Estimating null and potent modes of feedforward communication in a computational model of cortical activity

Communication across anatomical areas of the brain is key to both sensory and motor processes. Dimensionality reduction approaches have shown that the covariation of activity across cortical areas follows well-delimited patterns. Some of these patterns fall within the "potent space" of neural interactions and generate downstream responses; other patterns fall within the "null space" and prevent the feedforward propagation of synaptic inputs. Despite growing evidence for the role of null space activity in visual processing as well as preparatory motor control, a mechanistic understanding of its neural origins is lacking. Here, we developed a mean-rate model that allowed for the systematic control of feedforward propagation by potent and null modes of interaction. In this model, altering the number of null modes led to no systematic changes in firing rates, pairwise correlations, or mean synaptic strengths across areas, making it difficult to characterize feedforward communication with common measures of functional connectivity. A novel measure termed the null ratio captured the proportion of null modes relayed from one area to another. Applied to simultaneous recordings of primate cortical areas V1 and V2 during image viewing, the null ratio revealed that feedforward interactions have a broad null space that may reflect properties of visual stimuli.

Dynamical Latent State Computation in the Posterior Parietal Cortex

Success in many real-world tasks depends on our ability to dynamically track hidden states of the world. To understand the underlying neural computations, we recorded brain activity in posterior parietal cortex (PPC) of monkeys navigating by optic flow to a hidden target location within a virtual environment, without explicit position cues. In addition to sequential neural dynamics and strong interneuronal interactions, we found that the hidden state -- monkey's displacement from the goal -- was encoded in single neurons, and could be dynamically decoded from population activity. The decoded estimates predicted navigation performance on individual trials. Task manipulations that perturbed the world model induced substantial changes in neural interactions, and modified the neural representation of the hidden state, while representations of sensory and motor variables remained stable. The findings were recapitulated by a task-optimized recurrent neural network model, suggesting that neural interactions in PPC embody the world model to consolidate information and track task-relevant hidden states.

Sense Element Engagement Theory Explains How Neural Networks Produce Cortical Prosthetic Vision

<p>Demonstrating that an understanding of how neural networks produce a specific quality of experience has been achieved would provide a foundation for new research programs and neurotechnologies. The phenomena that comprise cortical prosthetic vision have two desirable properties for the pursuit of this goal: 1) Models of the subjective qualities of cortical prosthetic vision can be constructed; and 2) These models can be related in a natural way to models of the objective aspects of cortical prosthetic vision. Sense element engagement theory portrays the qualities of cortical prosthetic vision together with coordinated objective neural phenomena as constituting sensible spatiotemporal patterns that are produced by neural interactions. Small-scale neural network simulations are used to illustrate how these patterns are thought to arise. It is proposed that simulations and an electronic neural network (ENN) should be employed in devising tests of the theory. Large-scale simulations can provide estimates of parameter values that are required to construct an ENN. The ENN will be used to develop a prosthetic device that is predicted by the theory to produce visual forms in a novel fashion. According to the theory, confirmation of this prediction would also provide evidence that this ENN is a sentient device.</p>

Sense Element Engagement Theory Explains How Neural Networks Produce Cortical Prosthetic Vision

<p>Demonstrating that an understanding of how neural networks produce a specific quality of experience has been achieved would provide a foundation for new research programs and neurotechnologies. The phenomena that comprise cortical prosthetic vision have two desirable properties for the pursuit of this goal: 1) Models of the subjective qualities of cortical prosthetic vision can be constructed; and 2) These models can be related in a natural way to models of the objective aspects of cortical prosthetic vision. Sense element engagement theory portrays the qualities of cortical prosthetic vision together with coordinated objective neural phenomena as constituting sensible spatiotemporal patterns that are produced by neural interactions. Small-scale neural network simulations are used to illustrate how these patterns are thought to arise. It is proposed that simulations and an electronic neural network (ENN) should be employed in devising tests of the theory. Large-scale simulations can provide estimates of parameter values that are required to construct an ENN. The ENN will be used to develop a prosthetic device that is predicted by the theory to produce visual forms in a novel fashion. According to the theory, confirmation of this prediction would also provide evidence that this ENN is a sentient device.</p>

The structure of hippocampal CA1 interactions optimizes spatial coding across experience

Although much is known about how single neurons in the hippocampus represent an animal’s position, how cell-cell interactions contribute to spatial coding remains poorly understood. Using a novel statistical estimator and theoretical modeling, both developed in the framework of maximum entropy models, we reveal highly structured cell-to-cell interactions whose statistics depend on familiar vs. novel environment. In both conditions the circuit interactions optimize the encoding of spatial information, but for regimes that differ in the signal-to-noise ratio of their spatial inputs. Moreover, the topology of the interactions facilitates linear decodability, making the information easy to read out by downstream circuits. These findings suggest that the efficient coding hypothesis is not applicable only to individual neuron properties in the sensory periphery, but also to neural interactions in the central brain.

