Metabolic constraints on synaptic learning and memory

Information Thermodynamics: From Physics to Neuroscience

This paper provides a perspective on applying the concepts of information thermodynamics, developed recently in non-equilibrium statistical physics, to problems in theoretical neuroscience. Historically, information and energy in neuroscience have been treated separately, in contrast to physics approaches, where the relationship of entropy production with heat is a central idea. It is argued here that also in neural systems, information and energy can be considered within the same theoretical framework. Starting from basic ideas of thermodynamics and information theory on a classic Brownian particle, it is shown how noisy neural networks can infer its probabilistic motion. The decoding of the particle motion by neurons is performed with some accuracy, and it has some energy cost, and both can be determined using information thermodynamics. In a similar fashion, we also discuss how neural networks in the brain can learn the particle velocity and maintain that information in the weights of plastic synapses from a physical point of view. Generally, it is shown how the framework of stochastic and information thermodynamics can be used practically to study neural inference, learning, and information storing.

Brain energy metabolism as an underlying basis of slow and fast cognitive phenotypes in honeybees

In the context of slow-fast behavioral variation, fast individuals are hypothesized to be those who prioritize speed over accuracy while slow individuals are those which do the opposite. Since energy metabolism is a critical component of neural and cognitive functioning, this predicts such differences in cognitive style to be reflected at the level of the brain. We tested this idea in honeybees by first classifying individuals into slow and fast cognitive phenotypes based on a learning assay and then measuring their brain respiration with high-resolution respirometry. Our results broadly show that inter-individual differences in cognition are reflected in differences in brain mass and accompanying energy use at the level of the brain and the whole animal. Larger brains had lower mass-specific energy usage and bees with larger brains had a higher metabolic rate. These differences in brain respiration and brain mass were, in turn, associated with cognitive differences, such that bees with larger brains were fast cognitive phenotypes whereas those with smaller brains were slow cognitive phenotypes. We discuss these results in the context of the role of energy in brain functioning and slow-fast decision making and speed accuracy trade-off.

Signatures of Bayesian inference emerge from energy-efficient synapses

Biological synaptic transmission is unreliable, and this unreliability likely degrades neural circuit performance. While there are biophysical mechanisms that can increase reliability, for instance by increasing vesicle release probability, these mechanisms cost energy. We examined four such mechanisms along with the associated scaling of the energetic costs. We then embedded these energetic costs for reliability in artificial neural networks (ANNs) with trainable stochastic synapses, and trained these networks on standard image classification tasks. The resulting networks revealed a tradeoff between circuit performance and the energetic cost of synaptic reliability. Additionally, the optimised networks exhibited two testable predictions consistent with pre-existing experimental data. Specifically, synapses with lower variability tended to have (1) higher input firing rates and (2) lower learning rates. Surprisingly, these predictions also arise when synapse statistics are inferred through Bayesian inference. Indeed, we were able to find a formal, theoretical link between the performance-reliability cost tradeoff and Bayesian inference. This connection suggests two incompatible possibilities: evolution may have chanced upon a scheme for implementing Bayesian inference by optimising energy efficiency, or alternatively, energy-efficient synapses may display signatures of Bayesian inference without actually using Bayes to reason about uncertainty.

Deletion of CaMKIIα disrupts glucose metabolism, glutamate uptake, and synaptic energetics in the cerebral cortex

