time, as in trace conditioning and delay tasks.

Plasticity in D1-like receptor expression is associated with different components of cognitive processes

Dopamine D1-like receptors consist of D1 (D1A) and D5 (D1B) receptors and play a key role in working memory. However, their possibly differential contribution to working memory is unclear. We combined a working memory training protocol with a stepwise increase of cognitive subcomponents and real-time RT-PCR analysis of dopamine receptor expression in pigeons to identify molecular changes that accompany training of isolated cognitive subfunctions. In birds, the D1-like receptor family is extended and consists of the D1A, D1B, and D1D receptors. Our data show that D1B receptor plasticity follows a training that includes active mental maintenance of information, whereas D1A and D1D receptor plasticity in addition accompanies learning of stimulus-response associations. Plasticity of D1-like receptors plays no role for processes like response selection and stimulus discrimination. None of the tasks altered D2 receptor expression. Our study shows that different cognitive components of working memory training have distinguishable effects on D1-like receptor expression.

Variability of spike firing during θ-coupled replay of memories in a simulated attractor network

Simulation work has recently shown that attractor networks can reproduce Poisson-like variability of single cell spiking, with coefficient of variation (Cv(2)) around unity, consistent with cortical data. However, the use of local variability (Lv) measures has revealed area- and layer-specific deviations from Poisson-like firing. In order to test these findings in silico we used a biophysically detailed attractor network model. We show that Lv well above 1, specifically found in superficial cortical layers and prefrontal areas, can indeed be reproduced in such networks and is consistent with periodic replay rather than persistent firing. The memory replay at the theta time scale provides a framework for a multi-item memory storage in the model. This article is part of a Special Issue entitled Neural Coding.

Six types of multistability in a neuronal model based on slow calcium current

BACKGROUND

Multistability of oscillatory and silent regimes is a ubiquitous phenomenon exhibited by excitable systems such as neurons and cardiac cells. Multistability can play functional roles in short-term memory and maintaining posture. It seems to pose an evolutionary advantage for neurons which are part of multifunctional Central Pattern Generators to possess multistability. The mechanisms supporting multistability of bursting regimes are not well understood or classified.

METHODOLOGY/PRINCIPAL FINDINGS

Our study is focused on determining the bio-physical mechanisms underlying different types of co-existence of the oscillatory and silent regimes observed in a neuronal model. We develop a low-dimensional model typifying the dynamics of a single leech heart interneuron. We carry out a bifurcation analysis of the model and show that it possesses six different types of multistability of dynamical regimes. These types are the co-existence of 1) bursting and silence, 2) tonic spiking and silence, 3) tonic spiking and subthreshold oscillations, 4) bursting and subthreshold oscillations, 5) bursting, subthreshold oscillations and silence, and 6) bursting and tonic spiking. These first five types of multistability occur due to the presence of a separating regime that is either a saddle periodic orbit or a saddle equilibrium. We found that the parameter range wherein multistability is observed is limited by the parameter values at which the separating regimes emerge and terminate.

CONCLUSIONS

We developed a neuronal model which exhibits a rich variety of different types of multistability. We described a novel mechanism supporting the bistability of bursting and silence. This neuronal model provides a unique opportunity to study the dynamics of networks with neurons possessing different types of multistability.

