Different population dynamics in the supplementary motor area and motor cortex during reaching

Neural populations perform computations through their collective activity. Different computations likely require different population-level dynamics. We leverage this assumption to examine neural responses recorded from the supplementary motor area (SMA) and motor cortex. During visually guided reaching, the respective roles of these areas remain unclear; neurons in both areas exhibit preparation-related activity and complex patterns of movement-related activity. To explore population dynamics, we employ a novel “hypothesis-guided” dimensionality reduction approach. This approach reveals commonalities but also stark differences: linear population dynamics, dominated by rotations, are prominent in motor cortex but largely absent in SMA. In motor cortex, the observed dynamics produce patterns resembling muscle activity. Conversely, the non-rotational patterns in SMA co-vary with cues regarding when movement should be initiated. Thus, while SMA and motor cortex display superficially similar single-neuron responses during visually guided reaching, their different population dynamics indicate they are likely performing quite different computations

Artificial Brains and Hybrid Minds

The paper develops two related thought experiments exploring variations on an ‘animat’ theme. Animats are hybrid devices with both artificial and biological components. Traditionally, ‘components’ have been construed in concrete terms, as physical parts or constituent material structures. Many fascinating issues arise within this context of hybrid physical organization. However, within the context of functional/computational theories of mentality, demarcations based purely on material structure are unduly narrow. It is abstract functional structure which does the key work in characterizing the respective ‘components’ of thinking systems, while the ‘stuff’ of material implementation is of secondary importance. Thus the paper extends the received animat paradigm, and investigates some intriguing consequences of expanding the conception of bio-machine hybrids to include abstract functional and semantic structure. In particular, the thought experiments consider cases of mind-machine merger where there is no physical Brain-Machine Interface: indeed, the material human body and brain have been removed from the picture altogether. The first experiment illustrates some intrinsic theoretical difficulties in attempting to replicate the human mind in an alternative material medium, while the second reveals some deep conceptual problems in attempting to create a form of truly Artificial General Intelligence

Stability Analysis of Asynchronous States in Neuronal Networks with Conductance-Based Inhibition

Oscillations in networks of inhibitory interneurons have been reported at various sites of the brain and are thought to play a fundamental role in neuronal processing. This Letter provides a self-contained analytical framework that allows numerically efficient calculations of the population activity of a network of conductance-based integrate-and-fire neurons that are coupled through inhibitory synapses. Based on a normalization equation this Letter introduces a novel stability criterion for a network state of asynchronous activity and discusses its perturbations. The analysis shows that, although often neglected, the reversal potential of synaptic inhibition has a strong influence on the stability as well as the frequency of network oscillations

Posterior parietal cortex guides visual decisions in rats

Neurons in putative decision-making structures can reflect both sensory and decision signals, making their causal role in decisions unclear. Here, we tested whether rat posterior parietal cortex (PPC) is causal for processing visual sensory signals or instead for accumulating evidence for decision alternatives. We optogenetically disrupted PPC activity during decision-making and compared effects on decisions guided by auditory vs. visual evidence. Deficits were largely restricted to visual decisions. To further test for visual dominance in PPC, we evaluated electrophysiological responses following individual sensory events and observed much larger response modulation following visual stimuli than auditory stimuli. Finally, we measured trial-to-trial spike count variability during stimulus presentation and decision formation. Variability sharply decreased, suggesting the network is stabilized by inputs, unlike what would be expected if sensory signals were locally accumulated. Our findings argue that PPC plays a causal role in processing visual signals that are accumulated elsewhere.SIGNIFICANCE STATEMENTDefining the neural circuits that support decision-making bridges a gap between our understanding of simple sensorimotor reflexes and our understanding of truly complex behavior. However, identifying brain areas which play a causal role in decision-making has proved challenging. We tested the causal role of a candidate component of decision circuits, the rat posterior parietal cortex (PPC). Our interpretation of the data benefitted from our use of animals trained to make decisions guided by either visual or auditory evidence. Our results argue that PPC plays a causal role specifically in visual decision-making, and that PPC may support sensory aspects of the decision, such as interpreting the visual signals so that evidence for a decision can be accumulated elsewhere

The Explication Defence of Arguments from Reference

In a number of influential papers, Machery, Mallon, Nichols and Stich have presented a powerful critique of so-called arguments from reference, arguments that assume that a particular theory of reference is correct in order to establish a substantive conclusion. The critique is that, due to cross-cultural variation in semantic intuitions supposedly undermining the standard methodology for theorising about reference, the assumption that a theory of reference is correct is unjustified. I argue that the many extant responses to Machery et al.’s critique do little for the proponent of an argument from reference, as they do not show how to justify the problematic assumption. I then argue that it can in principle be justified by an appeal to Carnapian explication. I show how to apply the explication defence to arguments from reference given by Andreasen (for the biological reality of race) and by Churchland (against the existence of beliefs and desires)

Levels of explanation in biological psychology

Until recently, the notions of function and multiple realization were supposed to save the autonomy of psychological explanations. Furthermore, the concept of supervenience presumably allows both dependence of mind on brain and non-reducibility of mind to brain, reconciling materialism with an independent explanatory role for mental and functional concepts and explanations. Eliminativism is often seen as the main or only alternative to such autonomy. It gladly accepts abandoning or thoroughly reconstructing the psychological level, and considers reduction if successful as equivalent with elimination. In comparison with the philosophy of mind, the philosophy of biology has developed more subtle and complex ideas about functions, laws, and reductive explanation than the stark dichotomy of autonomy or elimination. It has been argued that biology is a patchwork of local laws, each with different explanatory interests and more or less limited scope. This points to a pluralistic, domain-specific and multi-level view of explanations in biology. Explanatory pluralism has been proposed as an alternative to eliminativism on the one hand and methodological dualism on the other hand. It holds that theories at different levels of description, like psychology and neuroscience, can co-evolve, and mutually influence each other, without the higher-level theory being replaced by, or reduced to, the lower-level one. Such ideas seem to tally with the pluralistic character of biological explanation. In biological psychology, explanatory pluralism would lead us to expect many local and non-reductive interactions between biological, neurophysiological, psychological and evolutionary explanations of mind and behavior. This idea is illustrated by an example from behavioral genetics, where genetics, physiology and psychology constitute distinct but interrelated levels of explanation. Accounting for such a complex patchwork of related explanations seems to require a more sophisticated and precise way of looking at levels than the existing ideas on (reductive and non-reductive) explanation in the philosophy of mind

The Computational Brain.

