Search

In this paper we introduce some mathematical and numerical tools to analyze and interpret inhomogeneous quadratic forms. The resulting characterization is in some aspects similar to that given by experimental studies of cortical cells, making it particularly suitable for application to second-order approximations and theoretical models of physiological receptive fields. We first discuss two ways of analyzing a quadratic form by visualizing the coefficients of its quadratic and linear term directly and by considering the eigenvectors of its quadratic term. We then present an algorithm to compute the optimal excitatory and inhibitory stimuli, i.e. the stimuli that maximize and minimize the considered quadratic form, respectively, given a fixed energy constraint. The analysis of the optimal stimuli is completed by considering their invariances, which are the transformations to which the quadratic form is most insensitive. We introduce a test to determine which of these are statistically significant. Next we propose a way to measure the relative contribution of the quadratic and linear term to the total output of the quadratic form. Furthermore, we derive simpler versions of the above techniques in the special case of a quadratic form without linear term and discuss the analysis of such functions in previous theoretical and experimental studies. In the final part of the paper we show that for each quadratic form it is possible to build an equivalent two-layer neural network, which is compatible with (but more general than) related networks used in some recent papers and with the energy model of complex cells. We show that the neural network is unique only up to an arbitrary orthogonal transformation of the excitatory and inhibitory subunits in the first layer.

We examine a simple biologically-motivated neural network, the three-layer version of the Chialvo-Bak `minibrain' [Neurosci. 90 (1999) 1137], and present numerical results which indicate that a non-equilibrium phase transition between ordered and disordered phases occurs subject to the tuning of a control parameter. Scale-free behaviour is observed at the critical point. Notably, the transition here is due solely to network geometry and not any noise factor. The phase of the network is thus a design parameter which can be tuned. The phases are determined by differing levels of interference between active paths in the network and the consequent accidental destruction of good paths., Comment: 10 pages, 5 figures. Version accepted for publication in Physica A

Sound localization is a vital aspect of hearing for safe navigation of everyday environments. It is also an important factor in speech intelligibility. This ability is facilitated by the interaural level difference (ILD) cue, which arises from binaural hearing: a sound will be more intense at the nearer ear than the farther. In a hearing-impaired listener, this binaural cue may not be available for use and localization may be diminished. While conventional, bilateral, wide dynamic range compression (WDRC) hearing aids distort the interaural level difference by independently altering sound intensities in each ear, wirelessly-linked devices have been suggested to benefit this task by matching amplification in order to preserve ILD. However, this technology has been shown to have varying degrees of success in aiding speech intelligibility and sound localization. As hearing impairment has wide-ranging adverse impacts to physical and mental health, social activity, and cognition, the task of localization improvement must be urgently addressed. Toward this end, neural modelling techniques are used to determine neural representations of ILD cues for linked and unlinked bilateral WDRC hearing aids. Findings suggest that wirelessly-linked WDRC is preferable over unlinked hearing aids or unaided, hearing-impaired listening, although parameters for optimal benefit are dependent on sound level, frequency content, and preceding sounds. Thesis Master of Applied Science (MASc)

The prevalence of coherent oscillations in various frequency ranges in the central nervous system raises the question of the mechanisms that synchronize large populations of neurons. We study synchronization in models of large networks of spiking neurons with random sparse connectivity. Synchrony occurs only when the average number of synapses, M that a cell receives is larger than a critical value, $M_c$. Below $M_c$, the system is in an asynchronous state. In the limit of weak coupling, assuming identical neurons, we reduce the model to a system of phase oscillators which are coupled via an effective interaction, $\Gamma$. In this framework, we develop an approximate theory for sparse networks of identical neurons to estimate $M_c$ analytically from the Fourier coefficients of $\Gamma$. Our approach relies on the assumption that the dynamics of a neuron depend mainly on the number of cells that are presynaptic to it. We apply this theory to compute $M_c$ for the integrate-and-fire (\IF) model as a function of the intrinsic neuronal properties (\eg the refractory period $T_r$), the synaptic time constants and the strength of the external stimulus, $\Iapp$. When the neurons are inhibitory, $M_c$ is found to be non-monotonous with the strength of $\Iapp$. For $T_r=0$, we estimate the minimum value of $M_c$ over all the parameters of the model to be $363.8$. Above $M_c$, the neurons tend to fire in: 1) smeared one cluster states at high firing rates and 2) smeared two or more cluster states at low firing rates. For excitatory interactions synchrony can be achieved only if the firing rate is not too high. However, our estimates of $M_c$ are, in general, much smaller than for inhibitory networks for similar level of activity. Above $M_c$ excitatory networks settle into smeared 1-cluster states.Refractoriness decreases $M_c$ at intermediate and high firing rates. These results are compared against numerical simulations. We show numerically that systems with different sizes, $N$, behave in the same way provided the connectivity, $M$, is such a way that $1/ \Meff = 1 / M - 1 / N$ remains constant when $N$ varies. This allows one to extrapolate the large $N$ behavior of a network from numerical simulations of networks of relatively small sizes ($N=800$ in our case). We find that our theory predicts with remarkable accuracy the value of $M_c$ and the patterns of synchrony above $M_c$, provided the synaptic coupling is not too large. We also study the strong coupling regime of inhibitory sparse networks. All of our simulations demonstrate that increasing the coupling strength reduces the level of synchrony of the neuronal activity. Above a critical coupling strength, the network activity is asynchronous. We point out that there is a fundamental limitation for the mechanisms of synchrony relying on inhibition alone, if heterogeneities in the intrinsic properties of the neurons and spatial fluctuations in the external input are also taken into account.