Ca2+/calmodulin-dependent protein kinase II alpha (CaMKIIα) is a key regulator of neuronal signaling and synaptic plasticity. Synaptic activity and neurotransmitter homeostasis are closely coupled to the energy metabolism of both neurons and astrocytes. However, whether CaMKIIα function is implicated in brain energy and neurotransmitter metabolism remains unclear. Here, we explored the metabolic consequences of CaMKIIα deletion in the cerebral cortex using a genetic CaMKIIα knockout (KO) mouse. Energy and neurotransmitter metabolism was functionally investigated in acutely isolated cerebral cortical slices using stable 13C isotope tracing, whereas the metabolic function of synaptosomes was assessed by the rates of glycolytic activity and mitochondrial respiration. The oxidative metabolism of [U-13C]glucose was extensively reduced in cerebral cortical slices of the CaMKIIα KO mice. In contrast, metabolism of [1,2-13C]acetate, primarily reflecting astrocyte metabolism, was unaffected. Cellular uptake, and subsequent metabolism, of [U-13C]glutamate was decreased in cerebral cortical slices of CaMKIIα KO mice, whereas uptake and metabolism of [U-13C]GABA were unaffected, suggesting selective metabolic impairments of the excitatory system. Synaptic metabolic function was maintained during resting conditions in isolated synaptosomes from CaMKIIα KO mice, but both the glycolytic and mitochondrial capacities became insufficient when the synaptosomes were metabolically challenged. Collectively, this study shows that global deletion of CaMKIIα significantly impairs cellular energy and neurotransmitter metabolism, particularly of neurons, suggesting a metabolic role of CaMKIIα signaling in the brain.

Bounds on the rates of statistical divergences and mutual information via stochastic thermodynamics

Statistical divergences are important tools in data analysis, information theory, and statistical physics, and there exist well-known inequalities on their bounds. However, in many circumstances involving temporal evolution, one needs limitations on the rates of such quantities instead. Here, several general upper bounds on the rates of some f-divergences are derived, valid for any type of stochastic dynamics (both Markovian and non-Markovian), in terms of information-like and/or thermodynamic observables. As special cases, the analytical bounds on the rate of mutual information are obtained. The major role in all those limitations is played by temporal Fisher information, characterizing the speed of global system dynamics, and some of them contain entropy production, suggesting a link with stochastic thermodynamics. Indeed, the derived inequalities can be used for estimation of minimal dissipation and global speed in thermodynamic stochastic systems. Specific applications of these inequalities in physics and neuroscience are given, which include the bounds on the rates of free energy and work in nonequilibrium systems, limits on the speed of information gain in learning synapses, as well as the bounds on the speed of predictive inference and learning rate. Overall, the derived bounds can be applied to any complex network of interacting elements, where predictability and thermodynamics of network dynamics are of prime concern.

Protective Effects of Long-Term Escitalopram Administration on Memory and Hippocampal BDNF and BCL-2 Gene Expressions in Rats Exposed to Predictable and Unpredictable Chronic Mild Stress

Stress and escitalopram (an anti-stress medication) can affect brain functions and related gene expression. This study investigated the protective effects of long-term escitalopram administration on memory, as well as on hippocampal BDNF and BCL-2 gene expressions in rats exposed to predictable and unpredictable chronic mild stress (PCMS and UCMS, respectively). Male rats were randomly assigned to different groups: control (Co), sham (Sh), predictable and unpredictable stress (PSt and USt, respectively; 2 h/day for 21 consecutive days), escitalopram (Esc; 10 mg/kg for 21 days), and predictable and unpredictable stress with escitalopram (PSt-Esc and USt-Esc, respectively). The passive avoidance test was used to assess behavioral variables. The expressions of the BDNF and BCL-2 genes were assessed using real-time quantitative PCR. Latency significantly decreased in the PSt and USt groups. Additionally, latency showed significant improvement in the PSt-Esc group compared to the PSt group. The expression of the BDNF gene significantly decreased only in the USt group. BDNF gene expression significantly increased in the PSt-Esc and USt-Esc groups compared to their respective stress-related groups, whereas the expression of the BCL-2 gene did not change significantly in both PSt-Esc and USt-Esc groups. PCMS and UCMS had devastating effects on memory. Escitalopram improved memory only under PCMS conditions. PCMS and UCMS exhibited fundamental differences in hippocampal BDNF and BCL-2 gene expressions. Furthermore, escitalopram increased hippocampal BDNF gene expression in the PCMS and UCMS subjects. Hence, neurogenesis occurred more significantly than anti-apoptosis under both PCMS and UCMS conditions with escitalopram.

