Repeating or spacing learning sessions are strategies for memory improvement with shared molecular and neuronal components.

Intellectual disability is a common feature in genetic disorders with enhanced RAS-ERK1/2 signaling, including neurofibromatosis type 1 (NF1) and Noonan syndrome (NS). Additional training trials and additional spacing between trials, respectively, restores memory deficits in animal models of NF1 and NS. However, the relationship between the underlying mechanisms in these strategies remain obscure. Here, we developed an approach to examine the effect of adding training trials or spacing to a weak training protocol and used genetic and behavioral manipulations in Drosophila to explore such question. We found that repetition and spacing effects are highly related, being equally effective to improve memory in control flies and sharing mechanistic bases, including the requirement of RAS activity in mushroom body neurons and protein synthesis dependence. After spacing or repeating learning trials, memory improvement depends on the formation of long-term memory (LTM). Moreover, a disease-related gain-of-function RasV152G allele impaired LTM. Using minimal training protocols, we established that both learning strategies were also equally effective for memory rescue in the RasV152G mutant and showed non-additive interaction of the spacing and repetition effects. Memory improvement was never detected after Ras inhibition. We conclude that memory improvement by spacing or repeating training trials are two ways of using the same molecular resources, including RAS-ERK1/2-dependent signaling. This evidence supports the concept that learning problems in RAS-related disorders depend on the impaired ability to exploit the repetition and the spacing effect required for long-term memory induction.

The Phosphatase CSW Controls Life Span by Insulin Signaling and Metabolism Throughout Adult Life in Drosophila.

Noonan syndrome and related disorders are caused by mutations in genes encoding for proteins of the RAS-ERK1/2 signaling pathway, which affect development by enhanced ERK1/2 activity. However, the mutations' effects throughout adult life are unclear. In this study, we identify that the protein most commonly affected in Noonan syndrome, the phosphatase SHP2, known in Drosophila as corkscrew (CSW), controls life span, triglyceride levels, and metabolism without affecting ERK signaling pathway. We found that CSW loss-of-function mutations extended life span by interacting with components of the insulin signaling pathway and impairing AKT activity in adult flies. By expressing csw-RNAi in different organs, we determined that CSW extended life span by acting in organs that regulate energy availability, including gut, fat body and neurons. In contrast to that in control animals, loss of CSW leads to reduced homeostasis in metabolic rate during activity. Clinically relevant gain-of-function csw allele reduced life span, when expressed in fat body, but not in other tissues. However, overexpression of a wild-type allele did not affect life span, showing a specific effect of the gain-of-function allele independently of a gene dosage effect. We concluded that CSW normally regulates life span and that mutations in SHP2 are expected to have critical effects throughout life by insulin-dependent mechanisms in addition to the well-known RAS-ERK1/2-dependent developmental alterations.

Seizure localization using pre ictal phase-amplitude coupling in intracranial electroencephalography.

Understanding changes in brain rhythms provides useful information to predict the onset of a seizure and to localize its onset zone in epileptic patients. Brain rhythms dynamics in general, and phase-amplitude coupling in particular, are known to be drastically altered during epileptic seizures. However, the neural processes that take place before a seizure are not well understood. We analysed the phase-amplitude coupling dynamics of stereoelectroencephalography recordings (30 seizures, 5 patients) before and after seizure onset. Electrodes near the seizure onset zone showed higher phase-amplitude coupling. Immediately before the beginning of the seizure, phase-amplitude coupling dropped to values similar to the observed in electrodes far from the seizure onset zone. Thus, our results bring accurate information to detect epileptic events during pre-ictal periods and to delimit the zone of seizure onset in patients undergoing epilepsy surgery.

Ultrasound Axicon: Systematic Approach to Optimize Focusing Resolution through Human Skull Bone.

The use of axicon lenses is useful in many high-resolution-focused ultrasound applications, such as mapping, detection, and have recently been extended to ultrasonic brain therapies. However, in order to achieve high spatial resolution with an axicon lens, it is necessary to adjust the separation, called stand-off (δ), between a conventional transducer and the lens attached to it. Comprehensive ultrasound simulations, using the open-source k-Wave toolbox, were performed for an axicon lens attached to a piezo-disc type transducer with a radius of 14 mm, and a frequency of about 0.5 MHz, that is within the range of optimal frequencies for transcranial transmission. The materials properties were measured, and the lens geometry was modelled. Hydrophone measurements were performed through a human skull phantom. We obtained an initial easygoing design model for the lens angle and optimal stand-off using relatively simple formulas. The skull is not an obstacle for focusing of ultrasound with optimized axicon lenses that achieve an identical resolution to spherical transducers, but with the advantage that the focusing distance is shortened. An adequate stand-off improves the lateral resolution of the acoustic beam by approximately 50%. The approach proposed provides an effective way of designing polydimethylsiloxane (PDMS)-based axicon lenses equipped transducers.

