Mean-field method for generic conductance-based integrate-and-fire neurons with finite timescales.

The construction of transfer functions in theoretical neuroscience plays an important role in determining the spiking rate behavior of neurons in networks. These functions can be obtained through various fitting methods, but the biological relevance of the parameters is not always clear. However, for stationary inputs, such functions can be obtained without the adjustment of free parameters by using mean-field methods. In this work, we expand current Fokker-Planck approaches to account for the concurrent influence of colored and multiplicative noise terms on generic conductance-based integrate-and-fire neurons. We reduce the resulting stochastic system through the application of the diffusion approximation to a one-dimensional Langevin equation. An effective Fokker-Planck is then constructed using Fox Theory, which is solved numerically using a newly developed double integration procedure to obtain the transfer function and the membrane potential distribution. The solution is capable of reproducing the transfer function and the stationary voltage distribution of simulated neurons across a wide range of parameters. The method can also be easily extended to account for different sources of noise with various multiplicative terms, and it can be used in other types of problems in principle.

Mechanistic role of alpha oscillations in a computational model of working memory.

Brain oscillations are believed to be involved in the different operations necessary to manipulate information during working memory tasks. We propose a mechanistic role for the observed inhibition effect of the alpha rhythm based on its interference with the theta rhythm. Using the Lisman-Idiart model for multi-item working memory, we show that the interaction between these two oscillations is capable of creating a long lasting destructive interference that prevents the cyclic reactivation of neuronal ensembles and, as a consequence, memory maintenance. Additionally, to ensure robustness we propose a modular version of the model and implement oscillations as traveling waves. Using this model, we show that the interactions between theta and gamma determine the allocation of multiple memories in distinct modules, while the interference between theta and alpha disrupts the maintenance of the information already stored in them. The effect of alpha in erasing or blocking storage is robust and seems fairly independent of frequency, as long as it stays within the alpha range. This model helps us to understand why the alpha and theta oscillations, which have close frequency bands, could have opposite roles in working memory.

Effective recommendations towards healthy routines to preserve mental health during the COVID-19 pandemic

Objective: To assess the adherence to a set of evidence-based recommendations to support mental health during the coronavirus disease 2019 (COVID-19) pandemic and its association with depressive and anxiety symptoms. Methods: A team of health workers and researchers prepared the recommendations, formatted into three volumes (1: COVID-19 prevention; 2: Healthy habits; 3: Biological clock and sleep). Participants were randomized to receive only Volume 1 (control), Volumes 1 and 2, Volumes 1 and 3, or all volumes. We used a convenience sample of Portuguese-speaking participants over age 18 years. An online survey consisting of sociodemographic and behavioral questionnaires and mental health instruments (Patient Health Questionnaire-9 [PHQ-9] and Generalized Anxiety Disorder-7 [GAD-7]) was administered. At 14 and 28 days later, participants were invited to complete follow-up surveys, which also included questions regarding adherence to the recommendations. A total of 409 participants completed the study - mostly young adult women holding university degrees. Results: The set of recommendations contained in Volumes 2 and 3 was effective in protecting mental health, as suggested by significant associations of adherence with PHQ-9 and GAD-7 scores (reflecting anxiety and depression symptoms, respectively). Conclusion: The recommendations developed in this study could be useful to prevent negative mental health effects in the context of the pandemic and beyond.

Effective recommendations towards healthy routines to preserve mental health during the COVID-19 pandemic.

