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The brains of all animals are plastic, allowing us to form new memories, adapt to new environments, and to learn new tasks. What is less clear is how much plasticity is required to perform these cognitive functions: does learning require widespread plasticity across the brain, or can learning occur with more rigid networks, in which plasticity is highly localized? Here, we use biologically-inspired recurrent neural network (RNN) models to show that rapid multitask learning can be accomplished in reservoir-style networks, in which synaptic plasticity is sparse and highly localized. Crucially, only RNNs initialized with highly specific combinations of network properties, such as topology, normalization and reciprocal connection strength, are capable of such learning. Finally, we show that this rapid learning with localized plasticity can be accomplished with purely local error signals, without backpropagation, using a reinforcement learning setup. This work suggests that rapid learning in artificial (and potentially biological) agents can be accomplished with mostly-rigid networks, in which synaptic plasticity is highly constrained.

Whenever the retinal image changes, some neurons in visual cortex increase their rate of firing whereas others decrease their rate of firing. Linking specific sets of neuronal responses with perception and behavior is essential for understanding mechanisms of neural circuit computation. We trained mice of both sexes to perform visual detection tasks and used optogenetic perturbations to increase or decrease neuronal spiking primary visual cortex (V1). Perceptual reports were always enhanced by increments in V1 spike counts and impaired by decrements, even when increments and decrements in spiking were generated in the same neuronal populations. Moreover, detecting changes in cortical activity depended on spike count integration rather than instantaneous changes in spiking. Recurrent neural networks trained in the task similarly relied on increments in neuronal activity when activity has costs. This work clarifies neuronal decoding strategies used by cerebral cortex to translate cortical spiking into percepts that can be used to guide behavior.SIGNIFICANCE STATEMENTVisual responses in the primary visual cortex (V1) are diverse, in that neurons can be either excited or inhibited by the onset of a visual stimulus. We selectively potentiated or suppressed V1 spiking in mice while they performed contrast change detection tasks. In other experiments, excitation or inhibition was delivered to V1 independent of visual stimuli. Mice readily detected increases in V1 spiking while equivalent reductions in V1 spiking suppressed the probability of detection, even when increases and decreases in V1 spiking were generated in the same neuronal populations. Our data raise the striking possibility that only increments in spiking are used to render information to structures downstream of V1.

Recently it has been proposed that information in working memory (WM) may not always be stored in persistent neuronal activity but can be maintained in 'activity-silent' hidden states, such as synaptic efficacies endowed with short-term synaptic plasticity. To test this idea computationally, we investigated recurrent neural network models trained to perform several WM-dependent tasks, in which WM representation emerges from learning and is not a priori assumed to depend on self-sustained persistent activity. We found that short-term synaptic plasticity can support the short-term maintenance of information, provided that the memory delay period is sufficiently short. However, in tasks that require actively manipulating information, persistent activity naturally emerges from learning, and the amount of persistent activity scales with the degree of manipulation required. These results shed insight into the current debate on WM encoding and suggest that persistent activity can vary markedly between short-term memory tasks with different cognitive demands.

The ability to compare sequential sensory inputs is crucial for solving many behavioral tasks. To understand the neuronal mechanisms underlying sequential decisions, we compared neuronal responses in the prefrontal cortex (PFC) and the lateral and medial intra-parietal (LIP and MIP) areas in monkeys trained to decide whether sequentially presented stimuli were from matching (M) or nonmatching (NM) categories. We found that PFC leads the M/NM decision process relying on nonlinear neuronal integration of sensory and mnemonic information, whereas LIP and MIP are more involved in sensory evaluation and motor planning, respectively. Furthermore, multi-module recurrent neural networks trained on the same task exhibited the key features of PFC and LIP encoding, including nonlinear integrative encoding in the PFC-like module which was crucial for M/NM decisions. Together, our results illuminate the relative functions of LIP, PFC, and MIP in sensory, cognitive and motor functions, and suggest that nonlinear integration of task-related variables in PFC is important for mediating sequential decisions.