The structural complexity of culture cannot be characterized by simply modeling cultural beliefs or inherited ideas. Formal computational and algorithmic models of culture have focused on the inheritance of discrete cultural units, which can be hard to define and map to practical contexts. In cultural anthropology, research involving structuralist and post-structuralist perspectives have helped us better understand culturally-dependent classification systems and oppositional phenomena (e.g. light-dark, hot-cold, good-evil). Contemporary research in cognitive neuroscience suggests that complementary sets may be represented dynamically in the brain, but no model for the evolution of these sets has of yet been proposed. To fill this void, a method for simulating cultural or other highly symbolic behaviors called contextual geometric structures will be introduced. The contextual geometric structures approach is based on a hybrid model that approximates both individual/group cultural practice and a fluctuating environment. The hybrid model consists of two components. The first is a set of discrete automata with a soft classificatory structure. These automata are then embedded in a Lagrangian-inspired particle simulation that defines phase space relations and environmental inputs. The concept of conditional features and equations related to diversity, learning, and forgetting are used to approximate the goal-directed and open-ended features of cultural-related emergent behavior. This allows cultural patterns to be approximated in the context of both stochastic and deterministic evolutionary dynamics. This model can yield important information about multiple structures and social relationships, in addition to phenomena related to sensory function and higher-order cognition observed in neural systems.

Metabolic-repair models, or (M,R)-systems were introduced in Relational Biology by Robert Rosen. Subsequently, Rosen represented such (M,R)-systems (or simply MRs)in terms of categories of sets, deliberately selected without any structure other than the discrete topology of sets. Theoreticians of life's origins postulated that Life on Earth has begun with the simplest possible organism, called the primordial. Mathematicians interested in biology attempted to answer this important question of the minimal living organism by defining the functional relations that would have made life possible in such a minimal system- a grandad and grandma of all living organisms on Earth.