OBJECTIVE: To assess the adherence to a set of evidence-based recommendations to support mental health during the coronavirus disease 2019 (COVID-19) pandemic and its association with depressive and anxiety symptoms. METHODS: A team of health workers and researchers prepared the recommendations, formatted into three volumes (1: COVID-19 prevention; 2: Healthy habits; 3: Biological clock and sleep). Participants were randomized to receive only Volume 1 (control), Volumes 1 and 2, Volumes 1 and 3, or all volumes. We used a convenience sample of Portuguese-speaking participants over age 18 years. An online survey consisting of sociodemographic and behavioral questionnaires and mental health instruments (Patient Health Questionnaire-9 [PHQ-9] and Generalized Anxiety Disorder-7 [GAD-7]) was administered. At 14 and 28 days later, participants were invited to complete follow-up surveys, which also included questions regarding adherence to the recommendations. A total of 409 participants completed the study - mostly young adult women holding university degrees. RESULTS: The set of recommendations contained in Volumes 2 and 3 was effective in protecting mental health, as suggested by significant associations of adherence with PHQ-9 and GAD-7 scores (reflecting anxiety and depression symptoms, respectively). CONCLUSION: The recommendations developed in this study could be useful to prevent negative mental health effects in the context of the pandemic and beyond.

Handling missing data in rest-activity time series measured by actimetry.

A handling procedure of off-wrist episodes in actimetry time series of motor activity is presented using two records (regular vs. irregular sleep-wake cycle and daytime activity) of 14 consecutive days sampled in 1-minute epochs. We generated single missing value (NA) intervals of 1 h, 2 h, 4 h, 6 h, 12 h, and 24 h as well as random NA episodes following probabilistic rules to simulate real-life off-wrist episodes. Then, we replaced these episodes with "zeroes" (i.e., the default of immobility records), mean or median of the remaining 13 days corresponding to the missing bins. Single missing episodes of up to 12 h resulted in less than 5% variation from the original values. The irregular series showed higher variability in acrophase, MESOR, L5, M10 and RA compared to the regular series. Random missing allocation simulating real-life off-wrist episodes resulted in significant changes in most parameters, and the imputation of zeroes significantly increased the variance; however, replacing NA with mean or median resulted in patterns similar to those of NA. We recommend replacing 'zeroes' with NA whenever possible, given the risk of inflating invariance using zeroes. If the parameters cannot be computed in the presence of NA, we recommend using the weekly mean of corresponding timepoints.

Effects of lighting patterns in pubertal development and metabolism of female wistar rats.

Modern lifestyle is characterized by constant exposure to artificial light, which is associated with alterations in biological rhythms, abnormalities to reproductive cycles and metabolic changes. In this study, we aimed to evaluate the effects of four different lighting patterns on puberty timing and on possible metabolic changes in female Wistar rats. Additionally, we developed a machine learning algorithm to automatically classify the stages of the estrous cycle. Adult Wistar rats mated during a week at a photoperiod station where they were exposed to combined red-green-blue lights (RGB) during the photoperiod that varied its spectral composition (i.e., variable color temperature) during the day (RGB-v; N = 14), RGB during the photoperiod with a fixed light color temperature (RGB-f; N = 13) during the whole photoperiod; constant darkness (DD; N = 13) and constant fixed light (LL; N = 15). Experiments were performed only on female litters from postnatal day (PND) 22 to 50. Body weight, puberty onset, estrous cyclicity and serum metabolic parameters were measured. We also collected pictures of vaginal smears to create a dataset of 15,936 images to construct an automatic classifier based on convolutional neural networks. No significant differences were found in the age of vaginal opening; however, the RGB-v group showed a significantly lower number of complete and consecutives cycles. Also, the RGB-f group showed the first complete estrous cycle significantly earlier than the RGB-v group. Female rats housed in LL condition presented significantly lower mean body weight from PND 33 to PDN 47 compared to the other groups. Furthermore, higher levels of plasma triglycerides were found in the DD group compared to RGB-f and RGB-v. HDL levels were significantly lower in RGB-v compared to RGB-f and LL groups. Total cholesterol was significantly lower in RGB-v compared to all groups. Visceral fat was significantly higher in RGB-f compared to the LL group. These results suggest that both changes in photoperiod and lighting quality affect pubertal development and alter lipid profiles and visceral fat accumulation.

