Tag: action

An embodied account of serial order: How instabilities drive sequence generation.

Sandamirskaya, Y. and Schöner, G.
Neural Networks, 23:1164–1179, 2010
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Abstract

Learning and generating serially ordered sequences of actions is a core component of cognition both in organisms and in artificial cognitive systems. When these systems are embodied and situated in partially unknown environments, specific constraints arise for any neural mechanism of sequence generation. In particular, sequential action must resist fluctuating sensory information and be capable of generating sequences in which the individual actions may vary unpredictably in duration. We provide a solution to this problem within the framework of Dynamic Field Theory by proposing an architecture in which dynamic neural networks create stable states at each stage of a sequence. These neural attractors are destabilized in a cascade of bifurcations triggered by a neural representation of a condition of satisfaction for each action. We implement the architecture on a robotic vehicle in a color search task, demonstrating both sequence learning and sequence generation on the basis of low-level sensory information.

Review

The paper presents a dynamical model of the execution of sequential actions driven by sensory feedback which allows variable duration of individual actions as signalled by external cues of subtask fulfillment (i.e. end of action). Therefore, it is one of the first functioning models with continuous dynamics which truly integrates action and perception. The core technique used is dynamic field theory (DFT) which implements winner-take-all dynamics in the continuous domain, i.e. the basic dynamics stays at a uniform baseline until a sufficiently large input at a certain position drives activity over a threshold and produces a stable single peak of activity around there. The different components of the model all run with dynamics using the same principle and are suitably connected such that stable peaks in activity can be destabilised to allow moving the peak to a new position (signalling something different).

The aim of the excercise is to show that varying length sequential actions can be produced by a model of continuous neuronal population dynamics. Sequential structure is induced in the model by a set of ordinal nodes which are coupled via additional memory nodes such that they are active one after the after. However, the switch to the next ordinal node in the sequence needs to be triggered by sensory input which indicates that the aim of an action has been achieved. Activity of an ordinal node then directly induces a peak in the action field at a location determined by a set of learnt weights. In the robot example the action space is defined over the hue value, i.e. each action selects a certain colour. The actual action of the robot (turning and accelerating) is controlled by an additional color-space field and some motor dynamics not part of the sequence model. Hence, their sequence model as such only prescribes discrete actions. To decide whether an action has been successfully completed the action field increases activity in a particular spot in a condition of satisfaction field which only peaks at that spot, if suitable sensory input drives the activity at the spot over the threshold. Which spot the action field selects is determined by hand here (in the example it’s an identity function). A peak in the condition of satisfaction field then triggers a switch to the next ordinal node in the sequence. We don’t really see an evaluation of system performance (by what criterion?), but their system seems to work ok, at least producing the sequences in the order demonstrated during learning.

The paper is quite close to what we are envisaging. The free energy principle could add a Bayesian perspective (we would have to find a way to implement the conditional progression of a sequence, but I don’t see a reason why this shouldn’t be possible). Apart from that the function implemented by the dynamics is extremely simple. In fact, the whole sequential system could be replaced with simple, discrete if-then logic without having to change the continuous dynamics of the robot implementation layer (color-space field and motor dynamics). I don’t see how continuous dynamics here helps except that it is more biologically plausible. This is also a point on which the authors focus in the introduction and discussion. Something else that I noticed: all dynamic variables are only 1D (except for the colour-space field which is 2D). This is probably because the DFT formalism requires that the activity over the field is integrated for each position in the field every simulation step to compute the changes in activity (cf. computation of expectations in Bayesian inference) which is probably infeasible when the representations contain several variables.

Cortical Preparatory Activity: Representation of Movement or First Cog in a Dynamical Machine?

