Jadin C. Jackson

Quantitative measures of cluster quality for use in extracellular recordings

While the use of multi-channel electrodes (stereotrodes and tetrodes) has allowed for the simultaneous recording and identification of many neurons, quantitative measures of the quality of neurons in such recordings are lacking. In multi-channel recordings, each spike waveform is discriminated in a high-dimensional space, making traditional measures of unit quality inapplicable. We describe two measures of unit isolation quality, Lratio and Isolation Distance, and evaluate their performance using simulations and tetrode recordings. Both measures quantified how well separated the spikes of one cluster (putative neuron) were from other spikes recorded simultaneously on the same multi-channel electrode. In simulations and tetrode recordings, both Lratio and Isolation Distance discriminated well- and poorly-separated clusters. In data sets from the rodent hippocampus in which neurons were simultaneously recorded intracellularly and extracellularly, values of Isolation Distance and Lratio were related to the correct identification of spikes.

Hippocampal Sharp Waves and Reactivation during Awake States Depend on Repeated Sequential Experience

Hippocampal firing patterns during behavior are reactivated during rest and subsequent slow-wave sleep. These reactivations occur during transient local field potential (LFP) events, termed sharp waves. Theories of hippocampal processing suggest that sharp waves arise from strengthened plasticity, and that the strengthened plasticity depends on repeated cofiring of pyramidal cells. We tested these predictions by recording neural ensembles and LFPs from rats running tasks requiring different levels of behavioral repetition. The number of sharp waves emitted increased during sessions with more regular behaviors. Reactivation became more similar to behavioral firing patterns across the session. This enhanced reactivation also depended on the regularity of the behavior. Additional studies in CA3 and CA1 found that the number of sharp waves emitted also increased in CA3 recordings as well as CA1, but that the time courses were different between the two structures.

Reward cues in space: commonalities and differences in neural coding by hippocampal and ventral striatal ensembles.

Forming place-reward associations critically depends on the integrity of the hippocampal–ventral striatal system. The ventral striatum (VS) receives a strong hippocampal input conveying spatial-contextual information, but it is unclear how this structure integrates this information to invigorate reward-directed behavior. Neuronal ensembles in rat hippocampus (HC) and VS were simultaneously recorded during a conditioning task in which navigation depended on path integration. In contrast to HC, ventral striatal neurons showed low spatial selectivity, but rather coded behavioral task phases toward reaching goal sites. Outcome-predicting cues induced a remapping of firing patterns in the HC, consistent with its role in episodic memory. VS remapped in conjunction with the HC, indicating that remapping can take place in multiple brain regions engaged in the same task. Subsets of ventral striatal neurons showed a “flip” from high activity when cue lights were illuminated to low activity in intertrial intervals, or vice versa. The cues induced an increase in spatial information transmission and sparsity in both structures. These effects were paralleled by an enhanced temporal specificity of ensemble coding and a more accurate reconstruction of the animals position from population firing patterns. Altogether, the results reveal strong differences in spatial processing between hippocampal area CA1 and VS, but indicate similarities in how discrete cues impact on this processing.

ALLOSTATIC CONTROL FOR ROBOT BEHAVIOR REGULATION: A COMPARATIVE RODENT-ROBOT STUDY

Rodents are optimal real-world foragers that regulate internal states maintaining a dynamic stability with their surroundings. How these internal drive based behaviors are regulated remains unclear. Based on the physiological notion of allostasis, we investigate a minimal control system able to approximate their behavior. Allostasis is the process of achieving stability with the environment through change, opposed to homeostasis which achieves it through constancy. Following this principle, the so-called allostatic control system orchestrates the interaction of the homeostatic modules by changing their desired values in order to achieve stability. We use a minimal number of subsystems and estimate the model parameters from rat behavioral data in three experimental setups: free exploration, presence of reward, delivery of cues with reward predictive value. From this analysis, we show that a rat is influenced by the shape of the arena in terms of its openness. We then use the estimated model configurations to control a simulated and real robot which captures essential properties of the observed rat behavior. The allostatic reactive control model is proposed as an augmentation of the Distributed Adaptive Control architecture and provides a further contribution towards the realization of an artificial rodent.

Detecting dynamical changes within a simulated neural ensemble using a measure of representational quality.

Technological advances allowing simultaneous recording of neuronal ensembles have led to many developments in our understanding of how the brain performs neural computations. One key technique for extracting information from neural populations has been population reconstruction. While reconstruction is a powerful tool, it only provides a value and gives no indication of the quality of the representation itself. In this paper, we present a mathematically and statistically justified measure for assessing the quality of a representation in a neuronal ensemble. Using a simulated neural network, we show that this measure can distinguish between system states and identify moments of dynamical change within the system. While the examples used in this paper all derive from a standard network model, the measure itself is very general. It requires only a representational space, measured tuning curves, and neural ensembles.