Perineural Invasion and Associated Pain Transmission in Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is one of the cancers with the highest incidence of perineural invasion (PNI), which often indicates a poor prognosis. Aggressive tumor cells invade nerves, causing neurogenic inflammation; the tumor microenvironment also induces nerves to undergo a series of structural and functional reprogramming. In turn, neurons and the surrounding glial cells promote the development of pancreatic cancer through autocrine and/or paracrine signaling. In addition, hyperalgesia in PDAC patients implies alterations of pain transmission in the peripheral and central nervous systems. Currently, the studies on this topic are relatively limited. This review will elaborate on the mechanisms of tumor–neural interactions and its possible relationship with pain from several aspects that have been focused on in recent years.

Classification of Trauma-Related Outcomes in US Veterans Using Magnetoencephalography (MEG)

Previous research has demonstrated highly accurate classification of veterans with posttraumatic stress disorder (PTSD) and controls based on synchronous neural interactions (SNI), highlighting the utility of SNI as a biomarker of PTSD. Here we extend that research to classify additional trauma-related outcomes including subthreshold PTSD, partial recovery, and full recovery according to SNI. A total of 219 U.S. veterans completed diagnostic interviews and underwent a magnetoencephalography (MEG) scan from which SNI was computed. Linear discriminant analysis was used to classify the PTSD and control brains, achieving 100% accuracy. That discriminant function was then used to classify each brain in the subthreshold PTSD, partial recovery, and full recovery diagnostic groups as PTSD or Control. All of the subthreshold PTSD diagnostic group were classified as PTSD, as were three-quarters of the partial recovery group. Findings regarding the full recovery group were mixed, documenting variability in the functional brain status of PTSD recovery. The results of the present study add to the literature supporting the discriminatory power of MEG SNI and demonstrate the utility of SNI as a biomarker of various PTSD-related trajectories.

Bilateral Interference in Motor Performance in Homologous vs. Non-homologous Proximal and Distal Effectors

Performance of bimanual motor actions requires coordinated and integrated bilateral communication, but in some bimanual tasks, neural interactions and crosstalk might cause bilateral interference. The level of interference probably depends on the proportions of bilateral interneurons connecting homologous areas of the motor cortex in the two hemispheres. The neuromuscular system for proximal muscles has a higher number of bilateral interneurons connecting homologous areas of the motor cortex compared to distal muscles. Based on the differences in neurophysiological organization for proximal vs. distal effectors in the upper extremities, the purpose of the present experiment was to evaluate how the level of bilateral interference depends on whether the bilateral interference task is performed with homologous or non-homologous effectors as the primary task. Fourteen participants first performed a unilateral primary motor task with the dominant arm with (1) proximal and (2) distal controlled joysticks. Performance in the unilateral condition with the dominant arm was compared to the same effector’s performance when two different bilateral interference tasks were performed simultaneously with the non-dominant arm. The two different bilateral interference tasks were subdivided into (1) homologous and (2) non-homologous effectors. The results showed a significant decrease in performance for both proximal and distal controlled joysticks, and this effect was independent of whether the bilateral interference tasks were introduced with homologous or non-homologous effectors. The overall performance decrease as a result of bilateral interference was larger for proximal compared to distal controlled joysticks. Furthermore, a proximal bilateral interference caused a larger performance decrement independent of whether the primary motor task was controlled by a proximal or distal joystick. A novel finding was that the distal joystick performance equally interfered with either homologous (distal bilateral interference) or non-homologous (proximal bilateral interference) interference tasks performed simultaneously. The results indicate that the proximal–distal distinction is an important organismic constraint on motor control and for understanding bilateral communication and interference in general and, in particular, how bilateral interference caused by homologous vs. non-homologous effectors impacts motor performance for proximal and distal effectors. The results seem to map neuroanatomical and neurophysiological differences for these effectors.

Dynamical differential covariance recovers directional network structure in multiscale neural systems

Investigating causal neural interactions are essential to understanding sub- sequent behaviors. Many statistical methods have been used for analyzing neural activity, but efficiently and correctly estimating the direction of net- work interactions remains difficult. Here, we derive dynamical differential covariance (DDC), a new method based on dynamical network models that detects directional interactions with low bias and high noise tolerance with- out the stationary assumption. The method is first validated on networks with false positive motifs and multiscale neural simulations where the ground truth connectivity is known. Then, applying DDC to recordings of resting-state functional magnetic resonance imaging (rs-fMRI) from over 1,000 individual subjects, DDC consistently detected regional interactions with strong structural connectivity. DDC can be generalized to a wide range of dynamical models and recording techniques.