The decade of the dendritic NMDA spike

In the field of cortical cellular physiology, much effort has been invested in understanding thick apical dendrites of pyramidal neurons and the regenerative sodium and calcium spikes that take place in the apical trunk. Here we focus on thin dendrites of pyramidal cells (basal, oblique, and tuft dendrites), and we discuss one relatively novel form of an electrical signal ("NMDA spike") that is specific for these branches. Basal, oblique, and apical tuft dendrites receive a high density of glutamatergic synaptic contacts. Synchronous activation of 10-50 neighboring glutamatergic synapses triggers a local dendritic regenerative potential, NMDA spike/plateau, which is characterized by significant local amplitude (40-50 mV) and an extraordinary duration (up to several hundred milliseconds). The NMDA plateau potential, when it is initiated in an apical tuft dendrite, is able to maintain a good portion of that tuft in a sustained depolarized state. However, if NMDA-dominated plateau potentials originate in proximal segments of basal dendrites, they regularly bring the neuronal cell body into a sustained depolarized state, which resembles a cortical Up state. At each dendritic initiation site (basal, oblique, and tuft) an NMDA spike creates favorable conditions for causal interactions of active synaptic inputs, including the spatial or temporal binding of information, as well as processes of short-term and long-term synaptic modifications (e.g., long-term potentiation or long-term depression). Because of their strong amplitudes and durations, local dendritic NMDA spikes make up the cellular substrate for multisite independent subunit computations that enrich the computational power and repertoire of cortical pyramidal cells. We propose that NMDA spikes are likely to play significant roles in cortical information processing in awake animals (spatiotemporal binding, working memory) and during slow-wave sleep (neuronal Up states, consolidation of memories).

Persistent storage capability impairs decision making in a biophysical network model

Two long-standing questions in neuroscience concern the mechanisms underlying our abilities to make decisions and to store goal-relevant information in memory for seconds at a time. Recent experimental and theoretical advances suggest that NMDA receptors at intrinsic cortical synapses play an important role in both these functions. The long NMDA time constant is suggested to support persistent mnemonic activity by maintaining excitatory drive after the removal of a stimulus and to enable the slow integration of afferent information in the service of decisions. These findings have led to the hypothesis that the local circuit mechanisms underlying decisions must also furnish persistent storage of information. We use a local circuit cortical model of spiking neurons to test this hypothesis, controlling intrinsic drive by scaling NMDA conductance strength. Our simulations provide further evidence that persistent storage and decision making are supported by common mechanisms, but under biophysically realistic parameters, our model demonstrates that the processing requirements of persistent storage and decision making may be incompatible at the local circuit level. Parameters supporting persistent storage lead to strong dynamics that are at odds with slow integration, whereas weaker dynamics furnish the speed-accuracy trade-off common to psychometric data and decision theory.

The neuronal replicator hypothesis

We propose that replication (with mutation) of patterns of neuronal activity can occur within the brain using known neurophysiological processes. Thereby evolutionary algorithms implemented by neuro- nal circuits can play a role in cognition. Replication of structured neuronal representations is assumed in several cognitive architectures. Replicators overcome some limitations of selectionist models of neuronal search. Hebbian learning is combined with replication to structure exploration on the basis of associations learned in the past. Neuromodulatory gating of sets of bistable neurons allows patterns of activation to be copied with mutation. If the probability of copying a set is related to the utility of that set, then an evolutionary algorithm can be implemented at rapid timescales in the brain. Populations of neuronal replicators can undertake a more rapid and stable search than can be achieved by serial modification of a single solution. Hebbian learning added to neuronal replication allows a powerful structuring of variability capable of learning the location of a global optimum from multiple previously visited local optima. Replication of solutions can solve the problem of catastrophic forgetting in the stability-plasticity dilemma. In short, neuronal replication is essential to explain several features of flexible cognition. Predictions are made for the experimental validation of the neuronal replicator hypothesis.

Beneficial effects of the NMDA antagonist ketamine on decision processes in visual search

The ability of sensory-motor circuits to integrate sensory evidence over time is thought to underlie the process of decision-making in perceptual discrimination. Recent work has suggested that the NMDA receptor contributes to mediating neural activity integration. To test this hypothesis, we trained three female rhesus monkeys (Macaca mulatta) to perform a visual search task, in which they had to make a saccadic eye movement to the location of a target stimulus presented among distracter stimuli of lower luminance. We manipulated NMDA-receptor function by administering an intramuscular injection of the noncompetitive NMDA antagonist ketamine and assessed visual search performance before and after manipulation. Ketamine was found to lengthen response latency in a dose-dependent fashion. Surprisingly, it was also observed that response accuracy was significantly improved when lower doses were administered. These findings suggest that NMDA receptors play a crucial role in the process of decision-making in perceptual discrimination. They also further support the idea that multiple neural representations compete with one another through mutual inhibition, which may explain the speed-accuracy trade-off rule that shapes discrimination behavior: lengthening integration time helps resolve small differences between choice alternatives, thereby improving accuracy.

