Quantitative Biology

Neurons in the primate visual cortices show a wide range of stimulus selectivity. Some neurons respond to only a small fraction of stimulus images, whereas others respond to many stimulus images in a non-selective manner. It is unclear how stimulus selective and non-selective neurons contribute to visual object recognition. Herein, we examined the relationship between stimulus selectivity and the effect of deletion of units on task performance using fully a connected layer of two types of deep convolutional neural networks (DCNNs). Deleting a stimulus selective unit caused slight improvements of task performance, whereas deleting stimulus non-selective units caused a significant decrease in task performance. However, these findings do not imply that stimulus selective units have no use for the task. Indeed, better performance was obtained when the networks consisted of both stimulus selective and non-selective units.

Individuals vary in the time needed to produce a spoken word, for example when naming a picture. Similarly, they vary in how much their responses are delayed by interfering stimuli. Several studies have linked this variability to individual differences in inhibition abilities. The present study focuses on the relationship between the semantic interference effect in a picture-word naming task (longer picture-naming latencies in trials with semantically related than unrelated distractor words) and the change in the magnitude of this effect in the tail of the response time distribution. The positive correlation between these two measures, when computed separately for each participant, has been argued to reflect the involvement of selective inhibition in the naming task. We report a series of Bayesian meta-analyses to investigate the reliability of this relationship and to rule out possible alternative explanations. We find that while the relationship is robust, the same relationship is found when we calculate the index of inhibition for individual items, rather than participants. This latter finding challenges the assumption that this measurement reflects individual differences in inhibition ability.

Recent advances in neuroscience data acquisition allow for the simultaneous recording of large populations of neurons across multiple brain areas while subjects perform complex cognitive tasks. Interpreting these data requires us to index how task-relevant information is shared across brain regions, but this is often confounded by the mixing of different task parameters at the single neuron level. Here, inspired by a method developed for a single brain area, we introduce a new technique for demixing variables across multiple brain areas, called demixed shared component analysis (dSCA). dSCA decomposes population activity into a few components, such that the shared components capture the maximum amount of shared information across brain regions while also depending on relevant task parameters. This yields interpretable components that express which variables are shared between different brain regions and when this information is shared across time. To illustrate our method, we reanalyze two datasets recorded during decision-making tasks in rodents and macaques. We find that dSCA provides new insights into the shared computation between different brain areas in these datasets, relating to several different aspects of decision formation.

Neuronal circuits internally regulate electrical signaling via a host of homeostatic mechanisms. Two prominent mechanisms, synaptic scaling and structural plasticity, are believed to maintain average activity within an operating range by modifying the strength and spatial extent of network connectivity using negative feedback. However, both mechanisms operate on relatively slow timescales and thus face fundamental limits due to delays. We show that these mechanisms fulfill complementary roles in maintaining stability in a large network. In particular, even relatively, slow growth dynamics improves performance significantly beyond synaptic scaling alone.

The stiffness of the Hodgkin-Huxley (HH) equations during an action potential (spike) limits the use of large time steps. We observe that the neurons can be evolved independently between spikes, i.e., different neurons can be evolved with different methods and different time steps. This observation motivates us to design fast algorithms to raise efficiency. We present an adaptive method, an exponential time differencing (ETD) method and a library-based method to deal with the stiff period. All the methods can use time steps one order of magnitude larger than the regular Runge-Kutta methods to raise efficiency while achieving precise statistical properties of the original HH neurons like the largest Lyapunov exponent and mean firing rate. We point out that the ETD and library methods can stably achieve maximum 8 and 10 times of speedup, respectively.

Over the past few decades, neuroscience experiments have become increasingly complex and naturalistic. Experimental design has in turn become more challenging, as experiments must conform to an ever-increasing diversity of design constraints. In this article we demonstrate how this design process can be greatly assisted using an optimization tool known as Mixed Integer Linear Programming (MILP). MILP provides a rich framework for incorporating many types of real-world design constraints into a neuroimaging experiment. We introduce the mathematical foundations of MILP, compare MILP to other experimental design techniques, and provide four case studies of how MILP can be used to solve complex experimental design challenges.