Persistent activity within the frontoparietal network is consistently observed during tasks that require working memory. However, the neural circuit mechanisms underlying persistent neuronal encoding within this network remain unresolved. Here, we ask how neural circuits support persistent activity by examining population recordings from posterior parietal (PPC) and prefrontal (PFC) cortices in two male monkeys that performed spatial and motion direction-based tasks that required working memory. While spatially selective persistent activity was observed in both areas, robust selective persistent activity for motion direction was only observed in PFC. Crucially, we find that this difference between mnemonic encoding in PPC and PFC is associated with the presence of functional clustering: PPC and PFC neurons up to ∼700 μm apart preferred similar spatial locations, and PFC neurons up to ∼700 μm apart preferred similar motion directions. In contrast, motion-direction tuning similarity between nearby PPC neurons was much weaker and decayed rapidly beyond ∼200 μm. We also observed a similar association between persistent activity and functional clustering in trained recurrent neural network models embedded with a columnar topology. These results suggest that functional clustering facilitates mnemonic encoding of sensory information.SIGNIFICANCE STATEMENT Working memory refers to our ability to temporarily store and manipulate information. Numerous studies have observed that, during working memory, neurons in higher cortical areas, such as the parietal and prefrontal cortices, mnemonically encode the remembered stimulus. However, several recent studies have failed to observe mnemonic encoding during working memory, raising the question as to why mnemonic encoding is observed during some, but not all, conditions. In this study, we show that mnemonic encoding occurs when a cortical area is organized such that nearby neurons preferentially respond to the same stimulus. This result provides plausible neuronal conditions that allow for mnemonic encoding, and gives us further understanding of the brain's mechanisms that support working memory.

A traditional view of short-term working memory (STM) is that task-relevant information is maintained 'online' in persistent spiking activity. However, recent experimental and modeling studies have begun to question this long-held belief. In this review, we discuss new evidence demonstrating that information can be 'silently' maintained via short-term synaptic plasticity (STSP) without the need for persistent activity. We discuss how the neural mechanisms underlying STM are inextricably linked with the cognitive demands of the task, such that the passive maintenance and the active manipulation of information are subserved differently in the brain. Together, these recent findings point towards a more nuanced view of STM in which multiple substrates work in concert to support our ability to temporarily maintain and manipulate information.

SUMMARY BIN1, a member of the BAR adaptor protein family, is a significant late-onset Alzheimer disease risk factor. Here, we investigate BIN1 function in the brain using conditional knockout (cKO) models. Loss of neuronal Bin1 expression results in the select impairment of spatial learning and memory. Examination of hippocampal CA1 excitatory synapses reveals a deficit in presynaptic release probability and slower depletion of neurotransmitters during repetitive stimulation, suggesting altered vesicle dynamics in Bin1 cKO mice. Super-resolution and immunoelectron microscopy localizes BIN1 to presynaptic sites in excitatory synapses. Bin1 cKO significantly reduces synapse density and alters presynaptic active zone protein cluster formation. Finally, 3D electron microscopy reconstruction analysis uncovers a significant increase in docked and reserve pools of synaptic vesicles at hippocampal synapses in Bin1 cKO mice. Our results demonstrate a non-redundant role for BIN1 in presynaptic regulation, thus providing significant insights into the fundamental function of BIN1 in synaptic physiology relevant to Alzheimer disease., Graphical Abstract, In Brief BIN1 is a significant risk factor for late-onset Alzheimer disease. BIN1 has a general role in endocytosis and membrane dynamics in non-neuronal cells. De Rossi et al. show that BIN1 localizes to presynaptic terminals and plays an indispensable role in excitatory synaptic transmission by regulating neurotransmitter vesicle dynamics.

BIN1, a member of the BAR adaptor protein family, is a significant late-onset Alzheimer’s disease risk factor. Here, we investigated BIN1 function in the brain using conditional knockout (cKO) models. The loss of neuronal Bin1 expression resulted in select impairment of spatial learning and memory. Examination of hippocampal CA1 excitatory synapses revealed a deficit in presynaptic release probability and slower depletion of neurotransmitter during repetitive stimulation, suggesting altered vesicle dynamics in Bin1 cKO mice. Super-resolution and immunoelectron microscopy localized BIN1 to presynaptic sites in excitatory synapses. Bin1 cKO significantly reduced synapse density and altered presynaptic active zone protein cluster formation. Finally, 3D-electron microscopy reconstruction analysis uncovered a significant increase of docked and reserve pool of synaptic vesicles at hippocampal synapses in Bin1 cKO mice. Our results demonstrate a non-redundant role for BIN1 in presynaptic regulation, thus providing novel insights into BIN1's fundamental function in synaptic physiology relevant to Alzheimer’s disease.

