Review: seizure-related consolidation and the network theory of epilepsy.

Epilepsy is a complex, multifaceted disease that affects patients in several ways in addition to seizures, including psychological, social, and quality of life issues, but epilepsy is also known to interact with sleep. Seizures often occur at the boundary between sleep and wake, patients with epilepsy often experience disrupted sleep, and the rate of inter-ictal epileptiform discharges increases during non-REM sleep. The Network Theory of Epilepsy did not address a role for sleep, but recent emphasis on the interaction between epilepsy and sleep suggests that post-seizure sleep may also be involved in the process by which seizures arise and become more severe with time ("epileptogenesis") by co-opting processes related to the formation of long-term memories. While it is generally acknowledged that recurrent seizures arise from the aberrant function of neural circuits, it is possible that the progression of epilepsy is aided by normal, physiological function of neural circuits during sleep that are driven by pathological signals. Studies recording multiple, single neurons prior to spontaneous seizures have shown that neural assemblies activated prior to the start of seizures were reactivated during post-seizure sleep, similar to the reactivation of behavioral neural assemblies, which is thought to be involved in the formation of long-term memories, a process known as Memory Consolidation. The reactivation of seizure-related neural assemblies during sleep was thus described as being a component of Seizure-Related Consolidation (SRC). These results further suggest that SRC may viewed as a network-related aspect of epilepsy, even in those seizures that have anatomically restricted neuroanatomical origins. As suggested by the Network Theory of Epilepsy as a means of interfering with ictogenesis, therapies that interfered with SRC may provide some anti-epileptogenic therapeutic benefit, even if the interference targeted structures that were not involved originally in the seizure. Here, we show how the Network Theory of Epilepsy can be expanded to include neural plasticity mechanisms associated with learning by providing an overview of Memory Consolidation, the mechanisms thought to underlie MC, their relation to Seizure-Related Consolidation, and suggesting novel, anti-epileptogenic therapies targeting interference with network activation in epilepsy following seizures during post-seizure sleep.

Aberrant cortical activity, functional connectivity, and neural assembly architecture after photothrombotic stroke in mice.

Despite substantial progress in mapping the trajectory of network plasticity resulting from focal ischemic stroke, the extent and nature of changes in neuronal excitability and activity within the peri-infarct cortex of mice remains poorly defined. Most of the available data have been acquired from anesthetized animals, acute tissue slices, or infer changes in excitability from immunoassays on extracted tissue, and thus may not reflect cortical activity dynamics in the intact cortex of an awake animal. Here, in vivo two-photon calcium imaging in awake, behaving mice was used to longitudinally track cortical activity, network functional connectivity, and neural assembly architecture for 2 months following photothrombotic stroke targeting the forelimb somatosensory cortex. Sensorimotor recovery was tracked over the weeks following stroke, allowing us to relate network changes to behavior. Our data revealed spatially restricted but long-lasting alterations in somatosensory neural network function and connectivity. Specifically, we demonstrate significant and long-lasting disruptions in neural assembly architecture concurrent with a deficit in functional connectivity between individual neurons. Reductions in neuronal spiking in peri-infarct cortex were transient but predictive of impairment in skilled locomotion measured in the tapered beam task. Notably, altered neural networks were highly localized, with assembly architecture and neural connectivity relatively unaltered a short distance from the peri-infarct cortex, even in regions within 'remapped' forelimb functional representations identified using mesoscale imaging with anaesthetized preparations 8 weeks after stroke. Thus, using longitudinal two-photon microscopy in awake animals, these data show a complex spatiotemporal relationship between peri-infarct neuronal network function and behavioral recovery. Moreover, the data highlight an apparent disconnect between dramatic functional remapping identified using strong sensory stimulation in anaesthetized mice compared to more subtle and spatially restricted changes in individual neuron and local network function in awake mice during stroke recovery.

The impact of early and late blindness on language and verbal working memory: A brain-constrained neural model.

Neural circuits related to language exhibit a remarkable ability to reorganize and adapt in response to visual deprivation. Particularly, early and late blindness induce distinct neuroplastic changes in the visual cortex, repurposing it for language and semantic processing. Interestingly, these functional changes provoke a unique cognitive advantage - enhanced verbal working memory, particularly in early blindness. Yet, the underlying neuromechanisms and the impact on language and memory-related circuits remain not fully understood. Here, we applied a brain-constrained neural network mimicking the structural and functional features of the frontotemporal-occipital cortices, to model conceptual acquisition in early and late blindness. The results revealed differential expansion of conceptual-related neural circuits into deprived visual areas depending on the timing of visual loss, which is most prominent in early blindness. This neural recruitment is fundamentally governed by the biological principles of neural circuit expansion and the absence of uncorrelated sensory input. Critically, the degree of these changes is constrained by the availability of neural matter previously allocated to visual experiences, as in the case of late blindness. Moreover, we shed light on the implication of visual deprivation on the neural underpinnings of verbal working memory, revealing longer reverberatory neural activity in 'blind models' as compared to the sighted ones. These findings provide a better understanding of the interplay between visual deprivations, neuroplasticity, language processing and verbal working memory.

