Graph-Based Models of Cortical Axons for the Prediction of Neuronal Response to Extracellular Electrical Stimulation

Over the past decade, many important insights to brain function have been obtained through clever application of detailed compartmental model neurons. New computing capabilities brought opportunities to study large networks of model neurons. Certain applications for these models, such as extracellular electrical stimulation, demand a very high degree of biological realism. While dendrites and somatic morphology may be obtained from explicit reconstructions, this approach is less useful for axonal structures, which are more difficult to characterize across a neuronal population. The purpose of this paper is to extend neuronal morphology generative models to highly branched axon terminal arbors as well as to present a clear use-case for such models in the study of cortical tissue response to externally applied electric fields. The results of this work are (i) presentation and quantitative/qualitative description of generated fibers and (ii) an extracellular electrical stimulation strength-duration study.

ASIC Implementation of a Nonlinear Dynamical Model for Hippocampal Prosthesis

A hippocampal prosthesis is a very large scale integration (VLSI) biochip that needs to be implanted in the biological brain to solve a cognitive dysfunction. In this letter, we propose a novel low-complexity, small-area, and low-power programmable hippocampal neural network application-specific integrated circuit (ASIC) for a hippocampal prosthesis. It is based on the nonlinear dynamical model of the hippocampus: namely multi-input, multi-output (MIMO)-generalized Laguerre-Volterra model (GLVM). It can realize the real-time prediction of hippocampal neural activity. New hardware architecture, a storage space configuration scheme, low-power convolution, and gaussian random number generator modules are proposed. The ASIC is fabricated in 40 nm technology with a core area of 0.122 mm[Formula: see text] and test power of 84.4 [Formula: see text]W. Compared with the design based on the traditional architecture, experimental results show that the core area of the chip is reduced by 84.94% and the core power is reduced by 24.30%.

Developing a hippocampal neural prosthetic to facilitate human memory encoding and recall

OBJECTIVE

We demonstrate here the first successful implementation in humans of a proof-of-concept system for restoring and improving memory function via facilitation of memory encoding using the patient's own hippocampal spatiotemporal neural codes for memory. Memory in humans is subject to disruption by drugs, disease and brain injury, yet previous attempts to restore or rescue memory function in humans typically involved only nonspecific, modulation of brain areas and neural systems related to memory retrieval.

APPROACH

We have constructed a model of processes by which the hippocampus encodes memory items via spatiotemporal firing of neural ensembles that underlie the successful encoding of short-term memory. A nonlinear multi-input, multi-output (MIMO) model of hippocampal CA3 and CA1 neural firing is computed that predicts activation patterns of CA1 neurons during the encoding (sample) phase of a delayed match-to-sample (DMS) human short-term memory task.

MAIN RESULTS

MIMO model-derived electrical stimulation delivered to the same CA1 locations during the sample phase of DMS trials facilitated short-term/working memory by 37% during the task. Longer term memory retention was also tested in the same human subjects with a delayed recognition (DR) task that utilized images from the DMS task, along with images that were not from the task. Across the subjects, the stimulated trials exhibited significant improvement (35%) in both short-term and long-term retention of visual information.

SIGNIFICANCE

These results demonstrate the facilitation of memory encoding which is an important feature for the construction of an implantable neural prosthetic to improve human memory.

Designing Patient-Specific Optimal Neurostimulation Patterns for Seizure Suppression

Neurostimulation is a promising therapy for abating epileptic seizures. However, it is extremely difficult to identify optimal stimulation patterns experimentally. In this study, human recordings are used to develop a functional 24 neuron network statistical model of hippocampal connectivity and dynamics. Spontaneous seizure-like activity is induced in silico in this reconstructed neuronal network. The network is then used as a testbed to design and validate a wide range of neurostimulation patterns. Commonly used periodic trains were not able to permanently abate seizures at any frequency. A simulated annealing global optimization algorithm was then used to identify an optimal stimulation pattern, which successfully abated 92% of seizures. Finally, in a fully responsive, or closed-loop, neurostimulation paradigm, the optimal stimulation successfully prevented the network from entering the seizure state. We propose that the framework presented here for algorithmically identifying patient-specific neurostimulation patterns can greatly increase the efficacy of neurostimulation devices for seizures.