S'ha investigat molt tant els mecanismes neuronals bàsics de l'audició com l'organització psicològica de la percepció de la parla. Tanmateix, en ambdós temes n'hi ha una relativa escassetat en quant a modelització. Aquí describim dos treballs de modelització. Un d'ells proposa un nou mecanisme de millora de selectivitat de freqüències que explica resultats de experiments neurofisiològics investigant manifestacions de forward masking y sobretot auditory streaming en l'escorça auditiva principal (A1). El mecanisme funciona en una xarxa feed-forward amb depressió sináptica entre el tàlem y l'escorça, però mostrem que és robust a l'introducció d'una organització realista del circuit de A1, que per la seva banda explica cantitat de dades neurofisiològics. L'altre treball descriu un mecanisme candidat d'explicar la trobada en estudis psicofísics de diferències en la percepció de paraules entre bilinguës primerencs y simultànis. Simulant tasques de decisió lèxica y discriminació de fonemes, fortifiquem l'hipòtesi de que persones sovint exposades a variacions dialectals de paraules poden guardar aquestes en el seu lèxic, sense alterar representacions fonemàtiques ., Though much experimental research exists on both basic neural mechanisms of hearing and the psychological organization of language perception, there is a relative paucity of modelling work on these subjects. Here we describe two modelling efforts. One proposes a novel mechanism of frequency selectivity improvement that accounts for results of neurophysiological experiments investigating manifestations of forward masking and above all auditory streaming in the primary auditory cortex (A1). The mechanism works in a feed-forward network with depressing thalamocortical synapses, but is further showed to be robust to a realistic organization of the neural circuitry in A1, which accounts for a wealth of neurophysiological data. The other effort describes a candidate mechanism for explaining differences in word/non-word perception between early and simultaneous bilinguals found in psychophysical studies. By simulating lexical decision and phoneme discrimination tasks in an attractor neural network model, we strengthen the hypothesis that people often exposed to dialectal word variations can store these in their lexicons, without altering their phoneme representations., Se ha investigado mucho tanto los mecanismos neuronales básicos de la audición como la organización psicológica de la percepción del habla. Sin embargo, en ambos temas hay una relativa escasez en cuanto a modelización. Aquí describimos dos trabajos de modelización. Uno propone un nuevo mecanismo de mejora de selectividad de frecuencias que explica resultados de experimentos neurofisiológicos investigando manifestaciones de forward masking y sobre todo auditory streaming en la corteza auditiva principal (A1). El mecanismo funciona en una red feed-forward con depresión sináptica entre el tálamo y la corteza, pero mostramos que es robusto a la introducción de una organización realista del circuito de A1, que a su vez explica cantidad de datos neurofisiológicos. El otro trabajo describe un mecanismo candidato de explicar el hallazgo en estudios psicofísicos de diferencias en la percepción de palabras entre bilinguës tempranos y simultáneos. Simulando tareas de decisión léxica y discriminación de fonemas, fortalecemos la hipótesis de que personas expuestas a menudo a variaciones dialectales de palabras pueden guardar éstas en su léxico, sin alterar representaciones fonémicas.

We extend a cognitive paradigm for gene expression based on the asymptotic limit theorems of information theory to the epigenetic epidemiology of mental disorders. In particular, we recognize the fundamental role culture plays in human biology, another heritage mechanism parallel to, and interacting with, the more familiar genetic and epigenetic systems. We do this via a model through which culture acts as another tunable epigenetic catalyst that both directs developmental trajectories, and becomes convoluted with individual ontology, via a mutually-interacting crosstalk mediated by a social interaction that is itself culturally driven. We call for the incorporation of embedding culture as an essential component of the epigenetic regulation of human mental development and its dysfunctions, bringing what is perhaps the central reality of human biology into the center of biological psychiatry. Current US work on gene-environment interactions in psychiatry must be extended to a model of gene-environment-culture interaction to avoid becoming victim of an extreme American individualism that threatens to create paradigms particular to that culture and that are, indeed, peculiar in the context of the world's cultures. The cultural and epigenetic systems of heritage may well provide the 'missing' heritability of complex diseases now under so much intense discussion.

Serial endosymbiosis theory provides a unifying paradigm for examining the interaction of cognitive modules at vastly different scales of biological, social, and cultural organization. A trivial but not unimportant model associates a dual information source with a broad class of cognitive processes, and punctuated phenomena akin to phase transitions in physical systems, and associated coevolutionary processes, emerge as consequences of the homology between information source uncertainty and free energy density. The dynamics, including patterns of punctuation similar to ecosystem resilience transitions, are largely dominated by the availability of 'Roman roads' constituting channels for the transmission of information between modules.

In this paper the problem of regressors selection in Virtual Instruments (VI) design is addressed The VI is designed to replace the on line analyzer of a Sulfur Recovery Unit (SRU) of a large refinery located in Sicily during maintenance operations. It is designed by using nonlinear MA models implemented by a MLP neural network. The use of a set of cross-correlation functions, proposed by Billings and Voon to evaluate the performance of nonlinear models is used to select the regressors of the discrete-time NMA model by implementing an automatic regressor selection algorithm. The designed Soft Sensor has been implemented at the refinery to be tested on line.