Background Multiple types of neural signals are available for controlling assistive devices through brain–computer interfaces (BCIs). Intracortically recorded spiking neural signals are attractive for BCIs because they can in principle provide greater fidelity of encoded information compared to electrocorticographic (ECoG) signals and electroencephalograms (EEGs). Recent reports show that the information content of these spiking neural signals can be reliably extracted simply by causally band-pass filtering the recorded extracellular voltage signals and then applying a spike detection threshold, without relying on “sorting” action potentials. New method We show that replacing the causal filter with an equivalent non-causal filter increases the information content extracted from the extracellular spiking signal and improves decoding of intended movement direction. This method can be used for real-time BCI applications by using a 4 ms lag between recording and filtering neural signals. Results Across 18 sessions from two people with tetraplegia enrolled in the BrainGate2 pilot clinical trial, we found that threshold crossing events extracted using this non-causal filtering method were significantly more informative of each participant's intended cursor kinematics compared to threshold crossing events derived from causally filtered signals. This new method decreased the mean angular error between the intended and decoded cursor direction by 9.7° for participant S3, who was implanted 5.4 years prior to this study, and by 3.5° for participant T2, who was implanted 3 months prior to this study. Conclusions Non-causally filtering neural signals prior to extracting threshold crossing events may be a simple yet effective way to condition intracortically recorded neural activity for direct control of external devices through BCIs.

Categorization is essential for interpreting sensory stimuli and guiding our actions. Recent studies have revealed robust neuronal category representations in the lateral intraparietal area (LIP). Here, we examine the specialization of LIP for categorization and the roles of other parietal areas by comparing LIP and the medial intraparietal area (MIP) during a visual categorization task. MIP is involved in goal-directed arm movements and visuomotor coordination but has not been implicated in non-motor cognitive functions, such as categorization. As expected, we found strong category encoding in LIP. Interestingly, we also observed category signals in MIP. However, category signals were stronger and appeared with a shorter latency in LIP than MIP. In this task, monkeys indicated whether a test stimulus was a category match to a previous sample with a manual response. Test-period activity in LIP showed category encoding and distinguished between matches and non-matches. In contrast, MIP primarily reflected the match/non-match status of test stimuli, with a strong preference for matches (which required a motor response). This suggests that, although category representations are distributed across parietal cortex, LIP and MIP play distinct roles: LIP appears more involved in the categorization process itself, whereas MIP is more closely tied to decision-related motor actions.

Two people with long-standing tetraplegia use neural interface system-based control of a robotic arm to perform three-dimensional reach and grasp movements. John Donoghue and colleagues have previously demonstrated that people with tetraplegia can learn to use neural signals from the motor cortex to control a computer cursor. Work from another lab has also shown that monkeys can learn to use such signals to feed themselves with a robotic arm. Now, Donoghue and colleagues have advanced the technology to a level at which two people with long-standing paralysis — a 58-year-old woman and a 66-year-old man — are able to use a neural interface to direct a robotic arm to reach for and grasp objects. One subject was able to learn to pick up and drink from a bottle using a device implanted 5 years earlier, demonstrating not only that subjects can use the brain–machine interface, but also that it has potential longevity. Paralysis following spinal cord injury, brainstem stroke, amyotrophic lateral sclerosis and other disorders can disconnect the brain from the body, eliminating the ability to perform volitional movements. A neural interface system1,2,3,4,5 could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with long-standing tetraplegia can use a neural interface system to move and click a computer cursor and to control physical devices6,7,8. Able-bodied monkeys have used a neural interface system to control a robotic arm9, but it is unknown whether people with profound upper extremity paralysis or limb loss could use cortical neuronal ensemble signals to direct useful arm actions. Here we demonstrate the ability of two people with long-standing tetraplegia to use neural interface system-based control of a robotic arm to perform three-dimensional reach and grasp movements. Participants controlled the arm and hand over a broad space without explicit training, using signals decoded from a small, local population of motor cortex (MI) neurons recorded from a 96-channel microelectrode array. One of the study participants, implanted with the sensor 5 years earlier, also used a robotic arm to drink coffee from a bottle. Although robotic reach and grasp actions were not as fast or accurate as those of an able-bodied person, our results demonstrate the feasibility for people with tetraplegia, years after injury to the central nervous system, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals.