High mutual cooperation rates in rats learning reciprocal altruism: The role of payoff matrix.

Cooperation is one of the most studied paradigms for the understanding of social interactions. Reciprocal altruism -a special type of cooperation that is taught by means of the iterated prisoner dilemma game (iPD)- has been shown to emerge in different species with different success rates. When playing iPD against a reciprocal opponent, the larger theoretical long-term reward is delivered when both players cooperate mutually. In this work, we trained rats in iPD against an opponent playing a Tit for Tat strategy, using a payoff matrix with positive and negative reinforcements, that is food and timeout respectively. We showed for the first time, that experimental rats were able to learn reciprocal altruism with a high average cooperation rate, where the most probable state was mutual cooperation (85%). Although when subjects defected, the most probable behavior was to go back to mutual cooperation. When we modified the matrix by increasing temptation rewards (T) or by increasing cooperation rewards (R), the cooperation rate decreased. In conclusion, we observe that an iPD matrix with large positive reward improves less cooperation than one with small rewards, shown that satisfying the relationship among iPD reinforcement was not enough to achieve high mutual cooperation behavior. Therefore, using positive and negative reinforcements and an appropriate contrast between rewards, rats have cognitive capacity to learn reciprocal altruism. This finding allows to infer that the learning of reciprocal altruism has early appeared in evolution.

Putative dopamine neurons in the ventral tegmental area enhance information coding in the prefrontal cortex.

It has been proposed that neuronal populations in the prefrontal cortex (PFC) robustly encode task-relevant information through an interplay with the ventral tegmental area (VTA). Yet, the precise computation underlying such functional interaction remains elusive. Here, we conducted simultaneous recordings of single-unit activity in PFC and VTA of rats performing a GO/NoGO task. We found that mutual information between stimuli and neural activity increases in the PFC as soon as stimuli are presented. Notably, it is the activity of putative dopamine neurons in the VTA that contributes critically to enhance information coding in the PFC. The higher the activity of these VTA neurons, the better the conditioned stimuli are encoded in the PFC.

A spherical treadmill system to train head-fixed adult rats.

BACKGROUND: While spherical treadmills are widely used in mouse models, there are only a few experimental setups suitable for adult rats, and none of them include head-fixation. NEW METHOD: We introduce a novel spherical treadmill apparatus for head-fixed rats that allows a wide repertory of natural responses. The rat is secured to a frame and placed on a freely rotating sphere. While being head-fixed, it can walk in any direction and perform different motor tasks. COMPARISON WITH EXISTING METHODS: Instead of being air-lifted, which is acceptable for light animals, the treadmill is sustained by three spherical bearings ensuring a smooth rotation in any direction. Movement detection is accomplished using a video camera that registers a dot pattern plotted on the sphere. RESULTS: Long Evans rats were trained to perform an auditory discrimination task in a Go/No-Go (walking/not-walking) paradigm. Animals were able to successfully discriminate between a 1 kHz and a 8 kHz auditory stimulus and execute the correct response, reaching the learning criterion (80% of correct responses) in approximately 20 training sessions. CONCLUSIONS: Our system broadens the possibilities of head-fixation experiments in adult rats making them compatible with spatial navigation on a spherical treadmill.

Cortical response states for enhanced sensory discrimination.

Brain activity during wakefulness is characterized by rapid fluctuations in neuronal responses. Whether these fluctuations play any role in modulating the accuracy of behavioral responses is poorly understood. Here, we investigated whether and how trial changes in the population response impact sensory coding in monkey V1 and perceptual performance. Although the responses of individual neurons varied widely across trials, many cells tended to covary with the local population. When population activity was in a 'low' state, neurons had lower evoked responses and correlated variability, yet higher probability to predict perceptual accuracy. The impact of firing rate fluctuations on network and perceptual accuracy was strongest 200 ms before stimulus presentation, and it greatly diminished when the number of cells used to measure the state of the population was decreased. These findings indicate that enhanced perceptual discrimination occurs when population activity is in a 'silent' response mode in which neurons increase information extraction.