The maturational characteristics of the GABA input in the anterior piriform cortex may also contribute to the rapid learning of the maternal odor during the sensitive period.

During the first ten postnatal days (P), infant rodents can learn olfactory preferences for novel odors if they are paired with thermo-tactile stimuli that mimic components of maternal care. After P10, the thermo-tactile pairing becomes ineffective for conditioning. The current explanation for this change in associative learning is the alteration in the norepinephrine (NE) inputs from the locus coeruleus (LC) to the olfactory bulb (OB) and the anterior piriform cortex (aPC). By combining patch-clamp electrophysiology and computational simulations, we showed in a recent work that a transitory high responsiveness of the OB-aPC circuit to the maternal odor is an alternative mechanism that could also explain early olfactory preference learning and its cessation after P10. That result relied solely on the maturational properties of the aPC pyramidal cells. However, the GABAergic system undergoes important changes during the same period. To address the importance of the maturation of the GABAergic system for early olfactory learning, we incorporated data from the GABA inputs, obtained from in vitro patch-clamp experiment in the aPC of rat pups aged P5-P7 reported here, to the model proposed in our previous publication. In the younger than P10 OB-aPC circuit with GABA synaptic input, the number of responsive aPC pyramidal cells to the conditioned maternal odor was amplified in 30% compared to the circuit without GABAergic input. When compared with the circuit with other younger than P10 OB-aPC circuit with adult GABAergic input profile, this amplification was 88%. Together, our results suggest that during the olfactory preference learning in younger than P10, the GABAergic synaptic input presumably acts by depolarizing the aPC pyramidal neurons in such a way that it leads to the amplification of the pyramidal neurons response to the conditioned maternal odor. Furthermore, our results suggest that during this developmental period, the aPC pyramidal cells themselves seem to resolve the apparent lack of GABAergic synaptic inhibition by a strong firing adaptation in response to increased depolarizing inputs.

Maturation of pyramidal cells in anterior piriform cortex may be sufficient to explain the end of early olfactory learning in rats.

Studies have shown that neonate rodents exhibit high ability to learn a preference for novel odors associated with thermo-tactile stimuli that mimics maternal care. Artificial odors paired with vigorous strokes in rat pups younger than 10 postnatal days (P), but not older, rapidly induce an orientation-approximation behavior toward the conditioned odor in a two-choice preference test. The olfactory bulb (OB) and the anterior olfactory cortex (aPC), both modulated by norepinephrine (NE), have been identified as part of a neural circuit supporting this transitory olfactory learning. One possible explanation at the neuronal level for why the odor-stroke pairing induces consistent orientation-approximation behavior in P10, is the coincident activation of prior existent neurons in the aPC mediating this behavior. Specifically, odor-stroke conditioning in P10 pups, promoting orientation-approximation behavior in the former but not in the latter. In order to test this hypothesis, we performed in vitro patch-clamp recordings of the aPC pyramidal neurons from rat pups from two age groups (P5-P8 and P14-P17) and built computational models for the OB-aPC neural circuit based on this physiological data. We conditioned the P5-P8 OB-aPC artificial circuit to an odor associated with NE activation (representing the process of maternal odor learning during mother-infant interactions inside the nest) and then evaluated the response of the OB-aPC circuit to the presentation of the conditioned odor. The results show that the number of responsive aPC neurons to the presentation of the conditioned odor in the P14-P17 OB-aPC circuit was lower than in the P5-P8 circuit, suggesting that at P14-P17, the reduced number of responsive neurons to the conditioned (maternal) odor might not be coincident with the responsive neurons for a second conditioned odor.

How the Brain Represents Language and Answers Questions? Using an AI System to Understand the Underlying Neurobiological Mechanisms.