Model-Based Analysis of Electrode Placement and Pulse Amplitude for Hippocampal Stimulation

Objective:

The ideal form of a neural-interfacing device is highly dependent upon the anatomy of the region with which it is meant to interface. Multiple-electrode arrays provide a system which can be adapted to various neural geometries. Computational models of stimulating systems have proven useful for evaluating electrode placement and stimulation protocols, but have yet to be adequately adapted to the unique features of the hippocampus.

Methods:

As an approach to understanding potential memory restorative devices, an Admittance Method-NEURON model was constructed to predict the direct and synaptic response of a region of the rat dentate gyrus to electrical stimulation of the perforant path.

Results:

A validation of estimated local field potentials against experimental recordings is performed and results of a bi-linear electrode placement and stimulation amplitude parameter search are presented.

Conclusion:

The parametric analysis presented herein suggests that stimulating electrodes placed between the lateral and medial perforant path, near the crest of the dentate gyrus, yield a larger relative population response to given stimuli.

Significance:

Beyond deepening understanding of the hippocampal tissue system, establishment of this model provides a method to evaluate candidate stimulating devices and protocols.

Evolving Applications, Technological Challenges and Future Opportunities in Neuromodulation: Proceedings of the Fifth Annual Deep Brain Stimulation Think Tank

The annual Deep Brain Stimulation (DBS) Think Tank provides a focal opportunity for a multidisciplinary ensemble of experts in the field of neuromodulation to discuss advancements and forthcoming opportunities and challenges in the field. The proceedings of the fifth Think Tank summarize progress in neuromodulation neurotechnology and techniques for the treatment of a range of neuropsychiatric conditions including Parkinson's disease, dystonia, essential tremor, Tourette syndrome, obsessive compulsive disorder, epilepsy and cognitive, and motor disorders. Each section of this overview of the meeting provides insight to the critical elements of discussion, current challenges, and identified future directions of scientific and technological development and application. The report addresses key issues in developing, and emphasizes major innovations that have occurred during the past year. Specifically, this year's meeting focused on technical developments in DBS, design considerations for DBS electrodes, improved sensors, neuronal signal processing, advancements in development and uses of responsive DBS (closed-loop systems), updates on National Institutes of Health and DARPA DBS programs of the BRAIN initiative, and neuroethical and policy issues arising in and from DBS research and applications in practice.

Multi-resolution multi-trial sparse classification model for decoding visual memories from hippocampal spikes in human

To understand how memories are encoded in the hippocampus, we build memory decoding models to classify visual memories based on hippocampal activities in human. Model inputs are spatio-temporal patterns of spikes recorded in the hippocampal CA3 and CA1 regions of epilepsy patients performing a delayed match-to-sample (DMS) task. Model outputs are binary labels indicating categories and features of sample images. To solve the super high-dimensional estimation problem with short data length, we develop a multi-trial, sparse model estimation method utilizing B-spline basis functions with a large range of temporal resolutions and a regularized logistic classifier. Results show that this model can effectively avoid overfitting and provide significant amount of prediction to memory categories and features using very limited number of data points. Stable estimation of sparse classification function matrices for each label can be obtained with this multi-resolution, multi-trial procedure. These classification models can be used not only to predict memory contents, but also to design optimal spatio-temporal patterns for eliciting specific memories in the hippocampus, and thus have important implications to the development of hippocampal memory prostheses.