Previous studies have shown that sensory neurons that are the most informative of the stimulus tend to be the best correlated with the subject's perceptual decision. We wanted to know whether this relationship might also apply to short time segments of a neuron's response. We asked whether spikes that conveyed more information about a motion stimulus were also more tightly linked to the perceptual behavior. We examined single-neuron activity in middle temporal (MT) area while monkeys performed a motion-detection task. Because of a slow stimulus update (every 27 ms), activity in many MT neurons was entrained and phase-locked to the stimulus. These stimulus-entrained neuronal oscillations allowed us to separate spikes based on phase. We observed a large amount of variability in how spikes at different phases of the oscillation encoded the stimulus, as revealed by the spike-triggered average of the motion. Spikes during certain phases of the cycle were much more informative about the presence of coherent motion than others. Importantly, we found that the phases that were the most informative about the motion stimulus were also more correlated with the behavioral performance and reaction time of the animal. Our results suggest that the relationship between a neuron's spikes, the stimulus, and behavior can vary on a time scale of tens of milliseconds.

We examined how hippocamal CA1 neurons process complex time-varying inputs that dendrites are likely to receive in vivo. We propose a functional model of the dendrite-to-soma input/output relationship that combines temporal integration and static-gain control mechanisms. Using simultaneous dual whole cell recordings, we injected 50 s of subthreshold and suprathreshold zero-mean white-noise current into the primary dendritic trunk along the proximal 2/3 of stratum radiatum and measured the membrane potential at the soma. Applying a nonlinear system-identification analysis, we found that a cascade of a linear filter followed by an adapting static-gain term fully accounted for the nonspiking input/output relationship between the dendrite and soma. The estimated filters contained a prominent band-pass region in the 1- to 10-Hz frequency range that remained constant as a function of stimulus variance. The gain of the dendrite-to-soma input/output relationship, in contrast, varied as a function of stimulus variance. When the contribution of the voltage-dependent current Ih was eliminated, the estimated filters lost their band-pass properties and the gain regulation was substantially altered. Our findings suggest that the dendrite-to-soma input/output relationship for proximal apical inputs to CA1 pyramidal neurons is well described as a band-pass filter in the theta frequency range followed by a gain-control nonlinearity that dynamically adapts to the statistics of the input signal.

Brain-computer interfaces (BCIs) promise to restore independence for people with severe motor disabilities by translating decoded neural activity directly into the control of a computer. However, recorded neural signals are not stationary (that is, can change over time), degrading the quality of decoding. Requiring users to pause what they are doing whenever signals change to perform decoder recalibration routines is time-consuming and impractical for everyday use of BCIs. We demonstrate that signal nonstationarity in an intracortical BCI can be mitigated automatically in software, enabling long periods (hours to days) of self-paced point-and-click typing by people with tetraplegia, without degradation in neural control. Three key innovations were included in our approach: tracking the statistics of the neural activity during self-timed pauses in neural control, velocity bias correction during neural control, and periodically recalibrating the decoder using data acquired during typing by mapping neural activity to movement intentions that are inferred retrospectively based on the user’s self-selected targets. These methods, which can be extended to a variety of neurally controlled applications, advance the potential for intracortical BCIs to help restore independent communication and assistive device control for people with paralysis.

Our ability to learn a wide range of behavioral tasks is essential for responding appropriately to sensory stimuli according to behavioral demands, but the underlying neural mechanism has been rarely examined by neurophysiological recordings in the same subjects across learning. To understand how learning new behavioral tasks affects neuronal representations, we recorded from posterior parietal cortex (PPC) before and after training on a visual motion categorization task. We found that categorization training influenced cognitive encoding in PPC, with a marked enhancement of memory-related delay-period encoding during the categorization task that was absent during a motion discrimination task before categorization training. In contrast, the prefrontal cortex (PFC) exhibited strong delay-period encoding during both discrimination and categorization tasks. This reveals a dissociation between PFC's and PPC's roles in working memory, with general engagement of PFC across multiple tasks, in contrast with more task-specific mnemonic encoding in PPC.