Cooperativity, Information Gain, and Energy Cost During Early LTP in Dendritic Spines

We investigate a mutual relationship between information and energy during the early phase of LTP induction and maintenance in a large-scale system of mutually coupled dendritic spines, with discrete internal states and probabilistic dynamics, within the framework of nonequilibrium stochastic thermodynamics. In order to analyze this computationally intractable stochastic multidimensional system, we introduce a pair approximation, which allows us to reduce the spine dynamics into a lower-dimensional manageable system of closed equations. We found that the rates of information gain and energy attain their maximal values during an initial period of LTP (i.e., during stimulation), and after that, they recover to their baseline low values, as opposed to a memory trace that lasts much longer. This suggests that the learning phase is much more energy demanding than the memory phase. We show that positive correlations between neighboring spines increase both a duration of memory trace and energy cost during LTP, but the memory time per invested energy increases dramatically for very strong, positive synaptic cooperativity, suggesting a beneficial role of synaptic clustering on memory duration. In contrast, information gain after LTP is the largest for negative correlations, and energy efficiency of that information generally declines with increasing synaptic cooperativity. We also find that dendritic spines can use sparse representations for encoding long-term information, as both energetic and structural efficiencies of retained information and its lifetime exhibit maxima for low fractions of stimulated synapses during LTP. Moreover, we find that such efficiencies drop significantly with increasing the number of spines. In general, our stochastic thermodynamics approach provides a unifying framework for studying, from first principles, information encoding, and its energy cost during learning and memory in stochastic systems of interacting synapses.

Sirtuin3 ensures the metabolic plasticity of neurotransmission during glucose deprivation

Neurotransmission is an energetically expensive process that underlies cognition. During intense electrical activity or dietary restrictions, the glucose level in the brain plummets, forcing neurons to utilize alternative fuels. However, the molecular mechanisms of neuronal metabolic plasticity remain poorly understood. Here, we demonstrate that glucose-deprived neurons activate the CREB and PGC1α transcriptional program, which induces expression of the mitochondrial deacetylase Sirtuin 3 (Sirt3) both in vitro and in vivo. We show that Sirt3 localizes to axonal mitochondria and stimulates mitochondrial oxidative capacity in hippocampal nerve terminals. Sirt3 plays an essential role in sustaining synaptic transmission in the absence of glucose by providing metabolic support for the retrieval of synaptic vesicles after release. These results demonstrate that the transcriptional induction of Sirt3 facilitates the metabolic plasticity of synaptic transmission.

Information encoded in volumes and areas of dendritic spines is nearly maximal across mammalian brains

Many experiments suggest that long-term information associated with neuronal memory resides collectively in dendritic spines. However, spines can have a limited size due to metabolic and neuroanatomical constraints, which should effectively limit the amount of encoded information in excitatory synapses. This study investigates how much information can be stored in the population of sizes of dendritic spines, and whether it is optimal in any sense. It is shown here, using empirical data for several mammalian brains across different regions and physiological conditions, that dendritic spines nearly maximize entropy contained in their volumes and surface areas for a given mean size in cortical and hippocampal regions. Although both short- and heavy-tailed fitting distributions approach [Formula: see text] of maximal entropy in the majority of cases, the best maximization is obtained primarily for short-tailed gamma distribution. We find that most empirical ratios of standard deviation to mean for spine volumes and areas are in the range [Formula: see text], which is close to the theoretical optimal ratios coming from entropy maximization for gamma and lognormal distributions. On average, the highest entropy is contained in spine length ([Formula: see text] bits per spine), and the lowest in spine volume and area ([Formula: see text] bits), although the latter two are closer to optimality. In contrast, we find that entropy density (entropy per spine size) is always suboptimal. Our results suggest that spine sizes are almost as random as possible given the constraint on their size, and moreover the general principle of entropy maximization is applicable and potentially useful to information and memory storing in the population of cortical and hippocampal excitatory synapses, and to predicting their morphological properties.