Mechanism-Based and Input-Output Modeling of the Key Neuronal Connections and Signal Transformations in the CA3-CA1 Regions of the Hippocampus

This letter examines the results of input-output (nonparametric) modeling based on the analysis of data generated by a mechanism-based (parametric) model of CA3-CA1 neuronal connections in the hippocampus. The motivation is to obtain biological insight into the interpretation of such input-output (Volterra-equivalent) models estimated from synthetic data. The insights obtained may be subsequently used to interpretat input-output models extracted from actual experimental data. Specifically, we found that a simplified parametric model may serve as a useful tool to study the signal transformations in the hippocampal CA3-CA1 regions. Input-output modeling of model-based synthetic data show that GABAergic interneurons are responsible for regulating neuronal excitation, controlling the precision of spike timing, and maintaining network oscillations, in a manner consistent with previous studies. The input-output model obtained from real data exhibits intriguing similarities with its synthetic-data counterpart, demonstrating the importance of a dynamic resonance in the system/model response around 2 Hz to 3 Hz. Using the input-output model from real data as a guide, we may be able to amend the parametric model by incorporating more mechanisms in order to yield better-matching input-output model. The approach we present can also be applied to the study of other neural systems and pathways.

Place field detection using grid-based clustering in a large-scale computational model of the rat dentate gyrus

Place cells are neurons in the hippocampus that are sensitive to location within an environment. Simulations of a large-scale, computational model of the rat dentate gyrus using grid cell input have been performed resulting in granule cells that express multiple place fields. The typical method of detecting place fields using a global threshold on this data is unreliable as the characteristics of the place fields from a single neuron can be highly variable. A grid-based implementation of DENCLUE has been developed to calculate local thresholds to identify each place field. An adaptive binning algorithm used to smooth the rate maps was combined with the DENCLUE implementation to adaptively choose the size of the smoothing kernel and reduce the number of free parameters of the total algorithm. A sensitivity analysis was performed using the threshold parameter to demonstrate the robustness of using local thresholds as opposed to using a single global threshold in detecting the place fields resulting from the large-scale simulation. The analysis supports the use of applying local thresholds for place field detection and will be used to further investigate the role of granule cells in hippocampal function.

Development of a detailed model of calcium dynamics at the postsynaptic spine of an excitatory synapse

Postsynaptic calcium dynamics play a critical role in synaptic plasticity, but are often difficult to measure in experimental protocols due to their relatively fast rise and decay times, and the small spine dimensions. To circumvent these limitations, we propose to develop a computational model of calcium dynamics in the postsynaptic spine. This model integrates the main elements that participate in calcium concentration influx, efflux, diffusion and buffering. These consist of (i) spine geometry; (ii) calcium influx through NMDA receptors and voltage-dependent calcium channels (VDCC); (iii) calcium efflux with plasma membrane calcium pumps (PMCA) and sodium-calcium exchangers (NCX); (iv) intracellular calcium stores; and (v) calcium buffers. We herein present computational results we obtained and compare them with experimentally measured data, thereby validating the proposed model. Overall the development of such postsynaptic calcium model may help us better understand the intricacies of interplay between the different elements that shape calcium dynamics and impact synaptic plasticity in normal functions and pathologies. This model also constitutes a first step in the development of a nonlinear input-output calcium dynamics model for multi-scale, large scale neuronal simulations.

A bi-directional communication paradigm between parallel NEURON and an external non-neuron process

In order to accurately model the pattern of activation due to electrical stimulation of the hippocampus, a multi-scale computational approach is necessary. At the system level, the Admittance Method (ADM) is used to calculate the extracellular voltages created by a stimulating electrode. At the network and cellular levels, a large-scale multi-compartmental neuron network is used to calculate cellular activation. This paper presents a bi-directional communication paradigm between the NEURON model and an external surrogate for the ADM solver, where at each time step, neurons share their membrane currents with the external process, and the external process shares calculated extracellular voltages with the neuronal network. This work constitutes an important first step towards a full multi-scale NEURON-ADM model with bi-directional communication.

