Biologically plausible neural computation

Neurons with Multiplicative Interactions of Nonlinear Synapses

Neurons are the fundamental units of the brain and nervous system. Developing a good modeling of human neurons is very important not only to neurobiology but also to computer science and many other fields. The McCulloch and Pitts neuron model is the most widely used neuron model, but has long been criticized as being oversimplified in view of properties of real neuron and the computations they perform. On the other hand, it has become widely accepted that dendrites play a key role in the overall computation performed by a neuron. However, the modeling of the dendritic computations and the assignment of the right synapses to the right dendrite remain open problems in the field. Here, we propose a novel dendritic neural model (DNM) that mimics the essence of known nonlinear interaction among inputs to the dendrites. In the model, each input is connected to branches through a distance-dependent nonlinear synapse, and each branch performs a simple multiplication on the inputs. The soma then sums the weighted products from all branches and produces the neuron’s output signal. We show that the rich nonlinear dendritic response and the powerful nonlinear neural computational capability, as well as many known neurobiological phenomena of neurons and dendrites, may be understood and explained by the DNM. Furthermore, we show that the model is capable of learning and developing an internal structure, such as the location of synapses in the dendritic branch and the type of synapses, that is appropriate for a particular task — for example, the linearly nonseparable problem, a real-world benchmark problem — Glass classification and the directional selectivity problem.

Modeling the N400 ERP component as transient semantic over-activation within a neural network model of word comprehension

The study of the N400 event-related brain potential has provided fundamental insights into the nature of real-time comprehension processes, and its amplitude is modulated by a wide variety of stimulus and context factors. It is generally thought to reflect the difficulty of semantic access, but formulating a precise characterization of this process has proved difficult. Laszlo and colleagues (Laszlo & Plaut, 2012; Laszlo & Armstrong, 2014) used physiologically constrained neural networks to model the N400 as transient over-activation within semantic representations, arising as a consequence of the distribution of excitation and inhibition within and between cortical areas. The current work extends this approach to successfully model effects on both N400 amplitudes and behavior of word frequency, semantic richness, repetition, semantic and associative priming, and orthographic neighborhood size. The account is argued to be preferable to one based on "implicit semantic prediction error" (Rabovsky & McRae, 2014) for a number of reasons, the most fundamental of which is that the current model actually produces N400-like waveforms in its real-time activation dynamics.

Stimulus-dependent refractoriness in the Frankenhaeuser-Huxley model

Phenomenological neural models, such as the leaky integrate-and-fire model, normally have a fixed refractory time-course that is independent of the stimulus. The recovery of threshold following an action potential is typically based on physiological experiments that use a two-pulse paradigm in which the first pulse is suprathreshold and causes excitation and the second pulse is used to determine the threshold at various intervals following the first. In such experiments, the nerve is completely unstimulated between the two pulses. This contrasts the receptor stimuli in normal physiological systems and the electrical stimuli used by cochlear implants and other neural prostheses. A numerical study of the Frankenhaeuser-Huxley conductance-based model of nerve fibre was therefore undertaken to investigate the effect of stimulation on refractoriness. We found that the application of a depolarizing stimulus during the later part of what is classically regarded as the absolute refractory period could effectively prolong the absolute refractory period, while leaving the refractory time-constants and other refractory parameters largely unaffected. Indeed, long depolarizing pulses, which would have been suprathreshold if presented to a resting nerve fibre, appeared to block excitation indefinitely. Stimulation during what is classically regarded as the absolute refractory period can therefore greatly affect the temporal response of a nerve. We conclude that the classical definition of absolute refractory period should be refined to include only the initial period following an action potential when an ongoing stimulus would not affect threshold; this period was found to be about half as long as the classical absolute refractory period. We further conclude that the stimulus-dependent nature of the relative refractory period must be considered when developing a phenomenological nerve model for complex stimuli.

PSPs and ERPs: applying the dynamics of post-synaptic potentials to individual units in simulation of temporally extended Event-Related Potential reading data

The Parallel Distributed Processing (PDP) framework is built on neural-style computation, and is thus well-suited for simulating the neural implementation of cognition. However, relatively little cognitive modeling work has concerned neural measures, instead focusing on behavior. Here, we extend a PDP model of reading-related components in the Event-Related Potential (ERP) to simulation of the N400 repetition effect. We accomplish this by incorporating the dynamics of cortical post-synaptic potentials--the source of the ERP signal--into the model. Simulations demonstrate that application of these dynamics is critical for model elicitation of repetition effects in the time and frequency domains. We conclude that by advancing a neurocomputational understanding of repetition effects, we are able to posit an interpretation of their source that is both explicitly specified and mechanistically different from the well-accepted cognitive one.