Keywords: reductionism, neural networks, distributed coding, Karl Pribram, computational neuroscience, receptive field 1.1 The broad goal of this book, expressed at the start, is ``to understand how neurons give rise to a mental life.'' A mental reductionism is assumed in this seductively simple formulation. Indeed, the book represents reductionism at its best, as the authors guide the reader through the many intermediate levels that link neurons with mental life. In so doing they attack a problem that has persisted for some decades in the neurosciences, since the development of single-cell recording methods. The problem is that millions of neurons participate in every behaviorally meaningful activity, but we normally record from only one neuron at a time, or at best a handful. The temptation is great to overestimate the one-millionth sample obtained from a single neuron, to interpret its activity as detecting a perceptual situation or driving a motor response. This approach, seemingly inescapable in the 1960s, became untenable, but there were no concrete alternatives. Evoked potential techniques gave only a gross average of activity, too vague to pin down mechanisms, and early PDP (parallel distributed processing, or artificial neural network) models were too biologically unrealistic to provide viable interpretations of the single-cell data. Churchland and Sejnowski show how distributed models can now attack this problem, providing significant insights into brain function in a number of domains. 1.2 The book has several parts. First, the authors introduce their approach, combining anatomical, physiological, behavioral and modelling methods in an integrated interdisciplinary attack on specific functional systems. There follows a review of enough anatomy and neurophysiology to make the authors' viewpoint clear and to provide a background for integrating PDP modelling into specific problems in the neurosciences. The heart of the book is a series of chapters reviewing particular models that have been successful in increasing our understanding of the functioning of biological brains. Models of reflex reactions in invertebrates, of locomotion, the vestibulo-ocular reflex in primates

Dataset from "Farzaneh Najafi, Gamaleldin F Elsayed, Robin Cao, Eftychios Pnevmatikakis, Peter E. Latham, John P Cunningham, Anne K Churchland (bioRxiv, 2018); Excitatory and inhibitory subnetworks are equally selective during decision-making and emerge simultaneously during learning.”

This package contains data, in NWB (Neurodata Without Borders) format, from the 4 mice included in "Farzaneh Najafi, Gamaleldin F Elsayed, Robin Cao, Eftychios Pnevmatikakis, Peter E. Latham, John P Cunningham, Anne K Churchland (bioRxiv, 2018); Excitatory and inhibitory subnetworks are equally selective during decision-making and emerge simultaneously during learning.” The "FN_dataSharing/nwb' folder contains NWB files for all recorded sessions for four mice discussed in the paper. Each NWB file represents the data and metadata associated with one recording session. In each NWB file, the metadata related to the session (mouse name, session date/time, lab/institution name, etc.) can be found under "general". Information related to ROI-segmentation such as ROI mask, ROI type (excitatory or inhibitory), poor or good quality, etc. can be found under "modules/Image-Segmentation/pln-seg". Trial information (e.g. start, end times, trial types, trial outcomes, etc.) can be found under "trials". Recorded trial-segmented neuronal responses aligned to different time event (e.g. stimulus start, animal choice, etc.) can be found under "modules/ Trial-based-Segmentation". A jupyter notebook presenting in detail how to work with NWB files is provided at https://github.com/ttngu207/najafi-2018-nwb/blob/master/notebooks/Najafi-2018_example.ipynb

Can we identify non-stationary dynamics of trial-to-trial variability?"

Identifying sources of the apparent variability in non-stationary scenarios is a fundamental problem in many biological data analysis settings. For instance, neurophysiological responses to the same task often vary from each repetition of the same experiment (trial) to the next. The origin and functional role of this observed variability is one of the fundamental questions in neuroscience. The nature of such trial-to-trial dynamics however remains largely elusive to current data analysis approaches. A range of strategies have been proposed in modalities such as electro-encephalography but gaining a fundamental insight into latent sources of trial-to-trial variability in neural recordings is still a major challenge. In this paper, we present a proof-of-concept study to the analysis of trial-to-trial variability dynamics founded on non-autonomous dynamical systems. At this initial stage, we evaluate the capacity of a simple statistic based on the behaviour of trajectories in classification settings, the trajectory coherence, in order to identify trial-to-trial dynamics. First, we derive the conditions leading to observable changes in datasets generated by a compact dynamical system (the Duffing equation). This canonical system plays the role of a ubiquitous model of non-stationary supervised classification problems. Second, we estimate the coherence of class-trajectories in empirically reconstructed space of system states. We show how this analysis can discern variations attributable to non-autonomous deterministic processes from stochastic fluctuations. The analyses are benchmarked using simulated and two different real datasets which have been shown to exhibit attractor dynamics. As an illustrative example, we focused on the analysis of the rat's frontal cortex ensemble dynamics during a decision-making task. Results suggest that, in line with recent hypotheses, rather than internal noise, it is the deterministic trend which most likely underlies the observed trial-to-trial variability. Thus, the empirical tool developed within this study potentially allows us to infer the source of variability in in-vivo neural recordings