Decoding memory features from hippocampal spiking activities using sparse classification models

To understand how memory information is encoded in the hippocampus, we build classification models to decode memory features from hippocampal CA3 and CA1 spatio-temporal patterns of spikes recorded from epilepsy patients performing a memory-dependent delayed match-to-sample task. The classification model consists of a set of B-spline basis functions for extracting memory features from the spike patterns, and a sparse logistic regression classifier for generating binary categorical output of memory features. Results show that classification models can extract significant amount of memory information with respects to types of memory tasks and categories of sample images used in the task, despite the high level of variability in prediction accuracy due to the small sample size. These results support the hypothesis that memories are encoded in the hippocampal activities and have important implication to the development of hippocampal memory prostheses.

Monte Carlo validation of spike-timing-dependent plasticity identification from spiking activity

As increasingly complex non-stationary models become possible to be identified from neural data, rigorous validation approaches must be developed to rule out overfitting and the potential to identify features by chance. Specifically, identification of spike-timing-dependent plasticity (STDP) from recorded spontaneous in vivo spike timing is a potentially powerful tool to quantify activity-dependent plasticity. In previous work, we presented a methodology to perform this STDP identification from spike timing alone and successfully identified a generative model. Validation was straightforward with the generative model because the underlying model was known, but becomes challenging when applied to experimental data. Here, we introduce a set of null hypothesis tests that can be performed with Monte Carlo (MC) simulations of null models to rule out cases of overfitting with experimental data. We demonstrate the identification of these null models and null hypothesis testing on a generative model in two test cases, one with and one without overfitting. Importantly, we show that it is possible to distinguish an identified STDP rule from a null case where there are similar weight fluctuations which are activity-independent. With the development of the null hypothesis tests described here, STDP identification can be effectively applied to experimental data recordings.

Sources of errors and uncertainties in the assessment of forest soil carbon stocks at different scales-review and recommendations

Spatially explicit knowledge of recent and past soil organic carbon (SOC) stocks in forests will improve our understanding of the effect of human- and non-human-induced changes on forest C fluxes. For SOC accounting, a minimum detectable difference must be defined in order to adequately determine temporal changes and spatial differences in SOC. This requires sufficiently detailed data to predict SOC stocks at appropriate scales within the required accuracy so that only significant changes are accounted for. When designing sampling campaigns, taking into account factors influencing SOC spatial and temporal distribution (such as soil type, topography, climate and vegetation) are needed to optimise sampling depths and numbers of samples, thereby ensuring that samples accurately reflect the distribution of SOC at a site. Furthermore, the appropriate scales related to the research question need to be defined: profile, plot, forests, catchment, national or wider. Scaling up SOC stocks from point sample to landscape unit is challenging, and thus requires reliable baseline data. Knowledge of the associated uncertainties related to SOC measures at each particular scale and how to reduce them is crucial for assessing SOC stocks with the highest possible accuracy at each scale. This review identifies where potential sources of errors and uncertainties related to forest SOC stock estimation occur at five different scales-sample, profile, plot, landscape/regional and European. Recommendations are also provided on how to reduce forest SOC uncertainties and increase efficiency of SOC assessment at each scale.

A million-plus neuron model of the hippocampal dentate gyrus: Dependency of spatio-temporal network dynamics on topography

This paper describes a million-plus granule cell compartmental model of the rat hippocampal dentate gyrus, including excitatory, perforant path input from the entorhinal cortex, and feedforward and feedback inhibitory input from dentate interneurons. The model includes experimentally determined morphological and biophysical properties of granule cells, together with glutamatergic AMPA-like EPSP and GABAergic GABAA-like IPSP synaptic excitatory and inhibitory inputs, respectively. Each granule cell was composed of approximately 200 compartments having passive and active conductances distributed throughout the somatic and dendritic regions. Modeling excitatory input from the entorhinal cortex was guided by axonal transport studies documenting the topographical organization of projections from subregions of the medial and lateral entorhinal cortex, plus other important details of the distribution of glutamatergic inputs to the dentate gyrus. Results showed that when medial and lateral entorhinal cortical neurons maintained Poisson random firing, dentate granule cells expressed, throughout the million-cell network, a robust, non-random pattern of spiking best described as spatiotemporal "clustering". To identify the network property or properties responsible for generating such firing "clusters", we progressively eliminated from the model key mechanisms such as feedforward and feedback inhibition, intrinsic membrane properties underlying rhythmic burst firing, and/or topographical organization of entorhinal afferents. Findings conclusively identified topographical organization of inputs as the key element responsible for generating a spatio-temporal distribution of clustered firing. These results uncover a functional organization of perforant path afferents to the dentate gyrus not previously recognized: topography-dependent clusters of granule cell activity as "functional units" that organize the processing of entorhinal signals.