Churchland, M. M., Cunningham, J. P., Kaufman, M. T., Ryu, S. I., and Shenoy, K. V.
Neuron, 68:387 – 400, 2010
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Abstract

Summary The motor cortices are active during both movement and movement preparation. A common assumption is that preparatory activity constitutes a subthreshold form of movement activity: a neuron active during rightward movements becomes modestly active during preparation of a rightward movement. We asked whether this pattern of activity is, in fact, observed. We found that it was not: at the level of a single neuron, preparatory tuning was weakly correlated with movement-period tuning. Yet, somewhat paradoxically, preparatory tuning could be captured by a preferred direction in an abstract #space# that described the population-level pattern of movement activity. In fact, this relationship accounted for preparatory responses better than did traditional tuning models. These results are expected if preparatory activity provides the initial state of a dynamical system whose evolution produces movement activity. Our results thus suggest that preparatory activity may not represent specific factors, and may instead play a more mechanistic role.

Review

What are the variables that best explain the preparatory tuning of neurons in dorsal premotor and primary motor cortex of monkeys doing a reaching task? This is the core question of the paper which is motivated by the observation of the authors that preparatory and perimovement (ie. within movement) activity of a single neuron may even qualitatively differ considerably (something conflicting with the view that preparatory activity is a subthreshold version of perimovment activity). This observation is experimentally underlined in the paper by showing that average preparatory activity and average perimovement activity of a single neuron are largely uncorrelated for different experimental conditions.

To quantify the suitability of a set of variables to explain perparatory activity of a neuron the authors use a linear regression approach in which the values of these variables for a given experimental condition are used to predict the firing rate of the neuron in that condition. The authors compute the generalisation error of the learnt linear model with crossvalidation and compare the performance of several sets of variables based on this error. The variables performing best are the principal component scores of the perimovement population activity of all recorded neurons. The difference to alternative sets of variables is significant and in particular the wide range of considered variables makes the result convincing (e.g. target position, initial velocity, endpoints and maximum speed, but also principal component scores of EMG activity and kinematic variables, i.e. position, speed and acceleration of the hand). That perimovement activity is the best regressor for preparatory activity is quite odd, or as Burak aptly put it: “They are predicting the past.”

The authors suggest a dynamical systems view as explanation for their results and hypthesise that preparatory activity sets the initial state of the dynamical system constituted by the population of neurons. In this view, the preparatory activity of a single neuron is not sufficient to predict its evolution of activity (note that the correlation between perparatory and perimovement activity assesses only one particular way of predicting perimovement from preparatory activity – scaling), but the evolution of activity of all neurons can be used to determine the preparatory activity of a single neuron under the assumption that the evolution of activity is governed by approximately linear dynamics. If the dynamics is linear, then any state in the future is a linear transformation of the initial state and given enough data points from the future the initial state can be determined by an appropriate linear inversion. The additional PCA, also a linear transformation, doesn’t change that, but makes the regression easier and, important for the noisy data, also regularises.

These findings and suggestions are all quite interesting and certainly fit into our preconceptions about neuronal activity, but are the presented results really surprising? Do people still believe that you can make sense of the activity of isolated neurons in cortex, or isn’t it already accepted that population dynamics is necessary to characterise neuronal responses? For example, Pillow et al. (Pillow2008) used coupled spiking models to successfully predict spike trains directly from stimuli in retinal ganglion cells. On the other hand, Churchland et al. indirectly claim in this paper that the population dynamics is (approximately) linear, which is certainly disputable, but what would nonlinear dynamics mean for their analysis?

Encoding of Motor Skill in the Corticomuscular System of Musicians.

