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MIMO signal model and MISO memory decoding model. The MIMO signal model predicts the output spatio-temporal patterns of spikes based on the ongoing input spatio-temporal patterns of spikes. The MISO memory decoding model predicts the memory (behavioral) events based on the input or output spatio-temporal patterns of spikes.
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To build a cognitive prosthesis that can replace the memory function of the hippocampus, it is essential to model the input-output function of the damaged hippocampal region, so the prosthetic device can stimulate the downstream hippocampal region, e.g., CA1, with the output signal, e.g., CA1 spike trains, predicted from the ongoing input signal, e...
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Context 1
... addition to ask the question, "What should the output signal be?" We further ask the ques- tion, "What do the signals mean?" Specifically, we combine our previously developed MIMO signal model ( Song et al., 2007Song et al., , 2009aSong et al., , 2013, which predicts the output signal based on the input signal, with an additional memory decoding model that relates the input and/or output signals to the behaviors (memories) of the animal (Figure 2). The MIMO signal model is essentially a time-series regression model non-linear dynamically mapping the multiple output (CA1) signals to the multiple input (CA3) signals. ...Context 2
... MIMO signal model of input-output spike train transforma- tion takes the form of the sparse generalized Laguerre-Volterra model (SGLVM) we previously developed (Song et al., 2009a,b, 2013). In this approach, a MIMO model is a concatenation of a series of MISO models (not to be confused with the MISO classification model), that each can be considered a spiking neu- ron model ( Song et al., 2006Song et al., , 2007 (Figure 2). In this study, each MISO model consists of (a) MISO second-order Volterra kernels k transforming the input spike trains x to the synaptic potential u, (b) a Gaussian noise term ε capturing the stochastic properties of spike generation, (c) a threshold θ for generat- ing output spikes y, (e) an adder generating the pre-threshold membrane potential w, and (d) a single-input, single-output first- order Volterra kernel h transforming the preceding output spikes to the spike-triggered feedback after-potential a. ...Context 3
... this approach, the feature space is defined as a set of B-spline basis functions for each neuron (input and/or output neurons depending on the application). The classifier is essentially the logistic regression (Figure 2). B-splines are piecewise polynomials with smooth transitions between the adjacent pieces at a set of interior knot points. ...Context 4
... resulting SGLVM non-linear dynamically predicts the CA1 spikes based on the ongoing and past (within the memory window) CA3 spikes ( Song et al., 2007Song et al., , 2009aSong and Berger, 2010). Results show that in both cases (Figures 6, 7, row 2 and 4), the MIMO signal model can accurately predict the CA1 spatio-temporal patterns at both the single trial level (Figures 6, 7, column 1-4) and the overall level (Figures 6, 7, column 5). Importantly, a single set of the model coefficients are used for both the left and right trials. ...Similar publications
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Citations
... The MDM model therefore allowed the creation of these static stimulation patterns outside of a stimulation session, while the MIMO model would be run during a session to generate trial specific stimulation patterns live. Briefly, B-spline basis functions are used to extract memory features from spatio-temporal patterns of spikes (Song et al., , 2014. B-splines are piecewise polynomial functions with smooth transitions between adjacent pieces at a set of interior knot points, where the number of knots determines the number of B-splines. ...
... Only out-of-sample MCCs are utilized for the SCFMSs and only SCFMs are considered for the MDM. Full details of the model are provided by Song et al. (2013Song et al. ( , 2014. ...
Objective: Here, we demonstrate the first successful use of static neural stimulation patterns for specific information content. These static patterns were derived by a model that was applied to a subject’s own hippocampal spatiotemporal neural codes for memory.
Approach: We constructed a new model of processes by which the hippocampus encodes specific memory items via spatiotemporal firing of neural ensembles that underlie the successful encoding of targeted content into short-term memory. A memory decoding model (MDM) of hippocampal CA3 and CA1 neural firing was computed which derives a stimulation pattern for CA1 and CA3 neurons to be applied during the encoding (sample) phase of a delayed match-to-sample (DMS) human short-term memory task.
Main results: MDM electrical stimulation delivered to the CA1 and CA3 locations in the hippocampus during the sample phase of DMS trials facilitated memory of images from the DMS task during a delayed recognition (DR) task that also included control images that were not from the DMS task. Across all subjects, the stimulated trials exhibited significant changes in performance in 22.4% of patient and category combinations. Changes in performance were a combination of both increased memory performance and decreased memory performance, with increases in performance occurring at almost 2 to 1 relative to decreases in performance. Across patients with impaired memory that received bilateral stimulation, significant changes in over 37.9% of patient and category combinations was seen with the changes in memory performance show a ratio of increased to decreased performance of over 4 to 1. Modification of memory performance was dependent on whether memory function was intact or impaired, and if stimulation was applied bilaterally or unilaterally, with nearly all increase in performance seen in subjects with impaired memory receiving bilateral stimulation.
