Multiscale relevance and informative encoding in neuronal spike trains

Neuronal responses to complex stimuli and tasks can encompass a wide range of time scales. Understanding these responses requires measures that characterize how the information on these response patterns are represented across multiple temporal resolutions. In this paper we propose a metric -- which we call multiscale relevance (MSR) -- to capture the dynamical variability of the activity of single neurons across different time scales. The MSR is a non-parametric, fully featureless indicator in that it uses only the time stamps of the firing activity without resorting to any a priori covariate or invoking any specific structure in the tuning curve for neural activity. When applied to neural data from the mEC and from the ADn and PoS regions of freely-behaving rodents, we found that neurons having low MSR tend to have low mutual information and low firing sparsity across the correlates that are believed to be encoded by the region of the brain where the recordings were made. In addition, neurons with high MSR contain significant information on spatial navigation and allow to decode spatial position or head direction as efficiently as those neurons whose firing activity has high mutual information with the covariate to be decoded and significantly better than the set of neurons with high local variations in their interspike intervals. Given these results, we propose that the MSR can be used as a measure to rank and select neurons for their information content without the need to appeal to any a priori covariate.Comment: 38 pages, 16 figure

Decoding the Brain: Neural Representation and the Limits of Multivariate Pattern Analysis in Cognitive Neuroscience

Since its introduction, multivariate pattern analysis (MVPA), or “neural decoding”, has transformed the field of cognitive neuroscience. Underlying its influence is a crucial inference, which we call the Decoder’s Dictum: if information can be decoded from patterns of neural activity, then this provides strong evidence about what information those patterns represent. Although the Dictum is a widely held and well-motivated principle in decoding research, it has received scant philosophical attention. We critically evaluate the Dictum, arguing that it is false: decodability is a poor guide for revealing the content of neural representations. However, we also suggest how the Dictum can be improved on, in order to better justify inferences about neural representation using MVPA

Nonlinear computations in spiking neural networks through multiplicative synapses

The brain efficiently performs nonlinear computations through its intricate networks of spiking neurons, but how this is done remains elusive. While nonlinear computations can be implemented successfully in spiking neural networks, this requires supervised training and the resulting connectivity can be hard to interpret. In contrast, the required connectivity for any computation in the form of a linear dynamical system can be directly derived and understood with the spike coding network (SCN) framework. These networks also have biologically realistic activity patterns and are highly robust to cell death. Here we extend the SCN framework to directly implement any polynomial dynamical system, without the need for training. This results in networks requiring a mix of synapse types (fast, slow, and multiplicative), which we term multiplicative spike coding networks (mSCNs). Using mSCNs, we demonstrate how to directly derive the required connectivity for several nonlinear dynamical systems. We also show how to carry out higher-order polynomials with coupled networks that use only pair-wise multiplicative synapses, and provide expected numbers of connections for each synapse type. Overall, our work demonstrates a novel method for implementing nonlinear computations in spiking neural networks, while keeping the attractive features of standard SCNs (robustness, realistic activity patterns, and interpretable connectivity). Finally, we discuss the biological plausibility of our approach, and how the high accuracy and robustness of the approach may be of interest for neuromorphic computing.Comment: This article has been peer-reviewed and recommended by Peer Community In Neuroscienc

The Redemption of Noise: Inference with Neural Populations.

In 2006, Ma et al. (Nat. Neurosci. 1006;9:1432-1438) presented an elegant theory for how populations of neurons might represent uncertainty to perform Bayesian inference. Critically, according to this theory, neural variability is no longer a nuisance, but rather a vital part of how the brain encodes probability distributions and performs computations with them.ERC Consolidator Grant (726090-COGTOM

Value-related neuronal responses in the human amygdala during observational learning

The amygdala plays an important role in many aspects of social cognition and reward learning. Here, we aimed to determine whether human amygdala neurons are involved in the computations necessary to implement learning through observation. We performed single-neuron recordings from the amygdalae of human neurosurgical patients (male and female) while they learned about the value of stimuli through observing the outcomes experienced by another agent interacting with those stimuli. We used a detailed computational modeling approach to describe patients' behavior in the task. We found a significant proportion of amygdala neurons whose activity correlated with both expected rewards for oneself and others, and in tracking outcome values received by oneself or other agents. Additionally, a population decoding analysis suggests the presence of information for both observed and experiential outcomes in the amygdala. Encoding and decoding analyses suggested observational value coding in amygdala neurons occurred in a different subset of neurons than experiential value coding. Collectively, these findings support a key role for the human amygdala in the computations underlying the capacity for learning through observation

Evidence against the Detectability of a Hippocampal Place Code Using Functional Magnetic Resonance Imaging

Individual hippocampal neurons selectively increase their firing rates in specific spatial locations. As a population, these neurons provide a decodable representation of space that is robust against changes to sensory- and path-related cues. This neural code is sparse and distributed, theoretically rendering it undetectable with population recording methods such as functional magnetic resonance imaging (fMRI). Existing studies nonetheless report decoding spatial codes in the human hippocampus using such techniques. Here we present results from a virtual navigation experiment in humans in which we eliminated visual- and path-related confounds and statistical limitations present in existing studies, ensuring that any positive decoding results would represent a voxel-place code. Consistent with theoretical arguments derived from electrophysiological data and contrary to existing fMRI studies, our results show that although participants were fully oriented during the navigation task, there was no statistical evidence for a place code

Spatio-temporally efficient coding: A computational principle of biological neural networks

Department of Biomedical Engineering (Human Factors Engineering)One of the major goals of neuroscience is to understand how the external world is represented in the brain. This is a neural coding problem: the coding from the external world to its neural representations. There are two different kinds of problems with neural coding. One is to study the types of neuronal activity that represent the external world. Representative examples here are rate coding and temporal coding. In this study, we will present the spike distance method that reads temporal coding-related information from neural data. Another is to study what principles make such neural representations possible. This is an approach to the computational principle and the main topic of the present study. The brain sensory system has hierarchical structures. It is important to find the principles assigning functions to the hierarchical structures. On the one hand, the hierarchical structures of the brain sensory system contain both bottom-up and top-down pathways. In this bidirectional hierarchical structure, two types of neuronal noise are generated. One of them is noise generated as neural information fluctuates across the hierarchy according to the initial condition of the neural response, even if the external sensory input is static. Another is noise, precisely error, caused by coding different information in each hierarchy because of the transmission delay of information when external sensory input is dynamic. Despite these noise problems, it seems that sensory information processing is performed without any major problems in the sensory system of the real brain. Therefore, a neural coding principle that can overcome these noise problems is neededHow can the brain overcome these noise problems? Efficient coding is one of representative neural coding principles, however, existing efficient coding does not take into account these noise problems. To treat these noise problems, as one of efficient coding principles, we devised spatio-temporal efficient coding, which was inspired by the efficient use of given space and time resources, to optimize bidirectional information transmission on the hierarchical structures. This optimization is to learn smooth neural responses on time domain. In simulations, we showed spatio-temporal efficient coding was able to solve above two noise problems. We expect that spatio-temporal efficient coding helps us to understand how the brain computes.ope