Cells respond to biophysical and biochemical signals. We developed a composite filament from collagen and silica particles modified to interact with collagen and/or present a laminin epitope (IKVAV) crucial for cell-matrix adhesion and signal transduction. This combines scaffolding and signaling and shows that local tuning of collagen organization enhances cell differentiation.

Objective: This paper aims to demonstrate functional discriminability among restored hand sensations with different locations, qualities, and intensities that are evoked by microelectrode stimulation of residual afferent fibers in human amputees. Methods: We implanted a Utah Slanted Electrode Array (USEA) in the median and ulnar residual arm nerves of three transradial amputees and delivered stimulation via different electrodes and at different frequencies to produce various locations, qualities, and intensities of sensation on the missing hand. Blind discrimination trials were performed to determine how well subjects could discriminate among these restored sensations. Results: Subjects discriminated among restored sensory percepts with varying cutaneous and proprioceptive locations, qualities, and intensities in blind trials, including discrimination among up to 10 different location-intensity combinations (15/30 successes, p < 0.0005). Variations in the site of stimulation within the nerve, via electrode selection, enabled discrimination among up to 5 locations and qualities (35/35 successes, p < 0.0001). Variations in the stimulation frequency enabled discrimination among 4 different intensities at the same location (13/20 successes, p < 0.005). One subject discriminated among simultaneous, alternating, and isolated stimulation of two different USEA electrodes, as may be desired during multi-sensor closed-loop prosthesis use (20/25 successes, p < 0.001). Conclusion: USEA stimulation enables encoding of a diversity of functionally discriminable sensations with different locations, qualities, and intensities. Significance: These percepts provide a potentially rich source of sensory feedback that may enhance performance and embodiment during multi-sensor, closed-loop prosthesis use.

Here we provide evidence that the fundamental basis of nervous communication is derived from a pressure pulse/soliton capable of computation with sufficient temporal precision to overcome any processing errors. Signalling and computing within the nervous system are complex and different phenomena. Action potentials are plastic and this makes the action potential peak an inappropriate fixed point for neural computation, but the action potential threshold is suitable for this purpose. Furthermore, neural models timed by spiking neurons operate below the rate necessary to overcome processing error. Using retinal processing as our example, we demonstrate that the contemporary theory of nerve conduction based on cable theory is inappropriate to account for the short computational time necessary for the full functioning of the retina and by implication the rest of the brain. Moreover, cable theory cannot be instrumental in the propagation of the action potential because at the activation-threshold there is insufficient charge at the activation site for successive ion channels to be electrostatically opened. Deconstruction of the brain neural network suggests that it is a member of a group of Quantum phase computers of which the Turing machine is the simplest: the brain is another based upon phase ternary computation. However, attempts to use Turing based mechanisms cannot resolve the coding of the retina or the computation of intelligence, as the technology of Turing based computers is fundamentally different. We demonstrate that that coding in the brain neural network is quantum based, where the quanta have a temporal variable and a phase-base variable enabling phase ternary computation as previously demonstrated in the retina.

Neural computation is associated with the emergence, reconfiguration and dissolution of cell assemblies in the context of varying oscillatory states. Here, we describe the complex spatio-temporal dynamics of cell assemblies through temporal network formalism. We use a sliding window approach to extract sequences of networks of information sharing among single units in hippocampus and enthorinal cortex during anesthesia and study how global and node-wise functional connectivity properties evolve along time. First, we find that information sharing networks display, at any time, a core-periphery structure in which an integrated core of more tightly functionally interconnected units link to more loosely connected network leaves. However the units participating to the core or to the periphery substantially change across time-windows. Second, we find that discrete network states can be defined on top of this continuously ongoing liquid core-periphery reorganization. Switching between network states results in a more abrupt modification of the units belonging to the core and is only loosely linked to transitions between global oscillatory states. Third, we characterize different styles of temporal connectivity that cells can exhibit within each state of the sharing network. While inhibitory cells tend to be central, we show that, otherwise, anatomical localization only poorly influences the patterns of temporal connectivity of the different cells. Cells can also change temporal connectivity style when the network changes state. Altogether, these findings reveal that the sharing of information mediated by the intrinsic dynamics of hippocampal and enthorinal cortex cell assemblies have a rich spatiotemporal structure, which could not have been identified by more conventional time- or state-averaged analyses of functional connectivity.