Gentner, R., Gorges, S., Weise, D., aufm Kampe, K., Buttmann, M., and Classen, J.
Current Biology, 20:1869-1874
 , 2010
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Abstract

Summary How motor skills are stored in the nervous system represents a fundamental question in neuroscience. Although musical motor skills are associated with a variety of adaptations [[1], [2] and [3]], it remains unclear how these changes are linked to the known superior motor performance of expert musicians. Here we establish a direct and specific relationship between the functional organization of the corticomuscular system and skilled musical performance. Principal component analysis was used to identify joint correlation patterns in finger movements evoked by transcranial magnetic stimulation over the primary motor cortex while subjects were at rest. Linear combinations of a selected subset of these patterns were used to reconstruct active instrumental playing or grasping movements. Reconstruction quality of instrumental playing was superior in skilled musicians compared to musically untrained subjects, displayed taxonomic specificity for the trained movement repertoire, and correlated with the cumulated long-term training exposure, but not with the recent past training history. In violinists, the reconstruction quality of grasping movements correlated negatively with the long-term training history of violin playing. Our results indicate that experience-dependent motor skills are specifically encoded in the functional organization of the primary motor cortex and its efferent system and are consistent with a model of skill coding by a modular neuronal architecture [4].

Review

The authors use PCA on TMS induced postures to show that motor cortex represents building blocks of movements which adapt to everyday requirements. To be precise, the authors recorded finger movements which were induced by TMS over primary motor cortex and extracted for each of the different stimulations the posture which had the largest deviation from rest. From the resulting set of postures they computed the first 4 principal components (PCs) and looked how well a linear combination of the PCs could reconstruct postures recorded during normal behaviour of the subjects. This is made more interesting by comparing groups of subjects with different motor experience. They use highly trained violinists and pianists and a group of non-musicians and then compare the different combinations of who is used for determining PCs and what is trying to be reconstructed (violin playing, piano playing, or grasping where grasping can be that of violinists or non-musicians). Basis of comparison is a correlation (R) between the series of joint angle vectors as defined in Shadmehr1994 which can be interpreted as something like the average correlation between data points of the two sequences measured across joint angles (cf. normalised inner product matrix in GPLVM). Don’t ask me why they take exactly this measure, but probably it doesn’t matter. The main finding is that the PCs from violinists are significantly better in reconstructing violin playing than either the piano PCs, or the non-musician PCs. This table is missing in the text (but the data is there, showing mean R and its standard deviation):

R violinists pianists non-musicians

violin 0.69+0.09 0.63+0.11 0.64+0.09

piano 0.70+0.06 0.74+0.06 0.70+0.07

grasp 0.76+0.09 0.76+0.09 0.76+0.10

what is not discussed in the paper is that pianists’ PCs are worse in reconstructing violin playing than PCs of non-musicians. An interesting finding is that the years of intensive training of violinists correlates significantly with the reconstruction quality for violin playing of violinist PCs while it is anticorrelated with the reconstruction quality for grasping indicating that the postures activated in primary motor cortex become more adapted to frequently executed tasks. However, it has to be noted that this correlation analysis is based on only 9 data points.

In the beginning of the paper they show an analysis of the recorded behaviour which simply is supposed to ensure that violin playing, piano playing and grasping movements are sufficiently different which we may believe, although piano playing and grasping apparently are somewhat similar.

Efficient Reductions for Imitation Learning.

Ross, S. and Bagnell, D.
in: JMLR W&CP 9: AISTATS 2010, pp. 661–668, 2010
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Abstract

Imitation Learning, while applied successfully on many large real-world problems, is typically addressed as a standard supervised learning problem, where it is assumed the training and testing data are i.i.d.. This is not true in imitation learning as the learned policy influences the future test inputs (states) upon which it will be tested. We show that this leads to compounding errors and a regret bound that grows quadratically in the time horizon of the task. We propose two alternative algorithms for imitation learning where training occurs over several episodes of interaction. These two approaches share in common that the learner’s policy is slowly modified from executing the expert’s policy to the learned policy. We show that this leads to stronger performance guarantees and demonstrate the improved performance on two challenging problems: training a learner to play 1) a 3D racing game (Super Tux Kart) and 2) Mario Bros.; given input images from the games and corresponding actions taken by a human expert and near-optimal planner respectively.