Our nervous system can efficiently recognize objects in spite of changes in contextual variables such as perspective or lighting conditions. Several lines of research have proposed that this ability for invariant recognition is learned by exploiting the fact that object identities typically vary more slowly in time than contextual variables or noise. Here, we study the question of how this “temporal stability” or “slowness” approach can be implemented within the limits of biologically realistic spike-based learning rules. We first show that slow feature analysis, an algorithm that is based on slowness, can be implemented in linear continuous model neurons by means of a modified Hebbian learning rule. This approach provides a link to the trace rule, which is another implementation of slowness learning. Then, we show analytically that for linear Poisson neurons, slowness learning can be implemented by spike-timing–dependent plasticity (STDP) with a specific learning window. By studying the learning dynamics of STDP, we show that for functional interpretations of STDP, it is not the learning window alone that is relevant but rather the convolution of the learning window with the postsynaptic potential. We then derive STDP learning windows that implement slow feature analysis and the “trace rule.” The resulting learning windows are compatible with physiological data both in shape and timescale. Moreover, our analysis shows that the learning window can be split into two functionally different components that are sensitive to reversible and irreversible aspects of the input statistics, respectively. The theory indicates that irreversible input statistics are not in favor of stable weight distributions but may generate oscillatory weight dynamics. Our analysis offers a novel interpretation for the functional role of STDP in physiological neurons., Author Summary Neurons interact by exchanging information via small connection sites, so-called synapses. Interestingly, the efficiency of synapses in transmitting neuronal signals is not static, but changes dynamically depending on the signals that the associated neurons emit. As neurons receive thousands of synaptic input signals, they can thus “choose” the input signals they are interested in by adjusting their synapses accordingly. This adaptation mechanism, known as synaptic plasticity, has long been hypothesized to form the neuronal correlate of learning. It raises a difficult question: what aspects of the input signals are the neurons interested in, given that the adaptation of the synapses follows a certain mechanistic rule? We address this question for spike-timing–dependent plasticity, a type of synaptic plasticity that has raised a lot of interest in the last decade. We show that under certain assumptions regarding neuronal information transmission, spike-timing–dependent plasticity focuses on aspects of the input signals that vary slowly in time. This relates spike-timing–dependent plasticity to a class of abstract learning rules that were previously proposed as a means of learning to recognize objects in spite of contextual changes such as size or position. Based on this link, we propose a novel functional interpretation of spike-timing–dependent plasticity.

This work deals with the design and implementation of soft sensors designed to perform the what-if analysis on a Sulfur Recovery Unit (SRU) in a refinery. Soft sensors are mathematical models able to emulate the behaviour of existing sensors on the basis of available measurements. This paper deals with the design of a soft sensor by using a nonlinear one step ahead model, eventually usable on-line. The realized models represented the building blocks for the design of the Virtual Instrument based on a multi steps ahead predictor to be used in the what-if analysis. The instrument allows the estimation the consequences that variations of the input trends produces on system outputs, to eventually improve control policy effectiveness.

Keywords: Learning by Imitation ; Neural Modelling Reference LASA-BOOK-2007-001 Record created on 2007-11-07, modified on 2017-05-10

The task of understanding how the brain works has met with only limited success since important design concepts are not as yet incorporated in the analysis. Relevant concepts can be uncovered by studying the powerful methodologies that have evolved in the context of computer programming, raising the question of how the concepts involved there can be realised in neural hardware. Insights can be gained in regard to such issues through the study of the role played by models and representation. These insights lead on to an appreciation of the mechanisms underlying subtle capacities such as those concerned with the use of language. A precise, essentially mathematical account of such capacities is in prospect for the future.

A forecast activity in the lowest layer of the atmosphere, well known for its strongly non-linear physics, is presented in this paper. The forecast method is mainly based on a neural network model, whose structure is briefly described. We stress that preprocessing allows us to extract the main periodicities and to train the network on a residual series of radon data: here the network itself is able to catch the hidden non-linear dynamics. Final results show the ability of the model to predict values of radon concentration and stable layer depth, which represent important physical information for air pollution forecasts near the surface.

Current methodologies in the neurosciences have difficulty in accounting for complex phenomena such as language, which can however be quite well characterised in phenomenological terms. This paper addresses the issue of unifying the two approaches. We typically understand complicated systems in terms of a collection of models, each characterisable in principle within a formal system, it being possible to explain higher-level properties in terms of lower level ones by means of a series of inferences based on these models. We consider the nervous system to be a mechanism for implementing the demands of an appropriate collection of models, each concerned with some aspect of brain and behaviour, the observer mechanism of Baas playing an important role in matching model and behaviour in this context. The discussion expounds these ideas in detail, showing their potential utility in connection with real problems of brain and behaviour, important areas where the ideas can be applied including the development of higher levels of abstraction, and linguistic behaviour, as described in the works of Karmiloff-Smith and Jackendoff respectively.

