Physicalism and Phenomenal Experience

Within this paper a physicalist account of phenomenal experience is presented in a roughly four part process. First, Levine\u27s explanatory gap and Kripke\u27s argument against type-identity physicalism are presented as examples of anti-physicalist arguments to be countered. Kripke\u27s arguments request an explanation for the felt contingency of the statement \u27pain is C-fiber firing.\u27 Levine\u27s explanatory gap is the inability of statements like \u27pain is C-fiber firing\u27 to explain within physicalist theories why C-fiber firing feels like pain. In the second part a physicalist account ofphenomenal experience is presented. This account relies upon a formalization of the mereological structure of events. A relation between events called the \u27observation relation\u27 is introduced and used to formalize observations made in everyday life. In the third step this account of events is used to defeat Kripke\u27s argument and Levine\u27s explanatory gap. Kripke\u27s argument is overcome by providing an explanation for the felt contingency ofthe statement \u27pain is C-fiber firing.\u27 Levine\u27s explanatory gap is defeated by clarifying the question Why do C-fiber firings feel like pain? and showing that asking this question is essentially inappropriate. Thus, the physicalist\u27s inability to explain why C-fiber firings feel like pain is not a failing of physicalism. In the fourth part the physicalist theory of phenomenal experience is compared to some classic views of phenomenal experience from Rosenthal, Nagel, and Dennett

A Tutorial for Information Theory in Neuroscience

Understanding how neural systems integrate, encode, and compute information is central to understanding brain function. Frequently, data from neuroscience experiments are multivariate, the interactions between the variables are nonlinear, and the landscape of hypothesized or possible interactions between variables is extremely broad. Information theory is well suited to address these types of data, as it possesses multivariate analysis tools, it can be applied to many different types of data, it can capture nonlinear interactions, and it does not require assumptions about the structure of the underlying data (i.e., it is model independent). In this article, we walk through the mathematics of information theory along with common logistical problems associated with data type, data binning, data quantity requirements, bias, and significance testing. Next, we analyze models inspired by canonical neuroscience experiments to improve understanding and demonstrate the strengths of information theory analyses. To facilitate the use of information theory analyses, and an understanding of how these analyses are implemented, we also provide a free MATLAB software package that can be applied to a wide range of data from neuroscience experiments, as well as from other fields of study

Being Critical of Criticality in the Brain

Relatively recent work has reported that networks of neurons can produce avalanches of activity whose sizes follow a power law distribution. This suggests that these networks may be operating near a critical point, poised between a phase where activity rapidly dies out and a phase where activity is amplified over time. The hypothesis that the electrical activity of neural networks in the brain is critical is potentially important, as many simulations suggest that information processing functions would be optimized at the critical point. This hypothesis, however, is still controversial. Here we will explain the concept of criticality and review the substantial objections to the criticality hypothesis raised by skeptics. Points and counter points are presented in dialog form

Correction: Maternal deprivation induces alterations in cognitive and cortical function in adulthood

The original version of this Article omitted the author Maureen M. Timm from the Department of Psychology, Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA

Rich-Club Organization in Effective Connectivity among Cortical Neurons.

The performance of complex networks, like the brain, depends on how effectively their elements communicate. Despite the importance of communication, it is virtually unknown how information is transferred in local cortical networks, consisting of hundreds of closely spaced neurons. To address this, it is important to record simultaneously from hundreds of neurons at a spacing that matches typical axonal connection distances, and at a temporal resolution that matches synaptic delays. We used a 512-electrode array (60 μm spacing) to record spontaneous activity at 20 kHz from up to 500 neurons simultaneously in slice cultures of mouse somatosensory cortex for 1 h at a time. We applied a previously validated version of transfer entropy to quantify information transfer. Similar to in vivo reports, we found an approximately lognormal distribution of firing rates. Pairwise information transfer strengths also were nearly lognormally distributed, similar to reports of synaptic strengths. Some neurons transferred and received much more information than others, which is consistent with previous predictions. Neurons with the highest outgoing and incoming information transfer were more strongly connected to each other than chance, thus forming a “rich club.” We found similar results in networks recorded in vivo from rodent cortex, suggesting the generality of these findings. A rich-club structure has been found previously in large-scale human brain networks and is thought to facilitate communication between cortical regions. The discovery of a small, but information-rich, subset of neurons within cortical regions suggests that this population will play a vital role in communication, learning, and memory.SIGNIFICANCE STATEMENT Many studies have focused on communication networks between cortical brain regions. In contrast, very few studies have examined communication networks within a cortical region. This is the first study to combine such a large number of neurons (several hundred at a time) with such high temporal resolution (so we can know the direction of communication between neurons) for mapping networks within cortex. We found that information was not transferred equally through all neurons. Instead, ∼70% of the information passed through only 20% of the neurons. Network models suggest that this highly concentrated pattern of information transfer would be both efficient and robust to damage. Therefore, this work may help in understanding how the cortex processes information and responds to neurodegenerative diseases