Self-repair in a bidirectionally coupled astrocyte-neuron (AN) system based on retrograde signaling

In this paper we demonstrate that retrograde signaling via astrocytes may underpin self-repair in the brain. Faults manifest themselves in silent or near silent neurons caused by low transmission probability (PR) synapses; the enhancement of the transmission PR of a healthy neighboring synapse by retrograde signaling can enhance the transmission PR of the "faulty" synapse (repair). Our model of self-repair is based on recent research showing that retrograde signaling via astrocytes can increase the PR of neurotransmitter release at damaged or low transmission PR synapses. The model demonstrates that astrocytes are capable of bidirectional communication with neurons which leads to modulation of synaptic activity, and that indirect signaling through retrograde messengers such as endocannabinoids leads to modulation of synaptic transmission PR. Although our model operates at the level of cells, it provides a new research direction on brain-like self-repair which can be extended to networks of astrocytes and neurons. It also provides a biologically inspired basis for developing highly adaptive, distributed computing systems that can, at fine levels of granularity, fault detect, diagnose and self-repair autonomously, without the traditional constraint of a central fault detect/repair unit.

Bidirectional coupling between astrocytes and neurons mediates learning and dynamic coordination in the brain: a multiple modeling approach

In recent years research suggests that astrocyte networks, in addition to nutrient and waste processing functions, regulate both structural and synaptic plasticity. To understand the biological mechanisms that underpin such plasticity requires the development of cell level models that capture the mutual interaction between astrocytes and neurons. This paper presents a detailed model of bidirectional signaling between astrocytes and neurons (the astrocyte-neuron model or AN model) which yields new insights into the computational role of astrocyte-neuronal coupling. From a set of modeling studies we demonstrate two significant findings. Firstly, that spatial signaling via astrocytes can relay a "learning signal" to remote synaptic sites. Results show that slow inward currents cause synchronized postsynaptic activity in remote neurons and subsequently allow Spike-Timing-Dependent Plasticity based learning to occur at the associated synapses. Secondly, that bidirectional communication between neurons and astrocytes underpins dynamic coordination between neuron clusters. Although our composite AN model is presently applied to simplified neural structures and limited to coordination between localized neurons, the principle (which embodies structural, functional and dynamic complexity), and the modeling strategy may be extended to coordination among remote neuron clusters.

Neural processing of counting in evolved spiking and McCulloch-Pitts agents

This article investigates the evolution of autonomous agents that perform a memory-dependent counting task. Two types of neurocontrollers are evolved: networks of McCulloch-Pitts neurons, and spiking integrate-and-fire networks. The results demonstrate the superiority of the spiky model in evolutionary success and network simplicity. The combination of spiking dynamics with incremental evolution leads to the successful evolution of agents counting over very long periods. Analysis of the evolved networks unravels the counting mechanism and demonstrates how the spiking dynamics are utilized. Using new measures of spikiness we find that even in agents with spiking dynamics, these are usually truly utilized only when they are really needed, that is, in the evolved subnetwork responsible for counting.

Discussion on the reverberatory model of short-term memory: a computational approach

Up to now a number of models have been proposed to underlie memory formation in the central nervous system. Two of these models are the reverberatory circuit model and the other one the self-feedback loop model. This paper considers these two models regarding their ability to preserve neural activity and to hold information. In the self-feedback loop model, the activity level of the loop output is computed regarding the short lasting initial input. In the reverberatory circuit model, the activity levels of the proposed two-layer network outputs were computed regarding the short lasting initial inputs of the network. In the self-feedback loop model, the activity level of the loop output changes with each reverberation until it reaches a specific limit and then remains at that level. In the reverberatory circuit model, the activity levels of the proposed two-layer network outputs display an oscillatory behavior. These models can preserve the input activity, but they change its level with each reverberation. Information carried by a single neuron is related to its activity level. Therefore these models change the information during the reverberation. Short-term memory must hold the information for a certain period of time, so these models cannot be proposed to underlie short-term memory formation.