To understand the computations that underlie high-level cognitive processes we propose a framework of mechanisms that could in principle implement START, an AI program that answers questions using natural language. START organizes a sentence into a series of triplets, each containing three elements (subject, verb, object). We propose that the brain similarly defines triplets and then chunks the three elements into a spatial pattern. A complete sentence can be represented using up to 7 triplets in a working memory buffer organized by theta and gamma oscillations. This buffer can transfer information into long-term memory networks where a second chunking operation converts the serial triplets into a single spatial pattern in a network, with each triplet (with corresponding elements) represented in specialized subregions. The triplets that define a sentence become synaptically linked, thereby encoding the sentence in synaptic weights. When a question is posed, there is a search for the closest stored memory (having the greatest number of shared triplets). We have devised a search process that does not require that the question and the stored memory have the same number of triplets or have triplets in the same order. Once the most similar memory is recalled and undergoes 2-level dechunking, the sought for information can be obtained by element-by-element comparison of the key triplet in the question to the corresponding triplet in the retrieved memory. This search may require a reordering to align corresponding triplets, the use of pointers that link different triplets, or the use of semantic memory. Our framework uses 12 network processes; existing models can implement many of these, but in other cases we can only suggest neural implementations. Overall, our scheme provides the first view of how language-based question answering could be implemented by the brain.

Internal Cholinergic Regulation of Learning and Recall in a Model of Olfactory Processing.

In the olfactory system, cholinergic modulation has been associated with contrast modulation and changes in receptive fields in the olfactory bulb, as well the learning of odor associations in olfactory cortex. Computational modeling and behavioral studies suggest that cholinergic modulation could improve sensory processing and learning while preventing pro-active interference when task demands are high. However, how sensory inputs and/or learning regulate incoming modulation has not yet been elucidated. We here use a computational model of the olfactory bulb, piriform cortex (PC) and horizontal limb of the diagonal band of Broca (HDB) to explore how olfactory learning could regulate cholinergic inputs to the system in a closed feedback loop. In our model, the novelty of an odor is reflected in firing rates and sparseness of cortical neurons in response to that odor and these firing rates can directly regulate learning in the system by modifying cholinergic inputs to the system. In the model, cholinergic neurons reduce their firing in response to familiar odors-reducing plasticity in the PC, but increase their firing in response to novel odor-increasing PC plasticity. Recordings from HDB neurons in awake behaving rats reflect predictions from the model by showing that a subset of neurons decrease their firing as an odor becomes familiar.

Grid Cells and Place Cells: An Integrated View of their Navigational and Memory Function.

Much has been learned about the hippocampal/entorhinal system, but an overview of how its parts work in an integrated way is lacking. One question regards the function of entorhinal grid cells. We propose here that their fundamental function is to provide a coordinate system for producing mind-travel in the hippocampus, a process that accesses associations with upcoming positions. We further propose that mind-travel occurs during the second half of each theta cycle. By contrast, the first half of each theta cycle is devoted to computing current position using sensory information from the lateral entorhinal cortex (LEC) and path integration information from the medial entorhinal cortex (MEC). This model explains why MEC lesions can abolish hippocampal phase precession but not place fields.

A model of cholinergic modulation in olfactory bulb and piriform cortex.

In this work we investigate in a computational model how cholinergic inputs to the olfactory bulb (OB) and piriform cortex (PC) modulate odor representations. We use experimental data derived from different physiological studies of ACh modulation of the bulbar and cortical circuitry and the interaction between these two areas. The results presented here indicate that cholinergic modulation in the OB significantly increases contrast and synchronization in mitral cell output. Each of these effects is derived from distinct neuronal interactions, with different groups of interneurons playing different roles. Both bulbar modulation effects contribute to more stable learned representations in PC, with pyramidal networks trained with cholinergic-modulated inputs from the bulb exhibiting more robust learning than those trained with unmodulated bulbar inputs. This increased robustness is evidenced as better recovery of memories from corrupted patterns and lower-concentration inputs as well as increased memory capacity.

Alternating predictive and short-term memory modes of entorhinal grid cells.