High-Degree Neurons Feed Cortical Computations

Recent work has shown that functional connectivity among cortical neurons is highly varied, with a small percentage of neurons having many more connections than others. Also, recent theoretical developments now make it possible to quantify how neurons modify information from the connections they receive. Therefore, it is now possible to investigate how information modification, or computation, depends on the number of connections a neuron receives (in-degree) or sends out (out-degree). To do this, we recorded the simultaneous spiking activity of hundreds of neurons in cortico-hippocampal slice cultures using a high-density 512-electrode array. This preparation and recording method combination produced large numbers of neurons recorded at temporal and spatial resolutions that are not currently available in any in vivo recording system. We utilized transfer entropy (a well-established method for detecting linear and nonlinear interactions in time series) and the partial information decomposition (a powerful, recently developed tool for dissecting multivariate information processing into distinct parts) to quantify computation between neurons where information flows converged. We found that computations did not occur equally in all neurons throughout the networks. Surprisingly, neurons that computed large amounts of information tended to receive connections from high out-degree neurons. However, the in-degree of a neuron was not related to the amount of information it computed. To gain insight into these findings, we developed a simple feedforward network model. We found that a degree-modified Hebbian wiring rule best reproduced the pattern of computation and degree correlation results seen in the real data. Interestingly, this rule also maximized signal propagation in the presence of network-wide correlations, suggesting a mechanism by which cortex could deal with common random background input. These are the first results to show that the extent to which a neuron modifies incoming information streams depends on its topological location in the surrounding functional network

p19( Arf) Suppresses Growth, Progression, and Metastasis of Hras-Driven Carcinomas through p53-Dependent and -Independent Pathways

Ectopic expression of oncogenes such as Ras induces expression of p19(Arf), which, in turn, activates p53 and growth arrest. Here, we used a multistage model of squamous cell carcinoma development to investigate the functional interactions between Ras, p19(Arf), and p53 during tumor progression in the mouse. Skin tumors were induced in wild-type, p19(Arf)-deficient, and p53-deficient mice using the DMBA/TPA two-step protocol. Activating mutations in Hras were detected in all papillomas and carcinomas examined, regardless of genotype. Relative to wild-type mice, the growth rate of papillomas was greater in p19(Arf)-deficient mice, and reduced in p53-deficient mice. Malignant conversion of papillomas to squamous cell carcinomas, as well as metastasis to lymph nodes and lungs, was markedly accelerated in both p19 (Arf)- and p53-deficient mice. Thus, p19(Arf) inhibits the growth rate of tumors in a p53-independent manner. Through its regulation of p53, p19(Arf) also suppresses malignant conversion and metastasis. p53 expression was upregulated in papillomas from wild-type but not p19( Arf)-null mice, and p53 mutations were more frequently seen in wild-type than in p19( Arf)-null carcinomas. This indicates that selection for p53 mutations is a direct result of signaling from the initiating oncogenic lesion, Hras, acting through p19(Arf)

Wet Processing of Granular Nickel for On-Demand Extrusion

On-demand extrusion is a direct-write additive manufacturing process in which paste is extruded through fine nozzles to produce a geometry, layer by layer, using a 3D gantry system. A manufactured paste is suitable for printing if it has an appropriate low shear viscosity, shear thinning behavior, and the particles are properly dispersed. This allows for easy extrusion and prevents agglomeration while maintaining shape retention after extrusion. While suitable nickel pastes for printing have been found, the characterization of nickel during the paste production process and optimization of paste formulation has not been explored. This study examined how the ball milling of granular nickel powder modifies the oxygen content, particle size distribution, and surface area of the starting powder. The effectiveness of ionic and nonionic dispersants were evaluated with rheology for different milling times. Preferred nickel milling times and dispersants were determined and used to make pastes for on-demand extrusion