The influence of learning history on anterograde interference

Classically interpreted as a competition between opposite memories (A vs B), anterograde interference (AI) also emerges in the absence of competing memories (A vs A), suggesting that mechanisms other than those involved in memory competition contribute to AI. To investigate this, we tested the hypothesis that extending motor practice would enhance a first memory, but come at the cost of reduced learning capabilities when subsequently exposed to a second learning session of the same task. Based on converging biological evidence, AI was expected to depend upon the degree of extended practice of the initial exposure. During a first Session, four conditions were carried out where participants (n = 24) adapted to a gradually introduced -20° visual deviation while the extent of the initial exposure was manipulated by varying the duration or type of the performance asymptote. Specifically, the performance asymptote at -20° was either Short (40 trials), Moderate (160 trials), Long (320 trials), or absent due to continuously changing perturbations around the mean of -20° (Jagged; 160 trials). After a 2-min interval, participants re-adapted to the same (-20°) visual deviation, which was meant to probe the effect of extended practice in the first Session on the learning capabilities of a second identical memory (A vs A). The results first confirmed that the duration of exposure in the first Session enhanced immediate aftereffects in the Moderate, Long, and Jagged conditions as compared to the Short condition, suggesting that extended practice enhanced retention of the first memory. When comparing the second Session to the first one, results revealed a different pattern of re-adaptation depending on the duration of initial exposure: in the Short condition, there was evidence for facilitated re-adaptation and similar aftereffects. However, in the Moderate, Long and Jagged conditions, re-adaptation was similar and aftereffects were impaired, suggestive of AI. This suggests that extended practice initially enhances memory formation, but comes at the cost of reduced subsequent learning capabilities. One possibility is that AI occurs because extended practice induces the emergence of network-specific homeostatic constraints, which limit subsequent neuroplastic and learning capabilities in the same neural network.

Energetically efficient learning in neuronal networks

Human and animal experiments have shown that acquiring and storing information can require substantial amounts of metabolic energy. However, computational models of neural plasticity only seldom take this cost into account, and might thereby miss an important constraint on biological learning. This review explores various ways to reduce energy requirements for learning in neural networks. By comparing the resulting learning rules to cognitive and neurophysiological observations, we discuss how energy efficiency might have shaped biological learning.

Enhanced presynaptic mitochondrial energy production is required for memory formation

Some of the prominent features of long-term memory formation include protein synthesis, gene expression, enhanced neurotransmitter release, increased excitability, and formation of new synapses. As these processes are critically dependent on mitochondrial function, we hypothesized that increased mitochondrial respiration and dynamics would play a prominent role in memory formation. To address this possibility, we measured mitochondrial oxygen consumption (OCR) in hippocampal tissue punches from trained and untrained animals. Our results show that context fear training significantly increased basal, ATP synthesis-linked, and maximal OCR in the Shaffer collateral-CA1 synaptic region, but not in the CA1 cell body layer. These changes were recapitulated in synaptosomes isolated from the hippocampi of fear-trained animals. As dynamin-related protein 1 (Drp1) plays an important role in mitochondrial fission, we examined its role in the increased mitochondrial respiration observed after fear training. Drp1 inhibitors decreased the training-associated enhancement of OCR and impaired contextual fear memory, but did not alter the number of synaptosomes containing mitochondria. Taken together, our results show context fear training increases presynaptic mitochondria respiration, and that Drp-1 mediated enhanced energy production in CA1 pre-synaptic terminals is necessary for context fear memory that does not result from an increase in the number of synaptosomes containing mitochondria or an increase in mitochondrial mass within the synaptic layer.