Brain-constrained neural modeling explains fast mapping of words to meaning.

Although teaching animals a few meaningful signs is usually time-consuming, children acquire words easily after only a few exposures, a phenomenon termed "fast-mapping." Meanwhile, most neural network learning algorithms fail to achieve reliable information storage quickly, raising the question of whether a mechanistic explanation of fast-mapping is possible. Here, we applied brain-constrained neural models mimicking fronto-temporal-occipital regions to simulate key features of semantic associative learning. We compared networks (i) with prior encounters with phonological and conceptual knowledge, as claimed by fast-mapping theory, and (ii) without such prior knowledge. Fast-mapping simulations showed word-specific representations to emerge quickly after 1-10 learning events, whereas direct word learning showed word-meaning mappings only after 40-100 events. Furthermore, hub regions appeared to be essential for fast-mapping, and attention facilitated it, but was not strictly necessary. These findings provide a better understanding of the critical mechanisms underlying the human brain's unique ability to acquire new words rapidly.

fmr1 Mutation Alters the Early Development of Sensory Coding and Hunting and Social Behaviors in Larval Zebrafish.

Autism spectrum disorders (ASDs) are developmental in origin; however, little is known about how they affect the early development of behavior and sensory coding. The most common inherited form of autism is Fragile X syndrome (FXS), caused by a mutation in FMR1 Mutation of fmr1 in zebrafish causes anxiety-like behavior, hyperactivity, and hypersensitivity in auditory and visual processing. Here, we show that zebrafish fmr1-/- mutant larvae of either sex also display changes in hunting behavior, tectal coding, and social interaction. During hunting, they were less successful at catching prey and displayed altered behavioral sequences. In the tectum, representations of prey-like stimuli were more diffuse and had higher dimensionality. In a social behavioral assay, they spent more time observing a conspecific but responded more slowly to social cues. However, when given a choice of rearing environment fmr1-/- larvae preferred one with reduced visual stimulation, and rearing them in this environment reduced genotype-specific effects on tectal excitability. Together, these results shed new light on how fmr1-/- changes the early development of neural systems and behavior in a vertebrate.SIGNIFICANCE STATEMENT Autism spectrum disorders (ASDs) are caused by changes in early neural development. Animal models of ASDs offer the opportunity to study these developmental processes in greater detail than in humans. Here, we found that a zebrafish mutant for a gene which in humans causes one type of ASD showed early alterations in hunting behavior, social behavior, and how visual stimuli are represented in the brain. However, we also found that mutant fish preferred reduced visual stimulation, and rearing them in this environment reduced alterations in neural activity patterns. These results suggest interesting new directions for using zebrafish as a model to study the development of brain and behavior in ASDs, and how the impact of ASDs could potentially be reduced.

Learning excitatory-inhibitory neuronal assemblies in recurrent networks.

Understanding the connectivity observed in the brain and how it emerges from local plasticity rules is a grand challenge in modern neuroscience. In the primary visual cortex (V1) of mice, synapses between excitatory pyramidal neurons and inhibitory parvalbumin-expressing (PV) interneurons tend to be stronger for neurons that respond to similar stimulus features, although these neurons are not topographically arranged according to their stimulus preference. The presence of such excitatory-inhibitory (E/I) neuronal assemblies indicates a stimulus-specific form of feedback inhibition. Here, we show that activity-dependent synaptic plasticity on input and output synapses of PV interneurons generates a circuit structure that is consistent with mouse V1. Computational modeling reveals that both forms of plasticity must act in synergy to form the observed E/I assemblies. Once established, these assemblies produce a stimulus-specific competition between pyramidal neurons. Our model suggests that activity-dependent plasticity can refine inhibitory circuits to actively shape cortical computations.

Behavioral Signatures of a Developing Neural Code.

During early life, neural codes must develop to appropriately transform sensory inputs into behavioral outputs. Here, we demonstrate a link between the maturity of neural coding in the visual brain and developmental changes in visually guided behavior. In zebrafish larvae, we show that visually driven hunting behavior improves from 4 to 15 days post-fertilization, becoming faster and more accurate. During the same period, population activity in parts of the optic tectum refines, improving decoding and information transmission for particular spatial positions. Remarkably, individual differences in decoding can predict each fish's hunting success. Together, these results help reveal how the neural codes required for a natural behavior emerge during development.