Review

The authors note that previous approaches of learning a policy from an example policy are limited in the sense that they only see successful examples generated from the desired policy and, therefore, will exhibit a larger error than expected from supervised learning of independent samples, because an error can propagate through the series of decisions, if the policy hasn’t learnt to recover to the desired policy when an error occurred. They then show that a lower error can be expected when a Forward Algorithm is used for training which learns a non-stationary policy successively for each time step. The idea probably being (I’m not too sure) that the data at the time step that is currently learnt contains the errors (that lead to different states) you would usually expect from the learnt policies, because for every time step new data is sampled based on the already learnt policies. They transfer this idea to learning of a stationary policy and propose SMILe (stochastic mixing iterative learning). In this algorithm the stationary policy is a linear combination of policies learnt in previous iterations where the initial policy is the desired one. The influence of the desired policy decreases exponentially with the number of iterations, but also the weights of policies learnt later decrease exponentially, but stay fixed in subsequent iterations, i.e. the policies learnt first will have the largest weights eventually. This makes sense, because they will most probably be closest to the desired policy (seeing mostly samples produced from the desired policy).

The aim is to make the learnt policy more robust without using too many samples from the desired policy. I really wonder whether you could achieve exactly the same performance by simply additionally sampling the desired policy from randomly perturbed states and adding these as training points to learning of a single policy. Depending on how expensive your learning algorithm is this may be much faster in total (as you only have to learn once on a larger data set). Of course, you then may not have the theoretical guarantees provided in the paper. Another drawback of the approach presented in the paper is that it needs to be possible to sample from the desired policy interactively during the learning. I can’t imagine a scenario where this is practical (a human in the loop?).

I was interested in this, because in an extended abstract to a workshop (see attached files) the authors referred to this approach and also mentioned Langford2009 as a similar learning approach based on local updates. Also you can see the policy as a differential equation, i.e. the results of the paper may also apply to learning of dynamical systems without control inputs. The problems are certainly very similar.

They use a neural network to learn policies in the particular application they consider.

Generating coherent patterns of activity from chaotic neural networks.

Sussillo, D. and Abbott, L. F.
Neuron, 63:544–557, 2009
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Abstract

Neural circuits display complex activity patterns both spontaneously and when responding to a stimulus or generating a motor output. How are these two forms of activity related? We develop a procedure called FORCE learning for modifying synaptic strengths either external to or within a model neural network to change chaotic spontaneous activity into a wide variety of desired activity patterns. FORCE learning works even though the networks we train are spontaneously chaotic and we leave feedback loops intact and unclamped during learning. Using this approach, we construct networks that produce a wide variety of complex output patterns, input-output transformations that require memory, multiple outputs that can be switched by control inputs, and motor patterns matching human motion capture data. Our results reproduce data on premovement activity in motor and premotor cortex, and suggest that synaptic plasticity may be a more rapid and powerful modulator of network activity than generally appreciated.

Review

The authors present a new way of reservoir computing. The setup apparently (haven’t read the paper) is very similar to the echo state networks of Herbert Jaeger (Jaeger and Haas, Science, 2004); the difference being the signal that is fed back to the reservoir from the output. While Jaeger fed back the target value f(t), they feed back the error between f(t) and the prediction given the current weights and reservoir activity. Key to their approach then is that they use a weight update rule which almost instantaneously provides weights that minimise the error. While this obviously leads to a very high variability of the weights across time steps at the start of learning, they argue that this variability diminishes during learning and weights eventually stabilise such that, when learning is switched off, the target dynamics is reproduced. They present a workaround which may make it possible to also learn non-periodic functions, but it’s clearly better suited for periodic functions.

I wonder how the learning is divided between feedback mechanism and weight adaptation (network model of Fig. 1A). In particular, it could well be that the feedback mechanism is solely responsible for successfull learning while the weights just settle to a more or less arbitrary setting once the dynamics is stabilised through the feedback (making weights uninterpretable). The authors also report how the synapses within the reservoir can be adapted to reproduce the target dynamics when no feedback signal is given from the network output (structure in Fig. 1C). Curiously, the credit assignment problem is solved by ignoring it: for the adaptation of reservoir synapses the same network level output error is used as for the adaptation of output weights.