Design of a flexible parylene-based multi-electrode array for multi-region recording from the rat hippocampus

The hippocampus is a critical deep brain structure in several aspects. It is directly related to the formation of new long-term declarative memory. The malfunction of the hippocampus closely relates to various disease and pathological conditions. It is also a model structure for the study of cortical function and synaptic plasticity in general because of its special neuro-anatomical structure and intrinsic connections within the hippocampus formation. Both the understanding of roles that the hippocampus plays in recognition memory and the study of neural plasticity require simultaneously recording of neural activities from multiple sub-regions of the hippocampus from behavioral animals. However the distribution of cells in the hippocampus make the recording from multiple sub-regions a big challenge with the traditional uni-length micro-wire arrays. Well-designed electrode arrays are required to reach multiple regions simultaneously because of the distinctive double C shape of the hippocampus cell body layers. In this work, we designed a multi-shanks electrode which uses Parylene C, a highly biocompatible and flexible polymer, as a base and has multiple recording sites specially positioned along the longitudinal axis to fit the curvy shape of the rat hippocampus.

Sparse Large-Scale Nonlinear Dynamical Modeling of Human Hippocampus for Memory Prostheses

In order to build hippocampal prostheses for restoring memory functions, we build sparse multi-input, multi-output (MIMO) nonlinear dynamical models of the human hippocampus. Spike trains are recorded from hippocampal CA3 and CA1 regions of epileptic patients performing a variety of memory-dependent delayed match-to-sample (DMS) tasks. Using CA3 and CA1 spike trains as inputs and outputs respectively, sparse generalized Laguerre-Volterra models are estimated with group lasso and local coordinate descent methods to capture the nonlinear dynamics underlying the CA3-CA1 spike train transformations. These models can accurately predict the CA1 spike trains based on the ongoing CA3 spike trains during multiple memory events, e.g., sample presentation, sample response, match presentation and match response, of the DMS task, and thus will serve as the computational basis of human hippocampal memory prostheses.

Identification of Stable Spike-Timing-Dependent Plasticity from Spiking Activity with Generalized Multilinear Modeling

Characterization of long-term activity-dependent plasticity from behaviorally driven spiking activity is important for understanding the underlying mechanisms of learning and memory. In this letter, we present a computational framework for quantifying spike-timing-dependent plasticity (STDP) during behavior by identifying a functional plasticity rule solely from spiking activity. First, we formulate a flexible point-process spiking neuron model structure with STDP, which includes functions that characterize the stationary and plastic properties of the neuron. The STDP model includes a novel function for prolonged plasticity induction, as well as a more typical function for synaptic weight change based on the relative timing of input-output spike pairs. Consideration for system stability is incorporated with weight-dependent synaptic modification. Next, we formalize an estimation technique using a generalized multilinear model (GMLM) structure with basis function expansion. The weight-dependent synaptic modification adds a nonlinearity to the model, which is addressed with an iterative unconstrained optimization approach. Finally, we demonstrate successful model estimation on simulated spiking data and show that all model functions can be estimated accurately with this method across a variety of simulation parameters, such as number of inputs, output firing rate, input firing type, and simulation time. Since this approach requires only naturally generated spikes, it can be readily applied to behaving animal studies to characterize the underlying mechanisms of learning and memory.

