Artur Luczak

Formation and Reverberation of Sequential Neural Activity Patterns Evoked by Sensory Stimulation Are Enhanced during Cortical Desynchronization

Memory formation is hypothesized to involve the generation of event-specific neural activity patterns during learning and the subsequent spontaneous reactivation of these patterns. Here, we present evidence that these processes can also be observed in urethane-anesthetized rats and are enhanced by desynchronized brain state evoked by tail pinch, subcortical carbachol infusion, or systemic amphetamine administration. During desynchronization, we found that repeated tactile or auditory stimulation evoked unique sequential patterns of neural firing in somatosensory and auditory cortex and that these patterns then reoccurred during subsequent spontaneous activity, similar to what we have observed in awake animals. Furthermore, the formation of these patterns was blocked by an NMDA receptor antagonist, suggesting that the phenomenon depends on synaptic plasticity. These results suggest that anesthetized animals with a desynchronized brain state could serve as a convenient model for studying stimulus-induced plasticity to improve our understanding of memory formation and replay in the brain.

Default activity patterns at the neocortical microcircuit level.

Even in absence of sensory stimuli cortical networks exhibit complex, self-organized activity patterns. While the function of those spontaneous patterns of activation remains poorly understood, recent studies both in vivo and in vitro have demonstrated that neocortical neurons activate in a surprisingly similar sequential order both spontaneously and following input into cortex. For example, neurons that tend to fire earlier within spontaneous bursts of activity also fire earlier than other neurons in response to sensory stimuli. These “default patterns” can last hundreds of milliseconds and are strongly conserved under a variety of conditions. In this paper, we will review recent evidence for these default patterns at the local cortical level. We speculate that cortical architecture imposes common constraints on spontaneous and evoked activity flow, which result in the similarity of the patterns.

Transcranial Direct Current Stimulation in Stroke Rehabilitation: A Review of Recent Advancements

Transcranial direct current stimulation (tDCS) is a promising technique to treat a wide range of neurological conditions including stroke. The pathological processes following stroke may provide an exemplary system to investigate how tDCS promotes neuronal plasticity and functional recovery. Changes in synaptic function after stroke, such as reduced excitability, formation of aberrant connections, and deregulated plastic modifications, have been postulated to impede recovery from stroke. However, if tDCS could counteract these negative changes by influencing the systems neurophysiology, it would contribute to the formation of functionally meaningful connections and the maintenance of existing pathways. This paper is aimed at providing a review of underlying mechanisms of tDCS and its application to stroke. In addition, to maximize the effectiveness of tDCS in stroke rehabilitation, future research needs to determine the optimal stimulation protocols and parameters. We discuss how stimulation parameters could be optimized based on electrophysiological activity. In particular, we propose that cortical synchrony may represent a biomarker of tDCS efficacy to indicate communication between affected areas. Understanding the mechanisms by which tDCS affects the neural substrate after stroke and finding ways to optimize tDCS for each patient are key to effective rehabilitation approaches.

UP-DOWN cortical dynamics reflect state transitions in a bistable network

In the idling brain, neuronal circuits transition between periods of sustained firing (UP state) and quiescence (DOWN state), a pattern the mechanisms of which remain unclear. Here we analyzed spontaneous cortical population activity from anesthetized rats and found that UP and DOWN durations were highly variable and that population rates showed no significant decay during UP periods. We built a network rate model with excitatory (E) and inhibitory (I) populations exhibiting a novel bistable regime between a quiescent and an inhibition-stabilized state of arbitrarily low rate. Fluctuations triggered state transitions, while adaptation in E cells paradoxically caused a marginal decay of E-rate but a marked decay of I-rate in UP periods, a prediction that we validated experimentally. A spiking network implementation further predicted that DOWN-to-UP transitions must be caused by synchronous high-amplitude events. Our findings provide evidence of bistable cortical networks that exhibit non-rhythmic state transitions when the brain rests.

Beyond the silence: bilateral somatosensory stimulation enhances skilled movement quality and neural density in intact behaving rats.

It is thought that a close dialogue between the primary motor (M1) and somatosensory (S1) cortices is necessary for skilled motor learning. The extent of the relative S1 contribution in producing skilled reaching movements, however, is still unclear. Here we used anodal transcranial direct current stimulation (tDCS), which is able to alter polarity-specific excitability in the S1, to facilitate skilled movement in intact behaving rats. We hypothesized that the critical role of S1 in reaching performance can be enhanced by bilateral tDCS. Pretrained rats were assigned to control or stimulation conditions: (1) UnAno: the unilateral application of an anodal current to the side contralateral to the paw preferred for reaching; (2) BiAno1: bilateral anodal current; (3) BiAno2: a bilateral anodal current with additional 30ms of 65μA pulses every 5s. Rats received tDCS (65μA; 10min/rat) to the S1 during skilled reach training for 20 days (online-effect phase). After-effect assessment occurred for the next ten days in the absence of electrical stimulation. Quantitative and qualitative analyses of online-effects of tDCS showed that UnAno and BiAno1 somatosensory stimulation significantly improve skilled reaching performance. Bilateral BiAno1 stimulation was associated with greater qualitative functional improvement than unilateral UnAno stimulation. tDCS-induced improvements were not observed in the after-effects phase. Quantitative cytoarchitectonic analysis revealed that somatosensory tDCS bilaterally increases cortical neural density. The findings emphasize the central role of bilateral somatosensory feedback in skill acquisition through modulation of cortico-motor excitability.

