Recurrent network models for perfect temporal integration of fluctuating correlated inputs

Future thinking to decrease real-world drinking in alcohol use disorder: Repairing reinforcer pathology in a randomized proof-of-concept trial

Reinforcer Pathology theory proposes that expanding the temporal window of reinforcement (i.e., reducing delay discounting) using episodic future thinking (EFT) would decrease alcohol consumption. However, evidence of effectiveness in real-world settings is lacking. Using a randomized proof-of-concept field trial, the current study examined the effect of expanding the temporal window of reinforcement, using remotely delivered EFT, on decreasing real-world alcohol consumption among individuals with alcohol use disorder (AUD). Fifty-two individuals (9 females) aged 18-65 years who met the DSM-5 criteria for moderate or severe AUD and aimed to drink in moderation or abstain from drinking completed the study and were included in analysis. EFT significantly (p = .031) reduced alcohol consumption (mean change of consumption pre-post intervention = -2.18 drinks/day) compared to control episodic recent thinking (ERT; mean change of -0.52 drinks/day). Changes in discounting rates pre-post intervention significantly predicted changes in alcohol consumption (coef. = .424, 95% CI [.043-.813], p = .030) even after controlling for age, gender, race, income, education, marital status, and family history of addiction. Overall satisfaction across groups was rated as 3.92 on a 1 to 5-point scale, suggesting that the current remote approach is feasible and acceptable. The current findings were congruent with the theory, Reinforcer Pathology, that EFT expands the temporal window and decreases alcohol consumption, and the remote approach was considered feasible and acceptable. We believe the present study contributes new knowledge with tangible benefits for scientifically understanding and better defining novel interventions that may be clinically deployed to improve treatment outcomes. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

What Is Addiction? How Can Animal and Human Research Be Used to Advance Research, Diagnosis, and Treatment of Alcohol and Other Substance Use Disorders?

The current article highlights key issues in defining, studying, and treating addiction, a concept related to but distinct from substance use disorders. The discussion is based upon a roundtable discussion at the 2017 annual meeting of the Research Society on Alcoholism where Warren K. Bickel and John C. Crabbe were charged with answering a range of questions posed by Kenneth J. Sher. All the presenters highlighted a number of central concerns for those interested in assessing and treating addiction as well as those seeking to conduct basic preclinical research that is amenable to meaningful translation to the human condition. In addition, the discussion illustrated both the power and limitations of using any single theory to explain multiple phenomena subsumed under the rubric of addiction. Among the major issues examined were the important differences between traditional diagnostic approaches and current concepts of addiction, the difficulty of modeling key aspects of human addiction in nonhuman animals, key aspects of addiction that have, to date, received little empirical attention, and the importance of thinking of recovery as a phenomenon that possibly involves processes distinct from those undergirding the development and maintenance of addiction.

Subcellular Localization and Activity of TRPM4 in Medial Prefrontal Cortex Layer 2/3

TRPM4 is a Ca²⁺-activated non-selective cationic channel that conducts monovalent cations. TRPM4 has been proposed to contribute to burst firing and sustained activity in several brain regions, however, the cellular and subcellular pattern of TRPM4 expression in medial prefrontal cortex (mPFC) during postnatal development has not been elucidated. Here, we use multiplex immunofluorescence labeling of brain sections to characterize the postnatal developmental expression of TRPM4 in the mouse mPFC. We also performed electrophysiological recordings to correlate the expression of TRPM4 immunoreactivity with the presence of TRPM4-like currents. We found that TRPM4 is expressed from the first postnatal day, with expression increasing up to postnatal day 35. Additionally, in perforated patch clamp experiments, we found that TRPM4-like currents were active at resting membrane potentials at all postnatal ages studied. Moreover, TRPM4 is expressed in both pyramidal neurons and interneurons. TRPM4 expression is localized in the soma and proximal dendrites, but not in the axon initial segment of pyramidal neurons. This subcellular localization is consistent with a reduction in the basal current only when we locally perfused 9-Phenanthrol in the soma, but not upon perfusion in the medial or distal dendrites. Our results show a specific localization of TRPM4 expression in neurons in the mPFC and that a 9-Phenanthrol sensitive current is active at resting membrane potential, suggesting specific functional roles in mPFC neurons during postnatal development and in adulthood.