The free-energy principle: a unified brain theory?

A free-energy principle has been proposed recently that accounts for action, perception and learning. This Review looks at some key brain theories in the biological (for example, neural Darwinism) and physical (for example, information theory and optimal control theory) sciences from the free-energy perspective. Crucially, one key theme runs through each of these theories - optimization. Furthermore, if we look closely at what is optimized, the same quantity keeps emerging, namely value (expected reward, expected utility) or its complement, surprise (prediction error, expected cost). This is the quantity that is optimized under the free-energy principle, which suggests that several global brain theories might be unified within a free-energy framework.

Effects of ethanol on persistent activity and up-States in excitatory and inhibitory neurons in prefrontal cortex

BACKGROUND

Elucidating mechanisms that underlie the neural actions of ethanol is critical for understanding how this drug affects behavior. Increasing evidence suggests that, in addition to mid-brain dopaminergic regions, higher cortical structures play an important role in the pathophysiology associated with alcohol abuse. Previous studies from this laboratory used a novel slice co-culture system to demonstrate that ethanol reduces network-dependent patterns of activity in excitatory pyramidal neurons of the prefrontal cortex (PFC). In the present study, we examine the effect of ethanol on the activity of fast-spiking (FS) interneurons, a sub-population of neurons critically involved in shaping cortical activity.

METHODS

Slices containing the dorsolateral PFC were prepared from neonatal C57 mice and maintained in culture. After 2 to 3 weeks in vitro, whole-cell patch-clamp electrophysiology was used to monitor spontaneous episodes of persistent activity in prelimbic PFC neurons. In some experiments, the use-dependent NMDA receptor blocker, MK801, was included in the pipette recording solution to assess the contribution of NMDA receptors to up-states.

RESULTS

MK801 reduced up-state amplitude and revealed underlying fast EPSPs in excitatory pyramidal neurons while having little effect on these parameters in FS interneurons. Despite this difference, ethanol (44 mM), significantly reduced up-state duration and up-state area in both pyramidal and FS interneurons.

CONCLUSIONS

These results suggest that ethanol reduces the activity of FS interneurons due to disruption of network-dependent activity. This would be expected to further impair the ability of PFC networks to carry out their normal function and may contribute to the adverse effects of ethanol on PFC-dependent behaviors.

Implications of synaptic biophysics for recurrent network dynamics and active memory

In cortical networks, synaptic excitation is mediated by AMPA- and NMDA-type receptors. NMDA differ from AMPA synaptic potentials with regard to peak current, time course, and a strong voltage-dependent nonlinearity. Here we illustrate based on empirical and computational findings that these specific biophysical properties may have profound implications for the dynamics of cortical networks, and via dynamics on cognitive functions like active memory. The discussion will be led along a minimal set of neural equations introduced to capture the essential dynamics of the various phenomena described. NMDA currents could establish cortical bistability and may provide the relatively constant synaptic drive needed to robustly maintain enhanced levels of activity during working memory epochs, freeing fast AMPA currents for other computational purposes. Perhaps more importantly, variations in NMDA synaptic input-due to their biophysical particularities-control the dynamical regime within which single neurons and networks reside. By provoking bursting, chaotic irregularity, and coherent oscillations their major effect may be on the temporal pattern of spiking activity, rather than on average firing rate. During active memory, neurons may thus be pushed into a spiking regime that harbors complex temporal structure, potentially optimal for the encoding and processing of temporal sequence information. These observations provide a qualitatively different view on the role of synaptic excitation in neocortical dynamics than entailed by many more abstract models. In this sense, this article is a plead for taking the specific biophysics of real neurons and synapses seriously when trying to account for the neurobiology of cognition.

The first second: models of short-term memory traces in the brain

Many network models in computational neuroscience rise to the challenge of explaining behavioural phenomena ranging from microseconds to tens of seconds using components operating mostly on a time-scale of milliseconds. These models have in common that the underlying system has a memory, which implies that its output depends on its past input history. In this review we compare how such memory traces or delayed responses may be implemented in different brain areas supporting a diversity of functions.