Measuring Neuronal Branching Patterns Using Model-Based Approach

Neurons have complex branching systems which allow them to communicate with thousands of other neurons. Thus understanding neuronal geometry is clearly important for determining connectivity within the network and how this shapes neuronal function. One of the difficulties in uncovering relationships between neuronal shape and its function is the problem of quantifying complex neuronal geometry. Even by using multiple measures such as: dendritic length, distribution of segments, direction of branches, etc, a description of three dimensional neuronal embedding remains incomplete. To help alleviate this problem, here we propose a new measure, a shape diffusiveness index (SDI), to quantify spatial relations between branches at the local and global scale. It was shown that growth of neuronal trees can be modeled by using diffusion limited aggregation (DLA) process. By measuring “how easy” it is to reproduce the analyzed shape by using the DLA algorithm it can be measured how “diffusive” is that shape. Intuitively, “diffusiveness” measures how tree-like is a given shape. For example shapes like an oak tree will have high values of SDI. This measure is capturing an important feature of dendritic tree geometry, which is difficult to assess with other measures. This approach also presents a paradigm shift from well-defined deterministic measures to model-based measures, which estimate how well a model with specific properties can account for features of analyzed shape.

Creation of a deep convolutional auto-encoder in Caffe

The development of a deep (stacked) convolutional auto-encoder in the Caffe deep learning framework is presented in this paper. We describe simple principles which we used to create this model in Caffe. The proposed model of convolutional auto-encoder does not have pooling/unpooling layers yet. The results of our experimental research show comparable accuracy of dimensionality reduction in comparison with a classic autoencoder on the example of MNIST dataset.

UP-DOWN cortical dynamics reflect state transitions in a bistable balanced network

In the idling brain, neuronal circuits often exhibit transitions between periods of sustained firing (UP state) and quiescence (DOWN state). Although these dynamics occur across multiple areas and behavioral conditions, the underlying mechanisms remain unclear. Here we analyze spontaneous population activity from the somatosensory cortex of urethane-anesthetized rats. We find that UP and DOWN periods are variable (i.e. non-rhythmic) and that the population rate shows no significant decay during UP periods. We build a network model of excitatory (E) and inhibitory (I) neurons that exhibits a new bistability between a quiescent state and a balanced state of arbitrarily low rate. Fluctuating inputs trigger state transitions. Adaptation in E cells paradoxically causes a marginal decay of E-rate but a marked decay of I-rate, a signature of balanced bistability that we validate experimentally. Our findings provide evidence of a bistable balanced network that exhibits non-rhythmic state transitions when the brain rests.

Learning to Predict Consequences as a Method of Knowledge Transfer in Reinforcement Learning

The reinforcement learning (RL) paradigm allows agents to solve tasks through trial-and-error learning. To be capable of efficient, long-term learning, RL agents should be able to apply knowledge gained in the past to new tasks they may encounter in the future. The ability to predict actions’ consequences may facilitate such knowledge transfer. We consider here domains where an RL agent has access to two kinds of information: agent-centric information with constant semantics across tasks, and environment-centric information, which is necessary to solve the task, but with semantics that differ between tasks. For example, in robot navigation, environment-centric information may include the robot’s geographic location, while agent-centric information may include sensor readings of various nearby obstacles. We propose that these situations provide an opportunity for a very natural style of knowledge transfer, in which the agent learns to predict actions’ environmental consequences using agent-centric information. These predictions contain important information about the affordances and dangers present in a novel environment, and can effectively transfer knowledge from agent-centric to environment-centric learning systems. Using several example problems including spatial navigation and network routing, we show that our knowledge transfer approach can allow faster and lower cost learning than existing alternatives.

Chronic mild stress exacerbates severity of experimental autoimmune encephalomyelitis in association with altered non-coding RNA and metabolic biomarkers

The causal factors determining the onset and severity of multiple sclerosis (MS) are not well understood. Here, we investigated the influence of chronic stress on clinical symptoms, metabolic and epigenetic manifestations of experimental autoimmune encephalomyelitis (EAE), a common animal model of MS. Lewis rats were immunized for monophasic EAE with MBP69-88 and were exposed to chronic stress for 37days starting 7days prior to immunization. The exposure to stress accelerated and exacerbated the clinical symptoms of EAE. Both stress and EAE also disrupted metabolic status as indicated by trace elemental analysis in body hair. Stress particularly exacerbated chlorine deposition in EAE animals. Moreover, deep sequencing revealed a considerable impact of stress on microRNA expression in EAE. EAE by itself upregulated microRNA expression in lumbar spinal cord, including miR-21, miR-142-3p, miR-142-5p, miR-146a, and miR-155. Stress in EAE further up-regulated miR-16, miR-146a and miR-155 levels. The latter two microRNAs are recognized biomarkers of human MS. Thus, stress may synergistically exacerbate severity of EAE by altering epigenetic regulatory pathways. The findings suggest that stress may represent a significant risk factor for symptomatic deterioration in MS. Stress-related metabolic and microRNA signatures support their value as biomarkers for predicting the risk and severity of MS.