A Neurobiologically Constrained Cortex Model of Semantic Grounding With Spiking Neurons and Brain-Like Connectivity.

One of the most controversial debates in cognitive neuroscience concerns the cortical locus of semantic knowledge and processing in the human brain. Experimental data revealed the existence of various cortical regions relevant for meaning processing, ranging from semantic hubs generally involved in semantic processing to modality-preferential sensorimotor areas involved in the processing of specific conceptual categories. Why and how the brain uses such complex organization for conceptualization can be investigated using biologically constrained neurocomputational models. Here, we improve pre-existing neurocomputational models of semantics by incorporating spiking neurons and a rich connectivity structure between the model 'areas' to mimic important features of the underlying neural substrate. Semantic learning and symbol grounding in action and perception were simulated by associative learning between co-activated neuron populations in frontal, temporal and occipital areas. As a result of Hebbian learning of the correlation structure of symbol, perception and action information, distributed cell assembly circuits emerged across various cortices of the network. These semantic circuits showed category-specific topographical distributions, reaching into motor and visual areas for action- and visually-related words, respectively. All types of semantic circuits included large numbers of neurons in multimodal connector hub areas, which is explained by cortical connectivity structure and the resultant convergence of phonological and semantic information on these zones. Importantly, these semantic hub areas exhibited some category-specificity, which was less pronounced than that observed in primary and secondary modality-preferential cortices. The present neurocomputational model integrates seemingly divergent experimental results about conceptualization and explains both semantic hubs and category-specific areas as an emergent process causally determined by two major factors: neuroanatomical connectivity structure and correlated neuronal activation during language learning.

Spontaneous Activity in the Zebrafish Tectum Reorganizes over Development and Is Influenced by Visual Experience.

Spontaneous patterns of activity in the developing visual system may play an important role in shaping the brain for function. During the period 4-9 dpf (days post-fertilization), larval zebrafish learn to hunt prey, a behavior that is critically dependent on the optic tectum. However, how spontaneous activity develops in the tectum over this period and the effect of visual experience are unknown. Here we performed two-photon calcium imaging of GCaMP6s zebrafish larvae at all days from 4 to 9 dpf. Using recently developed graph theoretic techniques, we found significant changes in both single-cell and population activity characteristics over development. In particular, we identified days 5-6 as a critical moment in the reorganization of the underlying functional network. Altering visual experience early in development altered the statistics of tectal activity, and dark rearing also caused a long-lasting deficit in the ability to capture prey. Thus, tectal development is shaped by both intrinsic factors and visual experience.

Potential Mechanisms and Functions of Intermittent Neural Synchronization.

Neural synchronization is believed to play an important role in different brain functions. Synchrony in cortical and subcortical circuits is frequently variable in time and not perfect. Few long intervals of desynchronized dynamics may be functionally different from many short desynchronized intervals although the average synchrony may be the same. Recent analysis of imperfect synchrony in different neural systems reported one common feature: neural oscillations may go out of synchrony frequently, but primarily for a short time interval. This study explores potential mechanisms and functional advantages of this short desynchronizations dynamics using computational neuroscience techniques. We show that short desynchronizations are exhibited in coupled neurons if their delayed rectifier potassium current has relatively large values of the voltage-dependent activation time-constant. The delayed activation of potassium current is associated with generation of quickly-rising action potential. This "spikiness" is a very general property of neurons. This may explain why very different neural systems exhibit short desynchronization dynamics. We also show how the distribution of desynchronization durations may be independent of the synchronization strength. Finally, we show that short desynchronization dynamics requires weaker synaptic input to reach a pre-set synchrony level. Thus, this dynamics allows for efficient regulation of synchrony and may promote efficient formation of synchronous neural assemblies.

Extended Neural Metastability in an Embodied Model of Sensorimotor Coupling.

The hypothesis that brain organization is based on mechanisms of metastable synchronization in neural assemblies has been popularized during the last decades of neuroscientific research. Nevertheless, the role of body and environment for understanding the functioning of metastable assemblies is frequently dismissed. The main goal of this paper is to investigate the contribution of sensorimotor coupling to neural and behavioral metastability using a minimal computational model of plastic neural ensembles embedded in a robotic agent in a behavioral preference task. Our hypothesis is that, under some conditions, the metastability of the system is not restricted to the brain but extends to the system composed by the interaction of brain, body and environment. We test this idea, comparing an agent in continuous interaction with its environment in a task demanding behavioral flexibility with an equivalent model from the point of view of "internalist neuroscience." A statistical characterization of our model and tools from information theory allow us to show how (1) the bidirectional coupling between agent and environment brings the system closer to a regime of criticality and triggers the emergence of additional metastable states which are not found in the brain in isolation but extended to the whole system of sensorimotor interaction, (2) the synaptic plasticity of the agent is fundamental to sustain open structures in the neural controller of the agent flexibly engaging and disengaging different behavioral patterns that sustain sensorimotor metastable states, and (3) these extended metastable states emerge when the agent generates an asymmetrical circular loop of causal interaction with its environment, in which the agent responds to variability of the environment at fast timescales while acting over the environment at slow timescales, suggesting the constitution of the agent as an autonomous entity actively modulating its sensorimotor coupling with the world. We conclude with a reflection about how our results contribute in a more general way to current progress in neuroscientific research.