Computational modeling of AMPK and mTOR crosstalk in glutamatergic synapse calcium signaling

Neuronal energy consumption is vital for information processing and memory formation in synapses. The brain consists of just 2% of the human body's mass, but consumes almost 20% of the body's energy budget. Most of this energy is attributed to active transport in ion signaling, with calcium being the canonical second messenger of synaptic transmission. Here, we develop a computational model of synaptic signaling resulting in the activation of two protein kinases critical in metabolic regulation and cell fate, AMP-Activated protein kinase (AMPK) and mammalian target of rapamycin (mTOR) and investigate the effect of glutamate stimulus frequency on their dynamics. Our model predicts that frequencies of glutamate stimulus over 10 Hz perturb AMPK and mTOR oscillations at higher magnitudes by up to 36% and change the area under curve (AUC) by 5%. This dynamic difference in AMPK and mTOR activation trajectories potentially differentiates high frequency stimulus bursts from basal neuronal signaling leading to a downstream change in synaptic plasticity. Further, we also investigate the crosstalk between insulin receptor and calcium signaling on AMPK and mTOR activation and predict that the pathways demonstrate multistability dependent on strength of insulin signaling and metabolic consumption rate. Our predictions have implications for improving our understanding of neuronal metabolism, synaptic pruning, and synaptic plasticity.

Role of Bmal1 in Type 2 Diabetes Mellitus-Related Glycolipid Metabolic Disorder and Neuropsychiatric Injury: Involved in the Regulation of Synaptic Plasticity and Circadian Rhythms

Increasing data suggest a crucial role of circadian rhythm in regulating metabolic and neurological diseases, and Bmal1 is regarded as a key regulator of circadian transcription. The aim of this study is to investigate the role of Bmal1 in the disruption of circadian rhythm and neuropsychiatric injuries in type 2 diabetes mellitus (T2DM). A T2DM model was induced by the combination of high-fat-diet (HFD) and streptozotocin (STZ) in vivo or HT-22 cells challenged with palmitic-acid (PA) in vitro. The glucolipid metabolism indicators, behavioral performance, and expression of synaptic plasticity proteins and circadian rhythm-related proteins were detected. These changes were also observed after interference of Bmal1 expression via overexpressed plasmid or small interfering RNAs in vitro. The results showed that HFD/STZ could induce T2DM-like glycolipid metabolic turmoil and abnormal neuropsychiatric behaviors in mice, as indicated by the increased concentrations of fasting blood-glucose (FBG), HbA1c and lipids, the impaired glucose tolerance, and the decreased preference index of novel object or novel arm in the novel object recognition test (NOR) and Y-maze test (Y-maze). Consistently, the protein expression of synaptic plasticity proteins and circadian rhythm-related proteins and the positive fluorescence intensity of MT1B and Bmal1 were decreased in the hippocampus of HFD/STZ-induced mice or PA-challenged HT-22 cells. Furthermore, overexpression of Bmal1 could improve the PA-induced lipid metabolic dysfunction and increase the decreased expressions of synaptic plasticity proteins and circadian rhythm-related proteins, and vice versa. These results suggested a crucial role of Bmal1 in T2DM-related glycolipid metabolic disorder and neuropsychiatric injury, which mechanism might be involved in the regulation of synaptic plasticity and circadian rhythms.

Sirtuin3 ensures the metabolic plasticity of neurotransmission during glucose deprivation

Neurotransmission is an energetically expensive process that underlies cognition. During intense electrical activity or dietary restrictions, glucose levels in the brain plummet, forcing neurons to utilize alternative fuels. However, the molecular mechanisms of neuronal metabolic plasticity remain poorly understood. Here, we demonstrate that glucose-deprived neurons activate the CREB and PGC1α transcriptional program that induces the expression of the mitochondrial deacetylase Sirtuin 3 (Sirt3) both in vitro and in vivo . We show that Sirt3 localizes to axonal mitochondria and stimulates mitochondrial oxidative capacity in hippocampal nerve terminals. Sirt3 plays an essential role in sustaining synaptic transmission in the absence of glucose by powering the retrieval of synaptic vesicles after release. These results demonstrate that the transcriptional induction of Sirt3 ensures the metabolic plasticity of synaptic transmission.

Highlights

Glucose deprivation drives transcriptional reprogramming of neuronal metabolism via CREB and PGC1α. Glucose or food deprivation trigger the neuronal expression of mitochondrial deacetylase sirtuin 3 (Sirt3) both in vitro and in vivo . Sirt3 stimulates oxidative ATP synthesis in nerve terminals.Sirt3 sustains the synaptic vesicle cycle in the absence of glucose.