It’s interesting that it works, but to know why and how it works would be good. The main argument of the authors why their proposal is better than echo state networks is that their proposal is more stable. They present corresponding results in Fig. 4, but they never tell us what they mean by stable. So how stable are the dynamics learnt by FORCE? How much can you perturb the network dynamics before it stops being able to reproduce the target dynamics. In other words, how far off the desired dynamics can you initialise the network state?

They have an interesting principal components analysis of network activity suggesting that the dynamics converges to the same values for the first principal components for different starting states, but I haven’t understood it well enough during this first read to comment further on that.

Modeling discrete and rhythmic movements through motor primitives: a review.

Degallier, S. and Ijspeert, A.
Biol Cybern, 103:319–338, 2010
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Abstract

Rhythmic and discrete movements are frequently considered separately in motor control, probably because different techniques are commonly used to study and model them. Yet the increasing interest in finding a comprehensive model for movement generation requires bridging the different perspectives arising from the study of those two types of movements. In this article, we consider discrete and rhythmic movements within the framework of motor primitives, i.e., of modular generation of movements. In this way we hope to gain an insight into the functional relationships between discrete and rhythmic movements and thus into a suitable representation for both of them. Within this framework we can define four possible categories of modeling for discrete and rhythmic movements depending on the required command signals and on the spinal processes involved in the generation of the movements. These categories are first discussed in terms of biological concepts such as force fields and central pattern generators and then illustrated by several mathematical models based on dynamical system theory. A discussion on the plausibility of theses models concludes the work.

Review

In the first part, the paper reviews experimental evidence for the existence of a motor primitive system located on the level of the spinal cord. In particular, the discussion is centred on the existence of central pattern generators and force fields (also: muscle synergies) defined in the spinal cord. Results showing the independence of these from cortical signals exist for animals up to the cat, or so. “In humans, the activity of the isolated spinal cord is not observable, […]: influences from higher cortical areas and from sensory pathways can hardly be excluded.”

The remainder of the article reviews dynamical systems that have been proposed as models for movement primitives. The models are roughly characterised according to the assumptions about the relationships between discrete and rhythmic movements. The authors define 4 categories: two/two, one/two, one/one and two/one, where a two means separate systems for discrete and rhythmic movements, a one means a common system, the number before the slash corresponds to the planning process (signals potentially generated as motor commands from cortex) and the number behind the slash corresponds to the execution system where the movement primitives are defined.

You would think that the aim of this excercise is to work out advantages and disadvantages of the models, but the authors mainly restrict themselves to describing the models. The main conclusion then is that discrete and rhythmic movements can be generated from movement primitives in the spinal cord while cortex may only provide simple, non-patterned commands. The proposed categorisation may help to discern models experimentally, but apparently there is currently no conclusive evidence favouring any of the categories (authors repeatedly cite two conflicting studies).

The Neural Costs of Optimal Control.

Gershman, S. J. and Wilson, R. C.
in: Advances in Neural Information Processing Systems 23, 2010
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Abstract

Optimal control entails combining probabilities and utilities. However, for most practical problems, probability densities can be represented only approximately. Choosing an approximation requires balancing the benefits of an accurate approximation against the costs of computing it. We propose a variational framework for achieving this balance and apply it to the problem of how a neural population code should optimally represent a distribution under resource constraints. The essence of our analysis is the conjecture that population codes are organized to maximize a lower bound on the log expected utility. This theory can account for a plethora of experimental data, including the reward-modulation of sensory receptive fields, GABAergic effects on saccadic movements, and risk aversion in decisions under uncertainty.