BACKGROUND:Multiple types of neural signals are available for controlling assistive devices through brain-computer interfaces (BCIs). Intracortically recorded spiking neural signals are attractive for BCIs because they can in principle provide greater fidelity of encoded information compared to electrocorticographic (ECoG) signals and electroencephalograms (EEGs). Recent reports show that the information content of these spiking neural signals can be reliably extracted simply by causally band-pass filtering the recorded extracellular voltage signals and then applying a spike detection threshold, without relying on "sorting" action potentials. NEW METHOD:We show that replacing the causal filter with an equivalent non-causal filter increases the information content extracted from the extracellular spiking signal and improves decoding of intended movement direction. This method can be used for real-time BCI applications by using a 4ms lag between recording and filtering neural signals. RESULTS:Across 18 sessions from two people with tetraplegia enrolled in the BrainGate2 pilot clinical trial, we found that threshold crossing events extracted using this non-causal filtering method were significantly more informative of each participant's intended cursor kinematics compared to threshold crossing events derived from causally filtered signals. This new method decreased the mean angular error between the intended and decoded cursor direction by 9.7° for participant S3, who was implanted 5.4 years prior to this study, and by 3.5° for participant T2, who was implanted 3 months prior to this study. CONCLUSIONS:Non-causally filtering neural signals prior to extracting threshold crossing events may be a simple yet effective way to condition intracortically recorded neural activity for direct control of external devices through BCIs.

Resume L’etude rapporte les resultats d’une serie continue, prospective, multicentrique de 425 protheses Wallaby I ® conservant le ligament croise posterieur, posees par le groupe Guepar de 1992 a 1995. Le composant femoral de cette prothese presente des condyles asymetriques et divergents pour se rapprocher le plus possible de la cinematique du genou normal. Le plateau tibial en polyethylene est fixe. L’âge moyen des patients (70,5 ans), la repartition homme (28,3 %) et femme (71,7 %), l’etiologie (91 % d’arthrose primitive ou secondaire) et la deviation preoperatoire des genoux (65,6 % de varus > a 3° et 24 % de valgus > a 4°) etaient identiques a ceux qui sont publies dans la litterature pour ce type de prothese. Vingt-six protheses ont ete suivies moins de 1 an, 84 moins de 5 ans et 315 5 ans ou plus. Parmi les 399 genoux suivis plus de 1 an, il y a eu 4 infections (1 %) dont 3 ont necessite le changement de prothese, 3 fractures du femur toutes post-traumatiques, 2 descellements (1 global et 1 tibial) ayant necessite une reprise, 1 descellement tibial et 3 descellements rotuliens non-repris. Trois patients presentaient des douleurs anterieures du genou traitees avec succes par 2 resurfacages secondaires de la rotule et 1 section de l’aileron rotulien externe. Dix-neuf fractures de rotules (4,7 %) (dont 17 decouvertes fortuitement) constituaient la complication la plus frequente, sans retentissement fonctionnel. Le score moyen IKS genou postoperatoire etait de 90,5 points et le score moyen IKS fonction de 61,6 points. La mobilite moyenne etait de 110,5° ; 72,9 % des genoux etaient axes suivant la classification radiologique IKS, La survie de cette prothese a 8 ans (suivant Kaplan Meier) etait de 97,7 % quelle que soit la raison du changement de prothese et de 98,5 % en ne prenant comme evenement que la reprise pour descellement aseptique. Ces bons resultats corroborent ceux qui sont publies dans la litterature concernant les protheses conservant le ligament croise posterieur a plateau tibial fixe.

© 2012 Cook et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Linking Neural Activity to Visual Perception: Separating Sensory and Attentional Contributions

Distinguishing which of the many proposed neural mechanisms of spatial attention actually underlies behavioral improvements in visually guided tasks has been difficult. One attractive hypothesis is that attention allows downstream neural circuits to selectively integrate responses from the most informative sensory neurons. This would allow behavioral performance to be based on the highest-quality signals available in visual cortex. We examined this hypothesis by asking how spatial attention affects both the stimulus sensitivity of middle temporal (MT) neurons and their corresponding correlation with behavior. Analyzing a data set pooled from two experiments involving four monkeys, we found that spatial attention did not appreciably affect either the stimulus sensitivity of the neurons or the correlation between their activity and behavior. However, for those sessions in which there was a robust behavioral effect of attention, focusing attention inside the neuron's receptive field significantly increased the correlation between these two metrics, an indication of selective integration. These results suggest that, similar to mechanisms proposed for the neural basis of perceptual learning, the behavioral benefits of focusing spatial attention are attributable to selective integration of neural activity from visual cortical areas by their downstream targets.