The molecular memory code and synaptic plasticity: A synthesis

The most widely accepted view of memory in the brain holds that synapses are the storage sites of memory, and that memories are formed through associative modification of synapses. This view has been challenged on conceptual and empirical grounds. As an alternative, it has been proposed that molecules within the cell body are the storage sites of memory, and that memories are formed through biochemical operations on these molecules. This paper proposes a synthesis of these two views, grounded in a computational model of memory. Synapses are conceived as storage sites for the parameters of an approximate posterior probability distribution over latent causes. Intracellular molecules are conceived as storage sites for the parameters of a generative model. The model stipulates how these two components work together as part of an integrated algorithm for learning and inference.

Interplay between metabolic energy regulation and memory pathways in Drosophila

Regulating energy metabolism is critical to maintain homeostasis of cellular and systemic functions. In the brain, specialised centres for energy storage regulation finely communicate with the periphery and integrate signals about internal states. As a result, the behavioural responses can be directly adjusted accordingly to the energetic demands. In the fruit fly Drosophila melanogaster, one of these regulatory centres is the mushroom bodies (MBs), a brain region involved in olfactory memory. The integration of metabolic cues by the MBs has a crucial impact on learned behaviour. In this review, we explore recent advances supporting the interplay between energy metabolism and memory establishment, as well as the instructive role of energy during the switch between memory phases.

Postsynaptic Potential Energy as Determinant of Synaptic Plasticity

Metabolic energy can be used as a unifying principle to control neuronal activity. However, whether and how metabolic energy alone can determine the outcome of synaptic plasticity remains unclear. This study proposes a computational model of synaptic plasticity that is completely determined by energy. A simple quantitative relationship between synaptic plasticity and postsynaptic potential energy is established. Synaptic weight is directly proportional to the difference between the baseline potential energy and the suprathreshold potential energy and is constrained by the maximum energy supply. Results show that the energy constraint improves the performance of synaptic plasticity and avoids setting the hard boundary of synaptic weights. With the same set of model parameters, our model can reproduce several classical experiments in homo- and heterosynaptic plasticity. The proposed model can explain the interaction mechanism of Hebbian and homeostatic plasticity at the cellular level. Homeostatic synaptic plasticity at different time scales coexists. Homeostatic plasticity operating on a long time scale is caused by heterosynaptic plasticity and, on the same time scale as Hebbian synaptic plasticity, is caused by the constraint of energy supply.

EphB2 activates CREB-dependent expression of Annexin A1 to regulate dendritic spine morphogenesis

Dendritic spines are the postsynaptic structure to mediate signal transduction in neural circuitry, whose function and plasticity are regulated by organization of their molecular architecture and by the expression of target genes and proteins. EphB2, a member of the Eph receptor tyrosine kinase family, potentiates dendritic spine maturation through cytoskeleton reorganization and protein trafficking. However, the transcriptional mechanisms underlying prolonged activation of EphB2 signaling during dendritic spine morphogenesis are unknown. Herein, we performed transcriptional profiling by stimulating EphB2 signaling and identified differentially expressed genes implicated in pivotal roles at synapses. Notably, we characterized an F-actin binding protein, Annexin A1, whose expression was induced by EphB2 signaling; the promotor activity of its coding gene Anxa1 is regulated by the activity of CREB (cAMP-response element-binding protein). Knockdown of Annexin A1 led to a significant reduction of mature dendritic spines without an obvious deficit in the complexity of dendrites. Altogether, our findings suggest that EphB2-induced, CREB-dependent Annexin A1 expression plays a key role in regulating dendritic spine morphology.

Protective effect of quercetin against the metabolic dysfunction of glucose and lipids and its associated learning and memory impairments in NAFLD rats

BACKGROUND

Quercetin (QUE) is a flavonol reported with anti-inflammatory and antioxidant activities, and previous results from the group of this study have demonstrated its neuroprotective effect against lipopolysaccharide-induced neuropsychiatric injuries. However, little is known about its potential effect on neuropsychiatric injuries induced or accompanied by metabolic dysfunction of glucose and lipids.