A large-scale detailed neuronal model of electrical stimulation of the dentate gyrus and perforant path as a platform for electrode design and optimization

Owing to the dramatic rise in treatment of neurological disorders with electrical micro-stimulation it has become apparent that the major technological limitation in deploying effective devices lies in the process of designing efficient, safe, and outcome specific electrode arrays. The time-consuming and low-fidelity nature of gathering test data using experimental means and the immense control and flexibility of computational models, has prompted us and others to build models of electrical stimulation of neural networks that can be simulated in a computer. Because prior work has been focused on single cells, very small networks, or non-biological models of neural tissue, it was expedient that we take advantage of our, 4,040 processor, computing cluster to construct a large-scale 3-dimensional emulation of hippocampal tissue using detailed neuronal models with explicit and unique morphologies. This model, when paired with an equivalent circuit method of estimating voltage signal attenuation throughout anisotropic resistive tissue, can be used to predict tissue response to an exhaustive set of stimulation and tissue conditions: electrode geometry, array geometry, static dielectric properties of tissue, stimulation pulse features, etc. Preliminary experiments demonstrate that this system is capable of yielding neuronal responses with striking similarities to experimental results. This work provides an avenue to qualitative evaluation of electrode arrays, and more meaningful modeling of local field potentials in terms of their contributing sources and sinks.

A flexible parylene probe for in vivo recordings from multiple subregions of the rat hippocampus

The hippocampus is crucial to the formation of long-term memory and declarative memory. It is divided into three sub-fields the CA1, the CA3 and the DG. To understand the neuronal circuitry within the hippocampus and to study the role of the hippocampus in memory function requires the collection of neural activities from multiple subregions of the hippocampus simultaneously. Micro-wire electrode arrays are commonly used as an interface with neural systems. However, recording from multiple deep brain regions with curved anatomical structures such as the thin cell body layers of the hippocampus requires the micro-wires to be arranged into a highly accurate, complex layout that is difficult to fabricated manually. In this work, we designed and developed a flexible parylene-C based neural probe which can be easily micro-machined to the desired dimensions. Sixty-four electrical recording sites are micromachined on to 8 parylene shanks and spaced according to the distribution of hippocampal principal neurons in different hippocampus subregions. Together with our collaborators, we developed and optimized the implantation procedure of the flexible parylene probe and tested the insertion method both in brain tissue phantom and in vivo with a sham device. Immunohistochemistry (IHC) staining post-implantation of the sham probe was used to verify the location of the probe and to evaluate immune responses to the probe. Fully functional devices were fabricated and, in future studies, functional probes will be chronically implanted into the rat hippocampus, and neural activities will be recorded and compared with signals obtained with micro-wire arrays.

A sparse Laguerre-Volterra autoregressive model for seizure prediction in temporal lobe epilepsy

A sparse Laguerre-Volterra autoregressive model has been developed as feature extraction from subdural human EEG data for seizure prediction in temporal lobe epilepsy. The use of Laguerre-Volterra kernel can compactly yield an autoregressive model of longer system memory without increasing the number of the coefficients. In 6 sets of seizure, we used a sparse Laguerre-Volterra autoregressive model with 6 coefficients and the decay parameter of 0.2 and obtained the 10-fold cross-validation prediction results of high Matthews correlation coefficients (0.7-1) and low prediction errors (<;15%). These results demonstrate that the sparse Laguerre-Volterra autoregressive model is effective in the feature extraction for seizure prediction. Finally, this sparse Laguerre-Volterra method can be easily adapted to a potentially more powerful nonlinear autoregressive model as the feature extraction rather than linear autoregressive model that we are currently using.

Estrogen and Neural Plasticity

Converging clinical evidence suggests that postmenopausal estrogen therapy in women is associated with improved cognition and a reduced incidence of Alzheimer's disease. In experimental work, investigators have found estrogen to promote changes in synaptic plasticity within the nervous system. In this article, we review both the clinical and the experimental literature, and consider mechanisms of action of estrogen on neurons and synaptic plasticity, and how they might protect against the cognitive impairments of old age.