Efficient enhancement of information in the prefrontal cortex during the presence of reward predicting stimuli.

The prefrontal cortex (PFC) is a key brain structure for decision making, behavioural flexibility and working memory. Neurons in PFC encode relevant stimuli through changes in their firing rate, although the metabolic cost of spiking activity puts strong constrains to neural codes based on firing rate modulation. Thus, how PFC neural populations code relevant information in an efficient way is not clearly understood. To address this issue we made single unit recordings in the PFC of rats performing a GO/NOGO discrimination task and analysed how entropy between pairs of neurons changes during cue presentation. We found that entropy rises only during reward-predicting cues. Moreover, this change in entropy occurred along an increase in the efficiency of the whole process. We studied possible mechanisms behind the efficient gain in entropy by means of a two neuron leaky integrate-and-fire model, and found that a precise relationship between synaptic efficacy and firing rate is required to explain the experimentally observed results.

Spontaneous Fluctuations in Visual Cortical Responses Influence Population Coding Accuracy.

Information processing in the cerebral cortex depends not only on the nature of incoming stimuli, but also on the state of neuronal networks at the time of stimulation. That is, the same stimulus will be processed differently depending on the neuronal context in which it is received. A major factor that could influence neuronal context is the background, or ongoing neuronal activity before stimulation. In visual cortex, ongoing activity is known to play a critical role in the development of local circuits, yet whether it influences the coding of visual features in adult cortex is unclear. Here, we investigate whether and how the information encoded by individual neurons and populations in primary visual cortex (V1) depends on the ongoing activity before stimulus presentation. We report that when individual neurons are in a "low" prestimulus state, they have a higher capacity to discriminate stimulus features, such as orientation, despite their reduction in evoked responses. By measuring the distribution of prestimulus activity across a population of neurons, we found that network discrimination accuracy is improved in the low prestimulus state. Thus, the distribution of ongoing activity states across the network creates an "internal context" that dynamically filters incoming stimuli to modulate the accuracy of sensory coding. The modulation of stimulus coding by ongoing activity state is consistent with recurrent network models in which ongoing activity dynamically controls the balanced background excitation and inhibition to individual neurons.

Dopamine modulation of GABAergic function enables network stability and input selectivity for sustaining working memory in a computational model of the prefrontal cortex.

Dopamine modulation of GABAergic transmission in the prefrontal cortex (PFC) is thought to be critical for sustaining cognitive processes such as working memory and decision-making. Here, we developed a neurocomputational model of the PFC that includes physiological features of the facilitatory action of dopamine on fast-spiking interneurons to assess how a GABAergic dysregulation impacts on the prefrontal network stability and working memory. We found that a particular non-linear relationship between dopamine transmission and GABA function is required to enable input selectivity in the PFC for the formation and retention of working memory. Either degradation of the dopamine signal or the GABAergic function is sufficient to elicit hyperexcitability in pyramidal neurons and working memory impairments. The simulations also revealed an inverted U-shape relationship between working memory and dopamine, a function that is maintained even at high levels of GABA degradation. In fact, the working memory deficits resulting from reduced GABAergic transmission can be rescued by increasing dopamine tone and vice versa. We also examined the role of this dopamine-GABA interaction for the termination of working memory and found that the extent of GABAergic excitation needed to reset the PFC network begins to occur when the activity of fast-spiking interneurons surpasses 40 Hz. Together, these results indicate that the capability of the PFC to sustain working memory and network stability depends on a robust interplay of compensatory mechanisms between dopamine tone and the activity of local GABAergic interneurons.

A computational theory for the learning of equivalence relations.

Equivalence relations (ERs) are logical entities that emerge concurrently with the development of language capabilities. In this work we propose a computational model that learns to build ERs by learning simple conditional rules. The model includes visual areas, dopaminergic, and noradrenergic structures as well as prefrontal and motor areas, each of them modeled as a group of continuous valued units that simulate clusters of real neurons. In the model, lateral interaction between neurons of visual structures and top-down modulation of prefrontal/premotor structures over the activity of neurons in visual structures are necessary conditions for learning the paradigm. In terms of the number of neurons and their interaction, we show that a minimal structural complexity is required for learning ERs among conditioned stimuli. Paradoxically, the emergence of the ER drives a reduction in the number of neurons needed to maintain those previously specific stimulus-response learned rules, allowing an efficient use of neuronal resources.