METHODS

A nonalcoholic fatty liver disease (NAFLD) rat model was induced via a high-fat diet (HFD), and glucolipid parameters and liver function were measured. Behavioral performance was observed via the open field test (OFT) and the Morris water maze (MWM). The plasma levels of triggering receptor expressed on myeloid cells-1 (TREM1) and TREM2 were measured via enzyme-linked immunosorbent assay (ELISA). The protein expression levels of Synapsin-1 (Syn-1), Synaptatogmin-1 (Syt-1), TREM1 and TREM2 in the hippocampus were detected using western blotting. Morphological changes in the liver and hippocampus were detected by HE and Oil red or silver staining.

RESULTS

Compared with the control rats, HFD-induced NAFLD model rats presented significant metabolic dysfunction, hepatocyte steatosis, and impaired learning and memory ability, as indicated by the increased plasma concentrations of total cholesterol (TC) and triglyceride (TG), the impaired glucose tolerance, the accumulated fat droplets and balloon-like changes in the liver, and the increased escaping latency but decreased duration in the target quadrant in the Morris water maze. All these changes were reversed in QUE-treated rats. Moreover, apart from improving the morphological injuries in the hippocampus, treatment with QUE could increase the decreased plasma concentration and hippocampal protein expression of TREM1 in NAFLD rats and increase the decreased expression of Syn-1 and Syt-1 in the hippocampus.

CONCLUSIONS

These results suggested the therapeutic potential of QUE against NAFLD-associated impairment of learning and memory, and the mechanism might involve regulating the metabolic dysfunction of glucose and lipids and balancing the protein expression of synaptic plasticity markers and TREM1/2 in the hippocampus.

Systems modeling predicts that mitochondria ER contact sites regulate the postsynaptic energy landscape

Spatiotemporal compartmentation of calcium dynamics is critical for neuronal function, particularly in postsynaptic spines. This exquisite level of Ca²⁺ compartmentalization is achieved through the storage and release of Ca²⁺ from various intracellular organelles particularly the endoplasmic reticulum (ER) and the mitochondria. Mitochondria and ER are established storage organelles controlling Ca²⁺ dynamics in neurons. Mitochondria also generate a majority of energy used within postsynaptic spines to support the downstream events associated with neuronal stimulus. Recently, high resolution microscopy has unveiled direct contact sites between the ER and the mitochondria (MERCs), which directly channel Ca²⁺ release from the ER into the mitochondrial membrane. In this study, we develop a computational 3D reaction-diffusion model to investigate the role of MERCs in regulating Ca²⁺ and ATP dynamics. This spatiotemporal model accounts for Ca²⁺ oscillations initiated by glutamate stimulus of metabotropic and ionotropic glutamate receptors and Ca²⁺ changes in four different compartments: cytosol, ER, mitochondria, and the MERC microdomain. Our simulations predict that the organization of these organelles and inter-organellar contact sites play a key role in modulating Ca²⁺ and ATP dynamics.

We further show that the crosstalk between geometry (mitochondria and MERC) and metabolic parameters (cytosolic ATP hydrolysis, ATP generation) influences the neuronal energy state. Our findings shed light on the importance of organelle interactions in predicting Ca²⁺ dynamics in synaptic signaling. Overall, our model predicts that a combination of MERC linkage and mitochondria size is necessary for optimal ATP production in the cytosol.