Electrical stimulation of the brain is a valuable research tool and has shown therapeutic promise in the development of new sensory neural prosthetics. Despite its widespread use, we still do not fully understand how current passed through a microelectrode interacts with functioning neural circuits. Past behavioral studies have suggested that weak electrical stimulation (referred to as microstimulation) of sensory areas of cortex produces percepts that are similar to those generated by normal sensory stimuli. In contrast, electrophysiological studies using in vitro or anesthetized preparations have shown that neural activity produced by brief microstimulation is radically different and longer lasting than normal responses. To help reconcile these two aspects of microstimulation, we examined the temporal properties that microstimulation has on visual perception. We found that brief application of subthreshold microstimulation in the middle temporal (MT) area of visual cortex produced smaller and longer-lasting effects on motion perception compared with an equivalent visual stimulus. In agreement with past electrophysiological studies, a computer simulation reproduced our behavioral effects when the time course of a single microstimulation pulse was modeled with three components: an immediate fast strong excitatory component, followed by a weaker inhibitory component, and then followed by a long duration weak excitatory component. Overall, these results suggest the behavioral effects of microstimulation in our experiments were caused by the unique and long-lasting temporal effects microstimulation has on functioning cortical circuits.

In both insect and vertebrate olfactory systems only two synapses separate the sensory periphery from brain areas required for memory formation and the organisation of behaviour. In the Drosophila olfactory system, which is anatomically very similar to its vertebrate counterpart, there has been substantial recent progress in understanding the flow of information from experiments using molecular genetic, electrophysiological and optical imaging techniques. In this review, we shall focus on how olfactory information is processed and transformed in order to extract behaviourally relevant information. We follow the progress from olfactory receptor neurons, through the first processing area, the antennal lobe, to higher olfactory centres. We address both the underlying anatomy and mechanisms that govern the transformation of neural activity. We emphasise our emerging understanding of how different elementary computations, including signal averaging, gain control, decorrelation and integration, may be mapped onto different circuit elements.

It is widely reported that the activity of single neurons in visual cortex is correlated with the perceptual decision of the subject. The strength of this correlation has implications for the neuronal populations generating the percepts. Here we asked whether microsaccades, which are small, involuntary eye movements, contribute to the correlation between neural activity and behavior. We analyzed data from three different visual detection experiments, with neural recordings from the middle temporal (MT), lateral intraparietal (LIP), and ventral intraparietal (VIP) areas. All three experiments used random dot motion stimuli, with the animals required to detect a transient or sustained change in the speed or strength of motion. We found that microsaccades suppressed neural activity and inhibited detection of the motion stimulus, contributing to the correlation between neural activity and detection behavior. Microsaccades accounted for as much as 19% of the correlation for area MT, 21% for area LIP, and 17% for VIP. While microsaccades only explain part of the correlation between neural activity and behavior, their effect has implications when considering the neuronal populations underlying perceptual decisions.

The discovery that an array of voltage- and time-dependent channels is present in both the dendrites and soma of neurons has led to a variety of models for single-neuron computation. Most of these models, however, are based on experimental techniques that use simplified inputs of either single synaptic events or brief current injections. In this study, we used a more complex time-varying input to mimic the continuous barrage of synaptic input that neurons are likely to receive in vivo. Using dual whole-cell recordings of CA1 pyramidal neurons, we injected long-duration white-noise current into the dendrites. The amplitude variance of this stimulus was adjusted to produce either low subthreshold or high suprathreshold fluctuations of the somatic membrane potential. Somatic action potentials were produced in the high variance input condition. Applying a rigorous system-identification approach, we discovered that the neuronal input/output function was extremely well described by a model containing a linear bandpass filter followed by a nonlinear static-gain. Using computer models, we found that a range of voltage-dependent channel properties can readily account for the experimentally observed filtering in the neuronal input/output function. In addition, the bandpass signal processing of the neuronal input/output function was determined by the time-dependence of the channels. A simple active channel, however, could not account for the experimentally observed change in gain. These results suggest that nonlinear voltage- and time-dependent channels contribute to the linear filtering of the neuronal input/output function and that channel kinetics shape temporal signal processing in dendrites.