The corticothalamic feedback and the thalamic reticular nucleus have gained much attention lately because of their integrative and modulatory functions. A previous study by the author suggested that this circuitry can process analogies (i.e., the {\em analogy hypothesis}). In this paper, the proposed model was implemented as a network of leaky integrate-and-fire neurons to test the {\em analogy hypothesis}. The previous proposal required specific delay and temporal dynamics, and the implemented network tuned accordingly functioned as predicted. Furthermore, these specific conditions turn out to be consistent with experimental data, suggesting that a further investigation of the thalamocortical circuit within the {\em analogical framework} may be worthwhile.

We review recent developments in the measurement of the dynamics of the response properties of auditory cortical neurons to broadband sounds, which is closely related to the perception of timbre. The emphasis is on a method that characterizes the spectro-temporal properties of single neurons to dynamic, broadband sounds, akin to the drifting gratings used in vision. The method treats the spectral and temporal aspects of the response on an equal footing., Comment: 27 pages, 17 figures, straight LaTeX +psfig. Originally submitted to the neuro-sys archive which was never publicly announced (was 9804002)

We investigate the behavioral patterns of a population of agents, each controlled by a simple biologically motivated neural network model, when they are set in competition against each other in the Minority Model of Challet and Zhang. We explore the effects of changing agent characteristics, demonstrating that crowding behavior takes place among agents of similar memory, and show how this allows unique `rogue' agents with higher memory values to take advantage of a majority population. We also show that agents' analytic capability is largely determined by the size of the intermediary layer of neurons. In the context of these results, we discuss the general nature of natural and artificial intelligence systems, and suggest intelligence only exists in the context of the surrounding environment (embodiment). Source code for the programs used can be found at http://neuro.webdrake.net ., Comment: 9 pages, 7 figures; should be listed as (c) American Physical Society 2001 if possible

The issue of meaningful yet unexpressed background—to language and to our experiences of the body—is one whose exploration is still in its infancy. There are various aspects of “invisible,” implicit, or background experiences which have been investigated from the viewpoints of phenomenology, cognitive psychology, and linguistics. I will argue that James's concept of the phenomenon of fringes, as explicated by Gurwitsch, provides a structural framework from which to investigate and better understand ideas and concepts that are indeterminate, particularly those experienced in the sense of being sought-after. Johnson's conception of the image-schematic gestalt (ISG) provides an approach to bridging the descriptive gap between phenomenology and cognitive psychology. Starting from an analysis of the fringes, I will turn to a consideration of the tip-of-tongue (TOT) state, as a kind of feeling-of-knowing (FOK) state, from a variety of approaches, focusing mainly on cognitive psychology and phenomenology. I will then integrate a phenomenological analysis of these experiences, from the James/Gurwitsch structural viewpoint, with a cognitive/phenomenological analysis in terms of ISGs, and further integrate that with a cognitive/functional analysis of the relation between consciousness and retrieval, employing Anderson et al's theory of inhibitory mechanisms in cognition. This synthesis of these viewpoints will be employed to explore the thesis that the TOT state and similar experiences may relate to the gestalt nature of schemas, and that figure/ground and other contrast-enhancing structures may be both explanatory and descriptive characterizations of the field of consciousness.

The relevance of particulate radon progeny measurements for an estimation of the mixing height was recently established. Here, an attempt at a short-range forecast of radon concentration is presented using a neural-network model applied at a 2-hour based time series. This forecasting activity leads to useful predictions of the mixing height during stability conditions.

It is argued that cognitive capacities can be understood as the outcome of the collective action of a set of agents created by tools that explore possible behaviours and train the agents to behave in such appropriate ways as may be discovered. The coherence of the whole system is assured by a combination of vetting the performance of new agents and dealing appropriately with any faults that the whole system may develop. This picture is shown to account for a range of cognitive capacities, including language.

This paper discusses the simulation results of a model of biological development for neural networks based on a regulatory genome. The model’s results are analyzed using the framework of Heterochrony theory (McKinney and McNamara, 1991). The network development is controlled by genes that produce elements regulating the activation, inhibition, and delay of neurogenetic events. The genome can also regulate the gene expression mechanisms. An ecological task of foraging behavior is used to test the model with an evolving population of artificial organisms. Organisms evolve an optimal foraging behavior and the ability to adapt to changing environments. The adaptive strategy consists in changes of network architecture that are determined by the regulatory rearrangment of neurogenetic events. Results show how heterochronic changes play an adaptive role in the evolution of neural networks.