Significance: These results demonstrate that memory encoding in patients with impaired memory function can be facilitated for specific memory content, which offers a stimulation method for a future implantable neural prosthetic to improve human memory.
... Vários achados indicaram que o hipocampo codifica informações e as transmite para outras regiões envolvidas no processamento da memória a partir da utilização do dispositivo de prótese de memória (MIMO) durante o desempenho da tarefa, mas ainda não há precisão e consistência nas investigações. Hampson et al. (2012) analisaram estudos sobre o uso do MIMO em animais não humanos e acreditam que esta será uma técnica de utilização de dispositivo no cérebro para melhoria da memória tanto para adequação do funcionamento cerebral no tratamento de transtornos quanto de aprimoramento. Entretanto, isso ainda não foi consistentemente testado em humanos e em larga escala. ...
O objetivo deste artigo é apresentar o estado da arte das pesquisas científicas sobre aprimoramento cognitivo, visando a fornecer subsídios para uma discussão menos especulativa sobre o tema. Para tal, realizamos uma revisão integrativa da literatura sobre os dispositivos de aprimoramento cognitivo biotecnológicos vigentes. O resultado aponta para o caráter inconcluso das pesquisas, não havendo respostas significativas que certifiquem a efetividade de práticas de aprimoramento cognitivo, ao menos da forma como este vem sendo idealizado e discutido. Defenderemos, assim, que os investimentos na área precisam ser analisados sob o ponto de vista da justiça, levando-se em consideração outras formas de aprimoramento cognitivo disponíveis e comprovadas. Pretendemos indicar como alternativa um investimento substancial na mais antiga forma de aprimoramento humano, a educação, a fim de que novas metodologias possam ser pensadas e adequadas às demandas da sociedade contemporânea e a um ideal de sociedade mais justa e igualitária. Palavras-chave: Aprimoramento cognitivo; Neurociência; Biotecnologia. ABSTRACT The purpose of this paper is to present the state of the art of scientific research on cognitive enhancement, aiming to provide subsidies for a less speculative discussion on the subject. To this end, we conducted an integrative review of literature on the current biotechnological cognitive enhancement devices. The result points to the inconclusive character of the research, so that there are no significant answers that certify the effectiveness of cognitive enhancement practices, at least in the way that it has been idealized and discussed. We will therefore defend that investments in the area need to be analyzed from the point of view of justice, taking into account other available and proven forms of cognitive enhancement. We aim to indicate as an alternative a substantial http://dx.
... However, MIMO stimulation patterned from the native spatiotemporal connectivity of input and output neurons extracted by the MIMO model facilitated short-term memory in the form of DMS performance (figure 7) as well as longer-term memory in the form of DR performance (figure 8). This result may explain why both the direct stimulation-induced impairments cited above, as well as similar effects shown in a recent study in humans (Jacobs et al 2016), are not effective, since hippocampal memory encoding requires specific spatiotemporal relationships with respect to within and between cell layer neural firing, i.e. neural 'codes' , Song et al 2014, Deadwyler et al 2017. Hence, fixed frequency or randomized stimulation applied directly to the hippocampus likely Figure 9. Summary bar graphs of overall DMS (left) and DR performance across all subjects (DMS: n = 7, DR: n = 6) and test conditions. ...
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.
... A multi-input, multi-output (MIMO) dynamical model can be decomposed into a series of structurally plausible multi-input, single-output (MISO) models (Song et al., 2014), which projects to a separate output neural and can be represented by ...
... Cortical prostheses seek to restore cognitive functions that have been lost due to diseases or injuries [65]. This kind of prostheses has to deal only with internal brain signals, creating an appropriate output by encoding the input brain signals so that the damaged brain area can be bypassed restoring the cerebral function. ...
Great progress has been achieved in the last few years in Neurorehabilitation and Neural Engineering. Thanks to the parallel development of medical research, the chances to survive a neural injury are growing, so it is necessary to develop technologies that can be used both for rehabilitation and to improve daily activities and social life. New robotic and prosthetic devices and technologies such as Functional Electrical Stimulation, Brain-Computer Interfaces and Virtual Reality are slowly becoming part of clinical rehabilitation setting, but they are far from being part of everyday life for the patients. As technology improves, expectations keep rising and new problems emerge: from cost reduction to the diffusion in the medical environment, from the enlargement of the number of patients who may benefit from these technologies to transferring rehabilitation to the patient’s home. All these requests lead to great challenges that researchers have to face to enhance their contribution towards the improvement of rehabilitation and life conditions of patients with neural impairment.