How is the activity of neurons in the sensory areas of cortex related to our perceptual abilities? Past studies have suggested that sensory neurons that best encode a visual stimulus are also more influential in forming the perception of the stimulus. We wanted to know if this relationship also extended to individual action potentials. If some spikes encode the stimulus better than others, then would these same spikes have more weight in supporting the perceptual behavior? To address this question, data from a past motion detection experiment was analyzed [1]. In this study, two monkeys were trained to detect the onset of coherent motion in a random dot patch that initially contained 0% coherent motion. The coherent motion signal occurred at a random time and the animals were rewarded for responding within a 750 ms window. Extracellular recordings were made from single neurons in the Middle Temporal (MT) area. The random dot patch overlapped the receptive field of the neuron and the coherent motion was matched to the neuron's preferred direction and speed. Importantly, the position of the random dots was only updated once every 27 ms. Because of the slow motion update rate, the neural activity of many of the MT neurons oscillated at the same frequency as the motion updates. It was these oscillations that allowed us to ask if some spikes were more influential than others by measuring how sensory and choice related information varied as a function of the phase of the oscillation. To determine whether some spikes encoded the stimulus better than others, we examined the difference between spikes on the rising phase of the oscillation (from the trough to the peak) compared to spikes on the falling phase (peak to trough). Using the spike-triggered average of the motion stimulus, we found that spikes did not equally represent sensory information. Spikes occurring during the rising phase of the stimulus-induced oscillation encoded stronger motion than spikes occurring during the falling phase. Importantly, the same spikes that encoded stronger motion were also more correlated with the animal's behavioral performance and reaction time. This suggests that the spikes carrying the most reliable task related information are more strongly linked to the behavioral decision. In addition, these results support the hypothesis that phase could be used as a possible encoding scheme during neuronal oscillations.

The discovery that an array of voltage- and time-dependent channels is present in both the dendrites and soma of neurons has led to a variety of models for single-neuron computation. Most of these models, however, are based on experimental techniques that use simplified inputs of either single synaptic events or brief current injections. In this study, we used a more complex time-varying input to mimic the continuous barrage of synaptic input that neurons are likely to receive in vivo. Using dual whole-cell recordings of CA1 pyramidal neurons, we injected long-duration white-noise current into the dendrites. The amplitude variance of this stimulus was adjusted to produce either low subthreshold or high suprathreshold fluctuations of the somatic membrane potential. Somatic action potentials were produced in the high variance input condition. Applying a rigorous system-identification approach, we discovered that the neuronal input/output function was extremely well described by a model containing a linear bandpass filter followed by a nonlinear static-gain. Using computer models, we found that a range of voltage-dependent channel properties can readily account for the experimentally observed filtering in the neuronal input/output function. In addition, the bandpass signal processing of the neuronal input/output function was determined by the time-dependence of the channels. A simple active channel, however, could not account for the experimentally observed change in gain. These results suggest that nonlinear voltage- and time-dependent channels contribute to the linear filtering of the neuronal input/output function and that channel kinetics shape temporal signal processing in dendrites.

Objective. Brain–computer interfaces (BCIs) aim to provide a means for people with severe motor disabilities to control their environment directly with neural activity. In intracortical BCIs for people with tetraplegia, the decoder that maps neural activity to desired movements has typically been calibrated using ‘open-loop’ (OL) imagination of control while a cursor automatically moves to targets on a computer screen. However, because neural activity can vary across contexts, a decoder calibrated using OL data may not be optimal for ‘closed-loop’ (CL) neural control. Here, we tested whether CL calibration creates a better decoder than OL calibration even when all other factors that might influence performance are held constant, including the amount of data used for calibration and the amount of elapsed time between calibration and testing. Approach. Two people with tetraplegia enrolled in the BrainGate2 pilot clinical trial performed a center-out-back task using an intracortical BCI, switching between decoders that had been calibrated on OL versus CL data. Main results. Even when all other variables were held constant, CL calibration improved neural control as well as the accuracy and strength of the tuning model. Updating the CL decoder using additional and more recent data resulted in further improvements. Significance. Differences in neural activity between OL and CL contexts contribute to the superiority of CL decoders, even prior to their additional ‘adaptive’ advantage. In the near future, CL decoder calibration may enable robust neural control without needing to pause ongoing, practical use of BCIs, an important step toward clinical utility.