Review

Within the area of decision theory they consider the problem of evaluating the expected utility of an action given a posterior. They propose a variational framework analogously to the one used for the EM algorithm in which the utility replaces the likelihood and the posterior replaces the prior. Their main contribution is to include a cost penalising the complexity of the approximation of the posterior and use the so defined lower bound on the expected utility to simultaneously optimise the density used to approximate the posterior. As the utility does not only contain the cost of the approximation, but also the actual utility of an action in the considered states, this model predicts that the approximating density should also reflect what is behaviourally relevant instead of only trying to represent the natural posterior distribution optimally.

In the results section they then show that under this model the approximated posterior can (and will) indeed put more probability mass on states with larger utility, something which has apparently been found in grasshoppers. Additionally they show that increasing the cost of spikes results in smaller firing rates which, as they argue, leads to response latencies as seen in experiments. Finally, they show that under the assumption that high utility or very costly states are rare, the model will automatically account for the nonlinear weighting of probabilities in risky choice observed in humans. The model therefore explains this irrational behaviour by noting that “under neural resource constraints, the approximate density will be biased towards high reward regions of the state space.”

I don’t know enough to judge the correspondence of the model behaviour/predictions with the experimental results, or whether the model contradicts some other results. However, the paper is quite inspiring in the sense that it presents an intuitive idea which potentially has big implications for how populations of neurons code probability distributions, namely that the neuronal codes are influenced as much by expected rewards as by the natural distribution. Of course, the paper leaves many questions open. The authors only show results for when the approximate distributions are optimised alone, but what happens when actions and distribution are optimised simultaneously? What are the timescales of the distribution optimisation? Is it really instantanious (on the same timescale as action selection) as the authors indicate, or is it rather a slower process? Their proposal also has the potential to explain how the dimensionality of the state space can be reduced by only considering states which are behaviourally relevant. However, it remains unclear to what extent this specialisation should be implemented. In other words, is the posterior dependent, e.g., on the precise goal in the task, or is it rather only dependent on the selected task? Especially the cost of spiking example suggests a connection between the proposed mechanism and attention. Can attention be explained by this low-level description of biased representations of the posterior distribution?

The paper is quite inspiring and you kind of wonder why nobody has made these ideas explicit before, or maybe somebody has? Actually Maneesh Sahani had a NIPS paper in 2004 which they cite, but not comment on, and which looks very similar to what they do here (from the abstract).

Winnerless competition between sensory neurons generates chaos: A possible mechanism for molluscan hunting behavior.

Varona, P., Rabinovich, M. I., Selverston, A. I., and Arshavsky, Y. I.
Chaos: An Interdisciplinary Journal of Nonlinear Science, 12:672–677, 2002
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Abstract

In the presence of prey, the marine mollusk Clione limacina exhibits search behavior, i.e., circular motions whose plane and radius change in a chaotic-like manner. We have formulated a dynamical model of the chaotic hunting behavior of Clione based on physiological in vivo and in vitro experiments. The model includes a description of the action of the cerebral hunting interneuron on the receptor neurons of the gravity sensory organ, the statocyst. A network of six receptor model neurons with Lotka-Volterra-type dynamics and nonsymmetric inhibitory interactions has no simple static attractors that correspond to winner take all phenomena. Instead, the winnerless competition induced by the hunting neuron displays hyperchaos with two positive Lyapunov exponents. The origin of the chaos is related to the interaction of two clusters of receptor neurons that are described with two heteroclinic loops in phase space. We hypothesize that the chaotic activity of the receptor neurons can drive the complex behavior of Clione observed during hunting.

Review

see Levi2005 for short summary in context

My biggest concern with this paper is that the changes in direction of the mollusc may also result from feedback from the body and especially the stratocysts during its accelerated swimming. The question is, are these direction changes a result of chaotic, but deterministic dynamics in the sensory network as suggested by the model, or are they a result of essentially random processes which may be influenced by feedback from other networks? The authors note that in their model “The neurons keep the sequence of activation but the interval in which they are active is continuously changing in time”. After a day of search for papers which have investigated the swimming behaviour of Clione limacina (the mollusc in question) I came to the conclusion that the data schown in Fig. 1 likely is the only data set of swimming behaviour that was published. This small data set suggests random changes in direction, in contrast to the model, but it does not allow to draw any definite conclusions about the repetitiveness of direction changes.