Several lines of evidence indicate that the entorhinal cortex has memory functions, but such functions have not been previously found in grid cells, a cell type that provides major input to the hippocampus. We examined the firing of grid cells as rats crossed (runs) through grid cell vertices. We found that on some runs, firing tended to occur mostly inbound as the rat approached a vertex center while on other runs firing occurred mainly outbound. These results suggest that cells have a predictive mode (inbound firing) in which they represent a position ahead of the animal and a short term memory (STM) mode (outbound firing) in which they represent positions just passed through. Analysis of cell pairs showed that when vertex crossings were less than 1 second apart, the two cells tended to have the same mode. This indicates that modes are a network property. The tendency to have the same mode disappeared if crossings were separated by 2-3 seconds, suggesting that modes alternate on the time scale of seconds. There was a small but statistically significant behavioral correlate of modes: velocity was slightly less in the STM mode. Both modes were organized by theta and gamma oscillations. The results suggest that the dual requirement for hippocampal storage and recall is met by rapidly alternating modes appropriate for predicting the future and storing the recent past.

The single place fields of CA3 cells: a two-stage transformation from grid cells.

Granule cells of the dentate gyrus (DG) generally have multiple place fields, whereas CA3 cells, which are second order, have only a single place field. Here, we explore the mechanisms by which the high selectivity of CA3 cells is achieved. Previous work showed that the multiple place fields of DG neurons could be quantitatively accounted for by a model based on the number and strength of grid cell inputs and a competitive network interaction in the DG that is mediated by gamma frequency feedback inhibition. We have now built a model of CA3 based on similar principles. CA3 cells receive input from an average of one active DG cell and from 1,400 cortical grid cells. Based on experimental findings, we have assumed a linear interaction of the two pathways. The results show that simulated CA3 cells generally have a single place field, as observed experimentally. Thus, a two-step process based on simple rules (and that can occur without learning) is able to explain how grid cell inputs to the hippocampus give rise to cells having ultimate spatial selectivity. The CA3 processes that produce a single place depend critically on the competitive network processes and do not require the direct cortical inputs to CA3, which are therefore likely to perform some other unknown function.

A second function of gamma frequency oscillations: an E%-max winner-take-all mechanism selects which cells fire.

The role of gamma oscillations in producing synchronized firing of groups of principal cells is well known. Here, we argue that gamma oscillations have a second function: they select which principal cells fire. This selection process occurs through the interaction of excitation with gamma frequency feedback inhibition. We sought to understand the rules that govern this process. One possibility is that a constant fraction of cells fire. Our analysis shows, however, that the fraction is not robust because it depends on the distribution of excitation to different cells. A robust description is termed E%-max: cells fire if they have suprathreshold excitation (E) within E% of the cell that has maximum excitation. The value of E%-max is approximated by the ratio of the delay of feedback inhibition to the membrane time constant. From measured values, we estimate that E%-max is 5-15%. Thus, an E%-max winner-take-all process can discriminate between groups of cells that have only small differences in excitation. To test the utility of this framework, we analyzed the role of oscillations in V1, one of the few systems in which both spiking and intracellular excitation have been directly measured. We show that an E%-max winner-take-all process provides a simple explanation for why the orientation tuning of firing is narrower than that of the excitatory input and why this difference is not affected by increasing excitation. Because gamma oscillations occur in many brain regions, the framework we have developed for understanding the second function of gamma is likely to have wide applicability.

The input-output transformation of the hippocampal granule cells: from grid cells to place fields.

