Storage Capacity Diverges With Synaptic Efficiency in an Associative Memory Model With Synaptic Delay and Pruning

Neural Field Continuum Limits and the Structure–Function Partitioning of Cognitive–Emotional Brain Networks

In The cognitive-emotional brain, Pessoa overlooks continuum effects on nonlinear brain network connectivity by eschewing neural field theories and physiologically derived constructs representative of neuronal plasticity. The absence of this content, which is so very important for understanding the dynamic structure-function embedding and partitioning of brains, diminishes the rich competitive and cooperative nature of neural networks and trivializes Pessoa’s arguments, and similar arguments by other authors, on the phylogenetic and operational significance of an optimally integrated brain filled with variable-strength neural connections. Riemannian neuromanifolds, containing limit-imposing metaplastic Hebbian- and antiHebbian-type control variables, simulate scalable network behavior that is difficult to capture from the simpler graph-theoretic analysis preferred by Pessoa and other neuroscientists. Field theories suggest the partitioning and performance benefits of embedded cognitive-emotional networks that optimally evolve between exotic classical and quantum computational phases, where matrix singularities and condensations produce degenerate structure-function homogeneities unrealistic of healthy brains. Some network partitioning, as opposed to unconstrained embeddedness, is thus required for effective execution of cognitive-emotional network functions and, in our new era of neuroscience, should be considered a critical aspect of proper brain organization and operation.

Structural synaptic plasticity has high memory capacity and can explain graded amnesia, catastrophic forgetting, and the spacing effect

Although already William James and, more explicitly, Donald Hebb's theory of cell assemblies have suggested that activity-dependent rewiring of neuronal networks is the substrate of learning and memory, over the last six decades most theoretical work on memory has focused on plasticity of existing synapses in prewired networks. Research in the last decade has emphasized that structural modification of synaptic connectivity is common in the adult brain and tightly correlated with learning and memory. Here we present a parsimonious computational model for learning by structural plasticity. The basic modeling units are "potential synapses" defined as locations in the network where synapses can potentially grow to connect two neurons. This model generalizes well-known previous models for associative learning based on weight plasticity. Therefore, existing theory can be applied to analyze how many memories and how much information structural plasticity can store in a synapse. Surprisingly, we find that structural plasticity largely outperforms weight plasticity and can achieve a much higher storage capacity per synapse. The effect of structural plasticity on the structure of sparsely connected networks is quite intuitive: Structural plasticity increases the "effectual network connectivity", that is, the network wiring that specifically supports storage and recall of the memories. Further, this model of structural plasticity produces gradients of effectual connectivity in the course of learning, thereby explaining various cognitive phenomena including graded amnesia, catastrophic forgetting, and the spacing effect.

Neuropsychological investigation of motor impairments in autism

It is unclear how standardized neuropsychological measures of motor function relate to brain volumes of motor regions in autism spectrum disorder (ASD). An all-male sample composed of 59 ASD and 30 controls (ages 5-33 years) completed three measures of motor function: strength of grip (SOG), finger tapping test (FTT), and grooved pegboard test (GPT). Likewise, all participants underwent magnetic resonance imaging with region of interest (ROI) volumes obtained to include the following regions: motor cortex (precentral gyrus), somatosensory cortex (postcentral gyrus), thalamus, basal ganglia, cerebellum, and caudal middle frontal gyrus. These traditional neuropsychological measures of motor function are assumed to differ in motor complexity, with GPT requiring the most followed by FTT and SOG. Performance by ASD participants on the GPT and FTT differed significantly from that of controls, with the largest effect size differences observed on the more complex GPT task. Differences on the SOG task between the two groups were nonsignificant. Since more complex motor tasks tap more complex networks, poorer GPT performance by those with ASD may reflect less efficient motor networks. There was no gross pathology observed in classic motor areas of the brain in ASD, as ROI volumes did not differ, but FTT was negatively related to motor cortex volume in ASD. The results suggest a hierarchical motor disruption in ASD, with difficulties evident only in more complex tasks as well as a potential anomalous size-function relation in motor cortex in ASD.