... For example, we may first build a memory decoding model for the hippocampal CA3 patterns to assess what memories are encoded, and then build another memory decoding model for the hippocampal CA1 patterns predicted by the MIMO model, to quantify how much of these memories are maintained in the restored signals. We have formulated this modeling framework and successfully applied it in our rodent studies [29], where much simpler memories (i.e., Left or Right locations in the delayed nonmatch-to-sample task) are involved. Human studies, on the other hand, involves much more complex memories with multiple categories, attributes and associations. ...
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.
... A multi-input, multi-output (MIMO) dynamical model can be decomposed into a series of structurally plausible multi-input, single-output (MISO) models (Song et al., 2014), which projects to a separate output neural and can be represented by ...
Neural plasticity, elicited by processes such as development and learning, is an important biological attribute which can be viewed as a time-varying property of the nervous system. In this paper, we investigated the novel use of Chebyshev polynomials to estimate the changes in model parameters efficiently for time-varying dynamical systems with binary inputs and outputs. A forward orthogonal least square (FOLS) algorithm selected the significant model terms. Extensive simulations showed that the proposed algorithm identified the system changes more accurately in comparison with adaptive filter. This approach can be applied to identify not only gradual but also abrupt temporal evolutions of neural dynamics underlying nervous system activity with high sensitivity and accuracy by observing input and output spike trains only.
... Sensorimotor NPs, also called bidirectional, simultaneously produce movements and evoke sensations (O'Doherty, , Bensmaia and Miller 2014. Cognitive NPs (Andersen, Burdick et al. 2004, Andersen, Hwang et al. 2010) reproduce higherorder functions, notably attention (Astrand, Wardak et al. 2014, Ordikhani-Seyedlar, Lebedev et al. 2016, memory (Berger, Hampson et al. 2011, Deadwyler, Hampson et al. 2013, Madan 2014, Song, Harway et al. 2014, and decision making ). Brainnets are NPs incorporating several interconnected brains (Ramakrishnan, Ifft et al. 2015). ...
Neural prostheses (NPs) link the brain to external devices, with an eventual goal of recovery of motor and sensory functions to patients with neurological conditions. Over the past half-century, NPs have advanced significantly from the early ideas that sounded like science fiction to the modern high-tech implementations. In particular, invasive recordings using multichannel implants have enabled real-time control of artificial limbs by nonhuman primates and human subjects. Furthermore, NPs can provide artificial sensory feedback, allowing users to perceive the movements of prosthetic limbs and their haptic interaction with external objects. Recently, NP approach was used to build brain-nets that enable information exchange between individual brains and execution of cooperative tasks. This review focuses on invasive NPs for sensorimotor functions.
... Attempts have been made to produce neuroprostheses that can directly interface with the brain and replace and/or supplement the function of hippocampus and other structures involved in memory formation and maintenance (Berger et al., 2011(Berger et al., , 2012Deadwyler et al., 2013;Song et al., 2014). Future generations of such neuroprostheses can serve as pen-drives for the brain that can be used to increase the cache memory of the brain or transfer information physically when direct neuroprosthetic communication channels are off-line or have insufficient bandwidth to transfer information in a timely manner (Madan, 2014). ...
Neuroprosthetics has enjoyed increasing interest among researchers, health care professionals, the media, and the general public over the past few decades. New developments in neuroprostheses are the result of increased collaboration among those working in science, engineering, and medicine. It is a rapidly growing field with tremendous potential for restoration or supplementation of an existing bodily function or sense, as well as creation of potentially novel functional and sensing abilities. With rapid technological advancements, significant ethical concerns related to neuroprostheses have been noted. Here we discuss such ethical issues associated with neuroprostheses at their various stages of development and use.
Neuromorphic engineering aims at designing and building electronic systems that emulate the function and organization of nervous systems in very large-scale integration (VLSI) technology. Current neuromorphic VLSI hardware can now emulate large-scale neural network models with up to a million of silicon neurons, as well as various computational primitives involved in pattern recognition, learning, classification, and decision-making processes. These advances have spurred the development of closed-loop biohybrid circuits between populations of biological neurons and neural networks of silicon neurons. Biohybrid systems provide biological realism and relevance for investigating neuronal network functions and for fast prototyping of bidirectional neuromorphic neural interfaces and neuroprosthetic devices. This chapter describes the current efforts toward large-scale neuromorphic neural interfaces and their emerging applications in closed-loop neuroscience, for the study of neuronal networks at multiple timescales and levels of biological organization, and in neuroprosthetics, for the restoration of sensory, motor, and cognitive functions.