On the theory of mental representation block. a novel perspective on learning and behavior

Understanding the mechanisms behind memory, learning, and behavior is crucial to human development and significant research has been done in this area. Classical and operant conditioning and other theories of learning have elucidated different mechanisms of learning and how it modulates behavior. Even with advances in this area, questions remain on how to unlearn faulty ideas or extinguish maladaptive behaviors. In this paper, a novel theory to improve our understanding of this area is proposed. The theory proposes that as a consequence of the brain's energy efficiency evolutionary adaptations, all learning following memory consolidation, reconsolidation, and repeated reinforcements or strengthening over time, results in a phenomenon called mental representation block. The implications of this block on learning and behavior are significant and broad and include cognitive biases, belief in a creator or God, close-mindedness, dogmatism, physician misdiagnosis, racism, homophobia, and transphobia, susceptibility to deception and indoctrination, hate and love, artificial intelligence and creativity.

Energetics of stochastic BCM type synaptic plasticity and storing of accurate information

Excitatory synaptic signaling in cortical circuits is thought to be metabolically expensive. Two fundamental brain functions, learning and memory, are associated with long-term synaptic plasticity, but we know very little about energetics of these slow biophysical processes. This study investigates the energy requirement of information storing in plastic synapses for an extended version of BCM plasticity with a decay term, stochastic noise, and nonlinear dependence of neuron’s firing rate on synaptic current (adaptation). It is shown that synaptic weights in this model exhibit bistability. In order to analyze the system analytically, it is reduced to a simple dynamic mean-field for a population averaged plastic synaptic current. Next, using the concepts of nonequilibrium thermodynamics, we derive the energy rate (entropy production rate) for plastic synapses and a corresponding Fisher information for coding presynaptic input. That energy, which is of chemical origin, is primarily used for battling fluctuations in the synaptic weights and presynaptic firing rates, and it increases steeply with synaptic weights, and more uniformly though nonlinearly with presynaptic firing. At the onset of synaptic bistability, Fisher information and memory lifetime both increase sharply, by a few orders of magnitude, but the plasticity energy rate changes only mildly. This implies that a huge gain in the precision of stored information does not have to cost large amounts of metabolic energy, which suggests that synaptic information is not directly limited by energy consumption. Interestingly, for very weak synaptic noise, such a limit on synaptic coding accuracy is imposed instead by a derivative of the plasticity energy rate with respect to the mean presynaptic firing, and this relationship has a general character that is independent of the plasticity type. An estimate for primate neocortex reveals that a relative metabolic cost of BCM type synaptic plasticity, as a fraction of neuronal cost related to fast synaptic transmission and spiking, can vary from negligible to substantial, depending on the synaptic noise level and presynaptic firing.

Phenotypic Switching Resulting From Developmental Plasticity: Fixed or Reversible?

The prevalent view of developmental phenotypic switching holds that phenotype modifications occurring during critical windows of development are "irreversible" - that is, once produced by environmental perturbation, the consequent juvenile and/or adult phenotypes are indelibly modified. Certainly, many such changes appear to be non-reversible later in life. Yet, whether animals with switched phenotypes during early development are unable to return to a normal range of adult phenotypes, or whether they do not experience the specific environmental conditions necessary for them to switch back to the normal range of adult phenotypes, remains an open question. Moreover, developmental critical windows are typically brief, early periods punctuating a much longer period of overall development. This leaves open additional developmental time for reversal (correction) of a switched phenotype resulting from an adverse environment early in development. Such reversal could occur from right after the critical window "closes," all the way into adulthood. In fact, examples abound of the capacity to return to normal adult phenotypes following phenotypic changes enabled by earlier developmental plasticity. Such examples include cold tolerance in the fruit fly, developmental switching of mouth formation in a nematode, organization of the spinal cord of larval zebrafish, camouflage pigmentation formation in larval newts, respiratory chemosensitivity in frogs, temperature-metabolism relations in turtles, development of vascular smooth muscle and kidney tissue in mammals, hatching/birth weight in numerous vertebrates,. More extreme cases of actual reversal (not just correction) occur in invertebrates (e.g., hydrozoans, barnacles) that actually 'backtrack' along normal developmental trajectories from adults back to earlier developmental stages. While developmental phenotypic switching is often viewed as a permanent deviation from the normal range of developmental plans, the concept of developmental phenotypic switching should be expanded to include sufficient plasticity allowing subsequent correction resulting in the normal adult phenotype.