Stress and anxiety in schizophrenia and depression: glucocorticoids, corticotropin-releasing hormone and synapse regression

Stress during childhood and adolescence has implications for the extent of depression and psychotic disorders in maturity. Stressful events lead to the regression of synapses with the loss of synaptic spines and in some cases whole dendrites of pyramidal neurons in the prefrontal cortex, a process that leads to the malfunctioning of neural networks in the neocortex. Such stress often shows concomitant increases in the activity of the hypothalamic-pituitary-adrenal system, with a consequent elevated release of glucocorticoids such as cortisol as well as of corticotropin-releasing hormone (CRH) from neurons. It is very likely that it is these hormones, acting on neuronal and astrocyte glucocorticoid receptors (GRs) and CRH receptors, respectively, that are responsible for the regression of synapses. The mechanism of such regression involves the loss of synaptic spines, the stability of which is under the direct control of the activity of N-methyl-d-aspartate (NMDA) receptors on the spines. Glutamate activates NMDA receptors, which then, through parallel pathways, control the extent in the spine of the cytoskeletal protein F-actin and so spine stability and growth. Both GR and CRH receptors in the spines can modulate NMDA receptors, reducing their activation by glutamate and hence spine stability. In contrast, glucocorticoids, probably acting on nerve terminal and astrocyte GRs, can release glutamate, so promoting NMDA receptor activation. It is suggested that spine stability is under dual control by glucocorticoids and CRH, released during stress to change the stability of synaptic spines, leading to the malfunctioning of cortical neural networks that are involved in depression and psychoses.

Receipt of vaginal-cervical stimulation modifies synapsin content in limbic areas of the female rat

Female rats require a sufficient amount and pattern of vaginal-cervical stimulation to initiate neuroendocrine changes required for the successful implantation of a fertilized ovum in the uterus. These changes are characterized by twice daily prolactin surges that last 10-12 days. Following a sterile mating, the endocrine changes are still observed, and are termed pseudopregnancy (PSP). The mating stimulation required to initiate these changes prior to pregnancy or PSP has a neural representation, which we have termed the intromission mnemonic. We sought to examine if the formation of the intromission mnemonic is accompanied by alterations in the number or density of synapses in limbic areas by immuno-labeling a pre-synaptic protein, synapsin. Groups of cycling female rats on proestrus day received either 15 or 5 intromissions or mounts-without intromissions from a vasectomized male; an additional time-matched control group was left in the home cage. All females were perfused after 90 min or 8 h. The brains were removed and sliced, and the amygdala and hippocampus immunostained for synapsin, then imaged by confocal microscopy. We found that 90 min after mating sufficient for PSP, the number of synapsin puncta (points of immunoreactivity equivalent to a synapse) was decreased and the intensity of the synapsin staining was increased in the posterodorsal medial amygdala (MePD). A similar reduction of puncta was observed in the CA1 region of the hippocampus, and an increase of intensity occurred in the basolateral amygdala. Spaced intromissions had no effect on synapsin expression anywhere examined. Intensity reductions unrelated to receipt of vaginal-cervical stimulation were observed in the hippocampus. None of these effects were observed after 8 h. Together, these results raise the possibility that synapses in the MePD may be pruned after mating stimulation, resulting in pathway-specific stabilization that contributes to the intromission mnemonic associated with the establishment of PSP.

Sensitivity to noise in bidirectional associative memory (BAM)

Original Hebbian encoding scheme of bidirectional associative memory (BAM) provides a poor pattern capacity and recall performance. Based on Rosenblatt's perceptron learning algorithm, the pattern capacity of BAM is enlarged, and perfect recall of all training pattern pairs is guaranteed. However, these methods put their emphases on pattern capacity, rather than error correction capability which is another critical point of BAM. This paper analyzes the sensitivity to noise in BAM and obtains an interesting idea to improve noise immunity of BAM. Some researchers have found that the noise sensitivity of BAM relates to the minimum absolute value of net inputs (MAV). However, in this paper, the analysis on failure association shows that it is related not only to MAV but also to the variance of weights associated with synapse connections. In fact, it is a positive monotone increasing function of the quotient of MAV divided by the variance of weights. This idea provides an useful principle of improving error correction capability of BAM. Some revised encoding schemes, such as small variance learning for BAM (SVBAM), evolutionary pseudorelaxation learning for BAM (EPRLAB) and evolutionary bidirectional learning (EBL), have been introduced to illustrate the performance of this principle. All these methods perform better than their original versions in noise immunity. Moreover, these methods have no negative effect on the pattern capacity of BAM. The convergence of these methods is also discussed in this paper. If there exist solutions, EPRLAB and EBL always converge to a global optimal solution in the senses of both pattern capacity and noise immunity. However, the convergence of SVBAM may be affected by a preset function.