Grid cells in the rat medial entorhinal cortex fire (periodically) over the entire environment. These cells provide input to hippocampal granule cells whose output is characterized by one or more small place fields. We sought to understand how this input-output transformation occurs. Available information allows simulation of this process with no freely adjustable parameters. We first examined the spatial distribution of excitation in granule cells produced by the convergence of excitatory inputs from randomly chosen grid cells. Because the resulting summation depends on the number of inputs, it is necessary to use a realistic number (approximately 1200) and to take into consideration their 20-fold variation in strength. The resulting excitation maps have only modest peaks and valleys. To analyze how this excitation interacts with inhibition, we used an E%-max (percentage of maximal suprathreshold excitation) winner-take-all rule that describes how gamma-frequency inhibition affects firing. We found that simulated granule cells have firing maps that have one or more place fields whose size and number approximates those observed experimentally. A substantial fraction of granule cells have no place fields, as observed experimentally. Because the input firing rates and synaptic properties are known, the excitatory charge into granule cells could be calculated (2-3 pC) and was found to be only somewhat larger than required to fire granule cells (1 pC). We conclude that the input-output transformation of dentate granule does not depend strongly on synaptic modification; place field formation can be understood in terms of simple summation of randomly chosen excitatory inputs, in conjunction with a winner-take-all network mechanism.

Memory retrieval time and memory capacity of the CA3 network: role of gamma frequency oscillations.

The existence of recurrent synaptic connections in CA3 led to the hypothesis that CA3 is an autoassociative network similar to the Hopfield networks studied by theorists. CA3 undergoes gamma frequency periodic inhibition that prevents a persistent attractor state. This argues against the analogy to Hopfield nets, in which an attractor state can be used for working memory. However, we show that such periodic inhibition allows one cycle of recurrent excitatory activity and that this is sufficient for memory retrieval (within milliseconds). Thus, gamma oscillations are compatible with a long-term autoassociative memory function for CA3. A second goal of our work was to evaluate previous methods for estimating the memory capacity (P) of CA3. We confirm the equation, P = c/a(2), where c is the probability that any two cells are recurrently connected and a is the fraction of cells representing a memory item. In applying this to CA3, we focus on CA3a, the subregion where recurrent connections are most numerous (c = 0.2) and approximate randomness. We estimate that a memory item is represented by approximately 225 of the 70,000 neurons in CA3a (a = 0.003) and that approximately 20,000 memory items can be stored. Our general conclusion is that the physiological and anatomical findings of CA3a are consistent with an autoassociative function. The nature of the information that is associated in CA3a is discussed. We also discuss how the autoassociative properties of CA3 and the heteroassociative properties of dentate synapses (linking sequential memories) form an integrated system for the storage and recall of item sequences. The recall process generates the phase precession in dentate, CA3, and entorhinal cortex.

Retrieval-time properties of the Little-Hopfield model and their physiological relevance.

We perform an extensive numerical investigation on the retrieval dynamics of the synchronous Hopfield model, also known as Little-Hopfield model, up to sizes of 2(18) neurons. Our results correct and extend much of the early simulations on the model. We find that the average convergence time has a power law behavior for a wide range of system sizes, whose exponent depends both on the network loading and the initial overlap with the memory to be retrieved. Surprisingly, we also find that the variance of the convergence time grows as fast as its average, making it a non-self-averaging quantity. Based on the simulation data we differentiate between two definitions for memory retrieval time, one that is mathematically strict, tau(c), the number of updates needed to reach the attractor whose properties we just described, and a second definition correspondent to the time tau(eta) when the network stabilizes within a tolerance threshold eta such that the difference of two consecutive overlaps with a stored memory is smaller that eta. We show that the scaling relationships between tau(c) and tau(eta) and the typical network parameters as the memory load alpha or the size of the network N vary greatly, being tau(eta) relatively insensitive to system sizes and loading. We propose tau(eta) as the physiological realistic measure for the typical attractor network response.

Rupture of a liposomal vesicle.

We discuss pore dynamics in osmotically stressed vesicles. A set of equations which govern the liposomal size, internal solute concentration, and pore diameter is solved numerically. We find that dependent on the internal solute concentration and vesicle size, liposomes can stay pore free, nucleate a short-lived pore, or nucleate a long-lived pore. The phase diagram of pore stability is constructed, and the different scaling regimes are deduced analytically.