Electrophysiological Abnormalities in SOD1 Transgenic Models in Amyotrophic Lateral Sclerosis: The Commonalities and Differences

Since its first description in 1874 by Charcot, the hallmark feature of ALS is the progressive degeneration of upper and lower motoneurons (Charcot, 1874). In the spinal cord, motoneuron degeneration starts long before symptom onset and advances in a size-related fashion, in which large-size alpha-motoneurons degenerate first followed by small-size alpha-motoneurons (Pun et al., 2006; Hegedus et al., 2007; Hegedus et al., 2008). There are conflicting reports regarding the survival of the smallest-sized spinal motoneurons, the gamma-motoneurons (Swash and Fox, 1974; Sobue et al., 1981). Despite its original description, the neuronal degeneration in ALS is not limited to motoneurons. Recent reports have shown evidence for degeneration of neurons in the brain (Karim et al., 1998; Lloyd et al., 2000; Maekawa et al., 2004) and interneurons in the spinal cord (Konno et al., 1986; Williams et al., 1990; Takahashi et al., 1993; Stephens et al., 2006). Before their degeneration, spinal motoneurons experience progressive changes in their properties. These changes result from the pathological actions of the disease and the compensatory mechanisms of the nervous system to mitigate the neuronal malfunction. In this chapter, we describe the changes in the anatomical and electrical properties of spinal motoneurons in various genetic mouse models of ALS and critically analyze literature for the common and different pathological features across these models. We also present data from computer simulations showing the consequences of the alterations in properties of mutant motoneurons on cell excitability and dendritic processing of synaptic inputs. The presented computational analysis allowed for the identification of motoneuron alterations undetectable using standard electrophysiological methods. This information is essential for understanding motoneuron pathophysiology in ALS

Suboptimal human inference can invert the bias-variance trade-off for decisions with asymmetric evidence

Solutions to challenging inference problems are often subject to a fundamental trade-off between: 1) bias (being systematically wrong) that is minimized with complex inference strategies, and 2) variance (being oversensitive to uncertain observations) that is minimized with simple inference strategies. However, this trade-off is based on the assumption that the strategies being considered are optimal for their given complexity and thus has unclear relevance to forms of inference based on suboptimal strategies. We examined inference problems applied to rare, asymmetrically available evidence, which a large population of human subjects solved using a diverse set of strategies that varied in form and complexity. In general, subjects using more complex strategies tended to have lower bias and variance, but with a dependence on the form of strategy that reflected an inversion of the classic bias-variance trade-off: subjects who used more complex, but imperfect, Bayesian-like strategies tended to have lower variance but higher bias because of incorrect tuning to latent task features, whereas subjects who used simpler heuristic strategies tended to have higher variance because they operated more directly on the observed samples but lower, near-normative bias. Our results help define new principles that govern individual differences in behavior that depends on rare-event inference and, more generally, about the information-processing trade-offs that can be sensitive to not just the complexity, but also the optimality, of the inference process. &nbsp;</p

Modeling focal epileptic activity in the Wilson-Cowan model with depolarization block

Measurements of neuronal signals during human seizure activity and evoked epileptic activity in experimental models suggest that, in these pathological states, the individual nerve cells experience an activity driven depolarization block, i.e. they saturate. We examined the effect of such a saturation in the Wilson–Cowan formalism by adapting the nonlinear activation function; we substituted the commonly applied sigmoid for a Gaussian function. We discuss experimental recordings during a seizure that support this substitution. Next we perform a bifurcation analysis on the Wilson–Cowan model with a Gaussian activation function. The main effect is an additional stable equilibrium with high excitatory and low inhibitory activity. Analysis of coupled local networks then shows that such high activity can stay localized or spread. Specifically, in a spatial continuum we show a wavefront with inhibition leading followed by excitatory activity. We relate our model simulations to observations of spreading activity during seizures

Learning efficient representations of environmental priors in working memory.

Experience shapes our expectations and helps us learn the structure of the environment. Inference models render such learning as a gradual refinement of the observer's estimate of the environmental prior. For instance, when retaining an estimate of an object's features in working memory, learned priors may bias the estimate in the direction of common feature values. Humans display such biases when retaining color estimates on short time intervals. We propose that these systematic biases emerge from modulation of synaptic connectivity in a neural circuit based on the experienced stimulus history, shaping the persistent and collective neural activity that encodes the stimulus estimate. Resulting neural activity attractors are aligned to common stimulus values. Using recently published human response data from a delayed-estimation task in which stimuli (colors) were drawn from a heterogeneous distribution that did not necessarily correspond with reported population biases, we confirm that most subjects' response distributions are better described by experience-dependent learning models than by models with fixed biases. This work suggests systematic limitations in working memory reflect efficient representations of inferred environmental structure, providing new insights into how humans integrate environmental knowledge into their cognitive strategies

Cross-scale effects of neural interactions during human neocortical seizure activity

Small-scale neuronal networks may impose widespread effects on large network dynamics. To unravel this relationship, we analyzed eight multiscale recordings of spontaneous seizures from four patients with epilepsy. During seizures, multiunit spike activity organizes into a submillimeter-sized wavefront, and this activity correlates significantly with low-frequency rhythms from electrocorticographic recordings across a 10-cm-sized neocortical network. Notably, this correlation effect is specific to the ictal wavefront and is absent interictally or from action potential activity outside the wavefront territory. To examine the multiscale interactions, we created a model using a multiscale, nonlinear system and found evidence for a dual role for feedforward inhibition in seizures: while inhibition at the wavefront fails, allowing seizure propagation, feedforward inhibition of the surrounding centimeter-scale networks is activated via long-range excitatory connections. Bifurcation analysis revealed that distinct dynamical pathways for seizure termination depend on the surrounding inhibition strength. Using our model, we found that the mesoscopic, local wavefront acts as the forcing term of the ictal process, while the macroscopic, centimeter-sized network modulates the oscillatory seizure activity