Incremental change in the set of coactive cortical assemblies enables mental continuity.

This opinion article explores how sustained neural firing in association areas allows high-order mental representations to be coactivated over multiple perception-action cycles, permitting sequential mental states to share overlapping content and thus be recursively interrelated. The term "state-spanning coactivity" (SSC) is introduced to refer to neural nodes that remain coactive as a group over a given period of time. SSC ensures that contextual groupings of goal or motor-relevant representations will demonstrate continuous activity over a delay period. It also allows potentially related representations to accumulate and coactivate despite delays between their initial appearances. The nodes that demonstrate SSC are a subset of the active representations from the previous state, and can act as referents to which newly introduced representations of succeeding states relate. Coactive nodes pool their spreading activity, converging on and activating new nodes, adding these to the remaining nodes from the previous state. Thus, the overall distribution of coactive nodes in cortical networks evolves gradually during contextual updating. The term "incremental change in state-spanning coactivity" (icSSC) is introduced to refer to this gradual evolution. Because a number of associated representations can be sustained continuously, each brain state is embedded recursively in the previous state, amounting to an iterative process that can implement learned algorithms to progress toward a complex result. The longer representations are sustained, the more successive mental states can share related content, exhibit progressive qualities, implement complex algorithms, and carry thematic or narrative continuity. Included is a discussion of the implications that SSC and icSSC may have for understanding working memory, defining consciousness, and constructing AI architectures.

Concepts and Relations in Neurally Inspired In Situ Concept-Based Computing.

In situ concept-based computing is based on the notion that conceptual representations in the human brain are "in situ." In this way, they are grounded in perception and action. Examples are neuronal assemblies, whose connection structures develop over time and are distributed over different brain areas. In situ concepts representations cannot be copied or duplicated because that will disrupt their connection structure, and thus the meaning of these concepts. Higher-level cognitive processes, as found in language and reasoning, can be performed with in situ concepts by embedding them in specialized neurally inspired "blackboards." The interactions between the in situ concepts and the blackboards form the basis for in situ concept computing architectures. In these architectures, memory (concepts) and processing are interwoven, in contrast with the separation between memory and processing found in Von Neumann architectures. Because the further development of Von Neumann computing (more, faster, yet power limited) is questionable, in situ concept computing might be an alternative for concept-based computing. In situ concept computing will be illustrated with a recently developed BABI reasoning task. Neurorobotics can play an important role in the development of in situ concept computing because of the development of in situ concept representations derived in scenarios as needed for reasoning tasks. Neurorobotics would also benefit from power limited and in situ concept computing.

Evidence for consolidation of neuronal assemblies after seizures in humans.

The establishment of memories involves reactivation of waking neuronal activity patterns and strengthening of associated neural circuits during slow-wave sleep (SWS), a process known as "cellular consolidation" (Dudai and Morris, 2013). Reactivation of neural activity patterns during waking behaviors that occurs on a timescale of seconds to minutes is thought to constitute memory recall (O'Keefe and Nadel, 1978), whereas consolidation of memory traces may be revealed and served by correlated firing (reactivation) that appears during sleep under conditions suitable for synaptic modification (Buhry et al., 2011). Although reactivation has been observed in human neuronal recordings (Gelbard-Sagiv et al., 2008; Miller et al., 2013), reactivation during sleep has not, likely because data are difficult to obtain and the effect is subtle. Seizures, however, provide intense and synchronous, yet sparse activation (Bower et al., 2012) that could produce a stronger consolidation effect if seizures activate learning-related mechanisms similar to those activated by learned tasks. Continuous wide-bandwidth recordings from patients undergoing intracranial monitoring for drug-resistant epilepsy revealed reactivation of seizure-related neuronal activity during subsequent SWS, but not wakefulness. Those neuronal assemblies that were most strongly activated during seizures showed the largest correlation changes, suggesting that consolidation selectively strengthened neuronal circuits activated by seizures. These results suggest that seizures "hijack" physiological learning mechanisms and also suggest a novel epilepsy therapy targeting neuronal dynamics during post-seizure sleep.