Values are critical for intelligent behavior, since values determine interests, and interests determine relevance. Therefore we address relevance and its role in intelligent behavior in animals and machines. Animals avoid exhaustive enumeration of possibilities by focusing on relevant aspects of the environment, which emerge into the (cognitive) foreground, while suppressing irrelevant aspects, which submerge into the background. Nevertheless, the background is not invisible, and aspects of it can pop into the foreground if background processing deems them potentially relevant. Essential to these ideas are questions of how contexts are switched, which defines cognitive/behavioral episodes, and how new contexts are created, which allows the efficiency of foreground/background processing to be extended to new behaviors and cognitive domains. Next we consider mathematical characterizations of the foreground/background distinction, which we treat as a dynamic separation of the concrete space into (approximately) orthogonal subspaces, which are processed differently. Background processing is characterized by large receptive fields which project into a space of relatively low dimension to accomplish rough categorization of a novel stimulus and its approximate location. Such background processing is partly innate and partly learned, and we discuss possible correlational (Hebbian) learning mechanisms. Foreground processing is characterized by small receptive fields which project into a space of comparatively high dimension to accomplish precise categorization and localization of the stimuli relevant to the context. We also consider mathematical models of valences and affordances, which are an aspect of the foreground. Cells processing foregound information have no fixed meaning (i.e., their meaning is contextual), so it is necessary to explain how the processing accomplished by foreground neurons can be made relative to the context. Thus we consider the properties of several simple mathematical models of how the contextual representation controls foreground processing. We show how simple correlational processes accomplish the contextual separation of foreground from background on the basis of differential reinforcement. That is, these processes account for the contextual separation of the concrete space into disjoint subspaces corresponding to the foreground and background. Since an episode may comprise the activation of several contexts (at varying levels of activity) we consider models, suggested by quantum mechanics, of foreground processing in superposition. That is, the contextual state may be a weighted superposition of several pure contexts, with a corresponding superposition of the foreground representations and the processes operating on them. This leads us to a consideration of the nature and origin of contexts. Although some contexts are innate, many are learned. We discuss a mathematical model of contexts which allows a context to split into several contexts, agglutinate from several contexts, or to constellate out of relatively acontextual processing. Finally, we consider the acontextual processing which occurs when the current context is no longer relevant, and may trigger the switch to another context or the formation of a new context. We relate this to the situation known as "breakdown" in phenomenology.

This paper shows how the relationship between two arrays of artificial neurons, representing different cortical regions, can be learned. The algorithm enables each neural network to self-organise into a topological map of the domain it represents at the same time as the relationship between these maps is found. Unlike previous methods learning is achieved without a separate training phase; the algorithm which learns the mapping is also that which performs the mapping.

The binding problem is regarded as one of today's key questions about brain function. Several solutions have been proposed, yet the issue is still controversial. The goal of this article is twofold. Firstly, we propose a new experimental paradigm requiring feature binding, the "delayed binding response task". Secondly, we propose a binding mechanism employing fast reversible synaptic plasticity to express the binding between concepts. We discuss the experimental predictions of our model for the delayed binding response task.

We apply Slow Feature Analysis (SFA) to image sequences generated from natural images using a range of spatial transformations. An analysis of the resulting receptive fields shows that they have a rich spectrum of invariances and share many properties with complex and hypercomplex cells of the primary visual cortex. Furthermore, the dependence of the solutions on the statistics of the transformations is investigated.

Humans’ capacity to imitate has been extensively investigated through a wide-range of behavioral and developmental studies. Yet, despite the huge amount of phenomenological evidence gathered, we are still unable to relate this behavioral data to any specific neural substrate. In this paper, we investigate how principles from psychology can be the result of neural computations and therefore attempt to bridge the gap between monkey neurophysiology and human behavioral data, and hence between these two complementary disciplines. Specifically, we address the principle of ideomotor compatibility, by which ‘observing the movements of others influences the quality of one’s own performance’ and develop two neural models which account for a set of related behavioral studies [Brass, M., Bekkering, H., Wohlschla¨ger, A., & Prinz, W. (2000). Compatibility between observed and executed finger movements: comparing symbolic, spatial and imitative cues. Brain and Cognition 44, 124–143]. We show that the ideomotor effect could be the result of two distinct cognitive pathways, which can be modeled by means of biologically plausible neural architectures. Furthermore, we propose a novel behavioral experiment to confirm or refute either of the two model pathways.