Implementing spiking neuron model and spike-timing-dependent plasticity with generalized Laguerre-Volterra models

To perform large-scale simulations of the brain or build biologically-inspired cognitive architectures, it is essential to have a succinct and flexible model of spiking neurons. The model should be able to capture the nonlinear dynamical properties of various types of neurons and the nonstationary properties such as the spike-timing-dependent plasticity (STDP). In this paper, we propose a generalized Laguerre-Volterra modeling approach for such a task. Due to its built-in nonlinear dynamical terms, the generalized Laguerre-Volterra model (GLVM) can capture various biological processes/mechanisms. Using Laguerre expansion of Volterra kernel technique, the model is fully represented with a small set of coefficients. The calculation of the model variables can be expressed recursively based on only the current and the one-step-before values and thus can be performed efficiently. In addition, we show that, using the same methodology, STDP can be implemented as a specific form of second-order Volterra kernel describing the causal relationship between pairs of input-output spikes and the changes of the feedforward kernels in the GLVMs.

A cognitive prosthesis for memory facilitation by closed-loop functional ensemble stimulation of hippocampal neurons in primate brain

Very productive collaborative investigations characterized how multineuron hippocampal ensembles recorded in nonhuman primates (NHPs) encode short-term memory necessary for successful performance in a delayed match to sample (DMS) task and utilized that information to devise a unique nonlinear multi-input multi-output (MIMO) memory prosthesis device to enhance short-term memory in real-time during task performance. Investigations have characterized how the hippocampus in primate brain encodes information in a multi-item, rule-controlled, delayed match to sample (DMS) task. The MIMO model was applied via closed loop feedback micro-current stimulation during the task via conformal electrode arrays and enhanced performance of the complex memory requirements. These findings clearly indicate detection of a means by which the hippocampus encodes information and transmits this information to other brain regions involved in memory processing. By employing the nonlinear dynamic multi-input/multi-output (MIMO) model, developed and adapted to hippocampal neural ensemble firing patterns derived from simultaneous recorded multi-neuron CA1 and CA3 activity, it was possible to extract information encoded in the Sample phase of DMS trials that was necessary for successful performance in the subsequent Match phase of the task. The extension of this MIMO model to online delivery of electrical stimulation patterns to the same recording loci that exhibited successful CA1 firing in the DMS Sample Phase provided the means to increase task performance on a trial-by-trial basis. Increased utility of the MIMO model as a memory prosthesis was exhibited by the demonstration of cumulative increases in DMS task performance with repeated MIMO stimulation over many sessions. These results, reported below in this article, provide the necessary demonstrations to further the feasibility of the MIMO model as a memory prosthesis to recover and/or enhance encoding of cognitive information in humans with memory disruptions resulting from brain injury, disease or aging.

The volterra functional series is a viable alternative to kinetic models for synaptic modeling--calibration and benchmarking

Synaptic transmission is governed by a series of complex and highly nonlinear mechanisms and pathways in which the dynamics have a profound influence on the overall signal sent to the postsynaptic cell. In simulation, these mechanisms are often represented through kinetic models governed by state variables and rate law equations. Calculations of such ordinary differential equations (ODEs) in kinetic models can be computationally intensive, and although algorithms have been optimally developed to handle ODEs efficiently, simulation of numerous, large and complex kinetic models requires a prohibitively large amount of computational power. Here we present an alternative representation of ionotropic glutamatergic receptors AMPAr and NMDAr kinetic models consisting of input-output surrogates of the receptor models which can capture the nonlinear dynamics seen in the kinetic models. We benchmark this Input-Output (IO) synapse model and compare it with kinetic receptor models to evaluate the simulation time required when using either synapse model, as well as the number of time steps each model needs for simulation. While remaining faithful to the original dynamics of the model, our results indicate that the IO synapse model requires less simulation time than the kinetic models under conditions which elicit normal physiological responses, thereby improving computational efficiency while preserving the complex non-linear dynamics of the receptors. These IO surrogates therefore constitute an appealing alternative to kinetic models in large scale networks simulations.