Impact of membrane bistability on dynamical response of neuronal populations

Neurons in many brain areas can develop a pronounced depolarized state of membrane potential (up state) in addition to the normal hyperpolarized state near the resting potential. The influence of the up state on signal encoding, however, is not well investigated. Here we construct a one-dimensional bistable neuron model and calculate the linear dynamical response to noisy oscillatory inputs analytically. We find that with the appearance of an up state, the transmission function is enhanced by the emergence of a local maximum at some optimal frequency and the phase lag relative to the input signal is reduced. We characterize the dependence of the enhancement of frequency response on intrinsic dynamics and on the occupancy of the up state.

Dense neuron clustering explains connectivity statistics in cortical microcircuits

Local cortical circuits appear highly non-random, but the underlying connectivity rule remains elusive. Here, we analyze experimental data observed in layer 5 of rat neocortex and suggest a model for connectivity from which emerge essential observed non-random features of both wiring and weighting. These features include lognormal distributions of synaptic connection strength, anatomical clustering, and strong correlations between clustering and connection strength. Our model predicts that cortical microcircuits contain large groups of densely connected neurons which we call clusters. We show that such a cluster contains about one fifth of all excitatory neurons of a circuit which are very densely connected with stronger than average synapses. We demonstrate that such clustering plays an important role in the network dynamics, namely, it creates bistable neural spiking in small cortical circuits. Furthermore, introducing local clustering in large-scale networks leads to the emergence of various patterns of persistent local activity in an ongoing network activity. Thus, our results may bridge a gap between anatomical structure and persistent activity observed during working memory and other cognitive processes.

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.

Neural dynamics and circuit mechanisms of decision-making

In this review, I briefly summarize current neurobiological studies of decision-making that bear on two general themes. The first focuses on the nature of neural representation and dynamics in a decision circuit. Experimental and computational results suggest that ramping-to-threshold in the temporal domain and trajectory of population activity in the state space represent a duality of perspectives on a decision process. Moreover, a decision circuit can display several different dynamical regimes, such as the ramping mode and the jumping mode with distinct defining properties. The second is concerned with the relationship between biologically-based mechanistic models and normative-type models. A fruitful interplay between experiments and these models at different levels of abstraction have enabled investigators to pose increasingly refined questions and gain new insights into the neural basis of decision-making. In particular, recent work on multi-alternative decisions suggests that deviations from rational models of choice behavior can be explained by established neural mechanisms.

Auditory signal detection appears to depend on temporal integration of subthreshold activity in auditory cortex

The threshold of hearing decreases with increasing sound duration up to a limit of a few hundred milliseconds, whereas other auditory time constants are orders of magnitude shorter. A possible solution to this resolution-integration paradox is that temporal integration occurs more centrally than computations depending on high temporal resolution. But this would require information about subthreshold events in the periphery to reach higher centers. Here we show that this prerequisite is fulfilled. The auditory evoked response to a just perceptible pulse series does basically not depend on whether single pulses are below or above behavioral threshold. The failure to find evidence of temporal integration up to response latencies of 30 ms suggests that the integrator is located more centrally than primary auditory cortex. By using noise to its advantage, the auditory system apparently has established a central integration mechanism that is about as efficient as the peripheral one in the visual system.

Microcircuitry coordination of cortical motor information in self-initiation of voluntary movements

Motor cortex neurons are activated at different times during self-initiated voluntary movement. However, the manner in which excitatory and inhibitory neurons in distinct cortical layers help to organize voluntary movement is poorly understood. We carried out juxtacellular and multiunit recordings from actively behaving rats and found temporally and functionally distinct activations of excitatory pyramidal cells and inhibitory fast-spiking interneurons. Across cortical layers, pyramidal cells were activated diversely for sequential motor phases (for example, preparation, initiation and execution). In contrast, fast-spiking interneurons, including parvalbumin-positive basket cells, were recruited predominantly for motor execution, with pyramidal cells producing a command-like activity. Thus, fast-spiking interneurons may underlie command shaping by balanced inhibition or recurrent inhibition, rather than command gating by temporally alternating excitation and inhibition. Furthermore, initiation-associated pyramidal cells excited similar and different functional classes of neurons through putative monosynaptic connections. This suggests that these cells may temporally integrate information to initiate and coordinate voluntary movement.