Magnetoencephalographic evidence of the interhemispheric asymmetry in echoic memory lifetime and its dependence on handedness and gender

The echoic memory trace (EMT) refers to neuronal activity associated with the short-term retention of stimulus-related information, especially within the primary and association auditory cortex. Using magnetoencephalography it is possible to determine quantitatively the lifetime of the EMT. Previous studies assumed that each new stimulus drives the EMT to its full strength, which then passively decays. In this study we show the limitations of this assumption using trains of auditory stimuli designed specifically for computing the EMT lifetime and its contextual sensitivity. We estimated a time-dependent EMT using a data-driven approach, which allows contributions from a relatively wide area around the auditory cortex in our quantitative measures. We identified: (1) internally generated cortical activations during the silent period between stimuli well separated in time from each other, which had influence on the morphology of the neuromagnetic response to the next external stimulus; and (2) EMTs with different lifetimes that modulate the amplitude of the evoked responses at different latencies, suggesting the existence of multiple neural delay lines. Long EMT lifetimes were observed on the descending part of the M100 complex, which showed handedness and gender-dependent interhemispheric asymmetry. Specifically, all subjects showed longer EMT lifetimes on the left hemisphere, except left-handed males. Distributed source analysis of the data for one left- and one right-handed male subject identified a secondary generator in the right-handed subject, which was located posterior to the early primary generator and dominated the auditory response at late latencies, where EMT lifetime asymmetry was high. The identified multiple neural delay lines and their laterality may provide a link between macroneuronal activity and left hemisphere specialization for processing linguistic material.

Modelling relative recency discrimination tasks using a stochastic working memory model

Relative recency discrimination (RD) task is typically used to assess the temporal organization function of the prefrontal cortex (PFC). Subjects look at a series of cards (with words or drawings on them) and on seeing a test card determine which of the two items was seen more recently. Results show that patients with damage to the prefrontal cortex are severely impaired on this task. We propose a memory trace-priming mechanism, based on automatic time-marking process hypothesis (Schacter, D.L., 1987. Memory, amnesia, and frontal lobe dysfunction: a critique and interpretation. Psychobiology 15, 21-36), to offer a computational account of the results. In this model, successive words seen by subjects leave decaying memory traces in PFC, which subsequently prime the representations in higher sensory areas such as inferior temporal Cortex (IT) during discrimination judgements. The paper focuses on the evaluation of a probabilistic pre-frontal trace mechanism using a pool of clusters of neurons with self-sustained firing that ends at a random time. The results show that the probabilistic behavior of subjects can be accounted for by the stochasticity of the trace model. A good fit to experimental data is obtained with a PFC memory persistence probability with a decay time constant of tau = approximately 30 s. The model allows for a distributed representation in IT and PFC, but the best fit suggests a sparse representation. It is concluded that further data are needed on representations in IT and PFC, on the connectivity between these two areas, and on the statistical and dynamic properties of memory neurons in PFC.

Computational analyses in cognitive neuroscience: in defense of biological implausibility

Because cognitive neuroscience researchers attempt to understand the human mind by bridging behavior and brain, they expect computational analyses to be biologically plausible. In this paper, biologically implausible computational analyses are shown to have critical and essential roles in the various stages and domains of cognitive neuroscience research. Specifically, biologically implausible computational analyses can contribute to (1) understanding and characterizing the problem that is being studied, (2) examining the availability of information and its representation, and (3) evaluating and understanding the neuronal solution. In the context of the distinct types of contributions made by certain computational analyses, the biological plausibility of those analyses is altogether irrelevant. These biologically implausible models are nevertheless relevant and important for biologically driven research.

Towards a neural model of timing

This paper describes a neural model of interval timing, which reproduces the duration discrimination experiments of Wearden, J.H., 1992, J. Exp. Psychol. 18, 134-144. The model comprises three layers of neural units. The units in the first layer represent clusters of neurons with probabilistic internal feedback that maintains self-sustained (short-term memory) activity for a random time. The unit in the second layer is a spiking neuron that fires as long as a sufficient number of input clusters are active. The unit in the third layer detects the offset of firing in the previous layer by producing a short burst of spikes. Analysis and simulation of the model shows spikes produced at random times with a distribution determined by the number of units in the first layer, their survival time constant, and the threshold of the unit in layer 2. Interval times can be learned with any of these parameters but lead to different Weber law relations. A variable threshold in layer 2 predicts S-shaped Weber curves, a variable number of units in layer 1 leads to a saturation of the Weber curve (decreasing Weber fraction) and a variable time constant in layer 1 causes a linear Weber curve.