Nanowires precisely grown on the ends of microwire electrodes permit the recording of intracellular action potentials within deeper neural structures.

AIMS Nanoelectrodes are an emerging biomedical technology that can be used to record intracellular membrane potentials from neurons while causing minimal damage during membrane penetration. Current nanoelectrode designs, however, have low aspect ratios or large substrates and thus are not suitable for recording from neurons deep within complex natural structures, such as brain slices. MATERIALS & METHODS We describe a novel nanoelectrode design that uses nanowires grown on the ends of microwire recording electrodes similar to those frequently used in vivo. RESULTS & DISCUSSION We demonstrate that these nanowires can record intracellular action potentials in a rat brain slice preparation and in isolated leech ganglia. CONCLUSION Nanoelectrodes have the potential to revolutionize intracellular recording methods in complex neural tissues, to enable new multielectrode array technologies and, ultimately, to be used to record intracellular signals in vivo.

Information Processing by Neuronal Populations: Measuring distributed properties of neural representations beyond the decoding of local variables: implications for cognition

Neural representations are distributed. This means that more information can be gleaned from neural ensembles than from single cells. Modern recording technology allows the simultaneous recording of large neural ensembles (of more than 100 cells simultaneously) from awake behaving animals. Historically, the principal means of analyzing representations encoded within large ensembles has been to measure the immediate accuracy of the encoding of behavioral variables (”reconstruction”). In this chapter, we will argue that measuring immediate reconstruction only touches the surface of what can be gleaned from these ensembles. We will discuss the implications of distributed representation, in particular, the usefulness of measuring self-consistency of the representation within neural ensembles. Because representations are distributed, neurons in a population can agree or disagree on the value being represented. Measuring the extent to which a firing pattern matches expectations can provide an accurate assessment of the self-consistency of a representation. Dynamic changes in the self-consistency of a representation are potentially indicative of cognitive processes. We will also discuss the implications of representation of non-local (non-immediate) values for cognitive processes. Because cognition occurs at fast time scales, changes must be detectable at fast (ms, tens of ms) timescales.

Perirhinal firing patterns are sustained across large spatial segments of the task environment

Spatial navigation and memory depend on the neural coding of an organisms location. Fine-grained coding of location is thought to depend on the hippocampus. Likewise, animals benefit from knowledge parsing their environment into larger spatial segments, which are relevant for task performance. Here we investigate how such knowledge may be coded, and whether this occurs in structures in the temporal lobe, supplying cortical inputs to the hippocampus. We found that neurons in the perirhinal cortex of rats generate sustained firing patterns that discriminate large segments of the task environment. This contrasted to transient firing in hippocampus and sensory neocortex. These spatially extended patterns were not explained by task variables or temporally discrete sensory stimuli. Previously it has been suggested that the perirhinal cortex is part of a pathway processing object, but not spatial information. Our results indicate a greater complexity of neural coding than captured by this dichotomy.

Large-scale neural modeling in MapReduce and Giraph

One of the most crucial challenges in scientific computing is scalability. Hadoop, an open-source implementation of the MapReduce parallel programming model developed by Google, has emerged as a powerful platform for performing large-scale scientific computing at very low costs. In this paper, we explore the use of Hadoop to model large-scale neural networks. A neural network is most naturally modeled by a graph structure with iterative processing. In this paper, we first present an improved graph algorithm design pattern in MapReduce called Mapper-side Schimmy. Experiments show that the application of our design pattern, combined with the current best practices, can reduce the running time of the neural network simulation on a neural network with 100,000 neurons and 2.3 billion edges by 64%. MapReduce, however, is inherently not efficient for iterative graph processing. To address the limitation of the MapReduce model, we then explore the use of Giraph, an open source large-scale graph processing framework that sits on top of Hadoop to implement graph algorithms with a vertex-centric approach. We show that our Giraph implementation boosted performance by 91% compared to a basic MapReduce implementation and by 60% compared to our improved Mapper-side Schimmy algorithm.

Wireless Galvanic Transmission Through Neural Tissue Via Modulation of a Carrier Signal by A Passive Probe

MARCH 2012, Vol. 6 / 017509-1 Wireless Galvanic transmission through neural tissue via modulation of a carrier signal by a passive probe Andrew E. Papale Graduate Program in Neuroscience, Univ. Minnesota Ryan Mork Dept. Electrical & Computer Engineering, U Minnesota Chris Boldt Department of Neuroscience, Univ. Minnesota Jadin C. Jackson Department of Biology, University of St. Thomas John E. Ferguson Medical Devices Center Fellow, Univ. Minnesota A. David Redish Department of Neuroscience University of Minnesota