The role of sensory network dynamics in generating a motor program.

Levi, R., Varona, P., Arshavsky, Y. I., Rabinovich, M. I., and Selverston, A. I.
J Neurosci, 25:9807–9815, 2005
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Abstract

Sensory input plays a major role in controlling motor responses during most behavioral tasks. The vestibular organs in the marine mollusk Clione, the statocysts, react to the external environment and continuously adjust the tail and wing motor neurons to keep the animal oriented vertically. However, we suggested previously that during hunting behavior, the intrinsic dynamics of the statocyst network produce a spatiotemporal pattern that may control the motor system independently of environmental cues. Once the response is triggered externally, the collective activation of the statocyst neurons produces a complex sequential signal. In the behavioral context of hunting, such network dynamics may be the main determinant of an intricate spatial behavior. Here, we show that (1) during fictive hunting, the population activity of the statocyst receptors is correlated positively with wing and tail motor output suggesting causality, (2) that fictive hunting can be evoked by electrical stimulation of the statocyst network, and (3) that removal of even a few individual statocyst receptors critically changes the fictive hunting motor pattern. These results indicate that the intrinsic dynamics of a sensory network, even without its normal cues, can organize a motor program vital for the survival of the animal.

Review

The authors investigate the neural mechanisms of hunting behaviour in a mollusk. It’s simplicity allows that the nervous system can be completely stripped apart from the rest of the body and be investigated in isolation from the body, but as a whole. In particular, the authors are interested in the causal influence of sensory neurons on motor activity.

The mollusk has two types of behaviour for positioning its body in the water: 1) it uses gravitational sensors (statocysts) to maintain a head-up position in the water under normal circumstances and 2) it swims in apparently chaotic, small loops when it suspects prey in its vicinity (searching). In this paper the authors present evidence that the searching behaviour 2) is still largely dependent on the (internal) dynamics of the statocysts.

The model is as follows (see Varona2002): without prey inhibitory connections between sensory cells in the stratocysts make sure that only a small proportion of cells are firing (those that are activated by mechanoreceptors according to gravitation acting on a stone-like structure in the statocysts), but when prey is in the vicinity of the mollusk (as indicated by e.g. chemoreceptors) cerebral hunting neurons additionally excite the statocyst cells inducing chaotic dynamics between them. The important thing to note is that then the statocysts still influence motor behaviour as shown in the paper. So the argument is that the same mechanism for producing motor output dependent on statocyst signals can be used to generate searching just through changing the activity of the sensory neurons.

Overall the evidence presented in the paper is convincing that statocyst activity influences the activity of the motor neurons also in the searching behaviour, but it cannot be said concludingly that the statocysts are necessary for producing the swimming, because the setup allowed only the activity of motor neurons to be observed without actually seeing the behaviour (actually Levi2004 show that the typical searching behaviour cannot be produced when the statocysts are removed). For the same reason, the experiments also neglected possible feedback mechanisms between body/mollusk and environment, e.g. in the statocyst activity due to changing gravitational state, i.e. orientation. The argument there is, though not explicitly stated, that the statocyst stops computing the actual orientation of the body, but is purely driven through its own dynamics. Feedback from the peripheral motor system is not modelled (Varona2002, argueing that for determining the origin of the apparent chaotic behaviour this is not necessary).

For us this is a nice example for how action can be a direct consequence of perception, but even more so that internal sensory dynamics can produce differentiated motor behaviour. The connection between sensory states and motor activity is relatively fixed, but different motor behaviour may be generated by different processing in the sensory system. The autonomous dynamics of the statocysts in searching behaviour may also be interpreted as being induced from different, high-precision predictions on a higher level. It may be questioned how good a model the mollusk nervous system is for information processing in the human brain, but maybe they share these principles.