The Neurosciences Institute, 10640 John Jay Hopkins Drive, San Diego, CA 92121, USA
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Abstract |
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Key Words: brain-based device, Darwin VIII, reentrant connectivity, visual binding, visual object recognition
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Introduction |
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The appeal to mechanisms of attention often focuses on parietal or frontal areas, the operations of which are distant from early stages of sensory processing (Shadlen and Movshon, 1999). It has been suggested that these areas bind and select objects in a visual scene by means of an executive mechanism, such as a spotlight of attention that combines visual features at specific locations in space (Treisman, 1998
; Shafritz et al., 2002
). In this mechanism, strict limits on the number of objects that can be simultaneously bound arise from the limited capacity of the attentional system (Pashler, 1999
).
Advocates of neural synchrony, by contrast, suggest that binding is an automatic, dynamic and pre-attentive process arising from low-level neural dynamics. For example, the linkage of neuronal groups by reentry, where reentry refers to the recursive exchange of signals across multiple, parallel and reciprocal connections (Edelman, 1993), can lead to selective synchronization (Tononi et al., 1992
; von der Malsburg and Buhmann, 1992
; Knoblauch and Palm, 2002a
,b). This synchronized activity among neuronal groups can form coherent circuits corresponding to perceptual categories (Sporns et al., 1991
). A fundamental question for proponents of neural synchrony is how such emergent functional circuits contribute to an organisms adaptive behavior, especially in situations that require preferential behavior towards one object among many in a scene.
A previous computational model of visual binding based on reentry (Tononi et al., 1992) learned to make simulated saccades to preferred visual objects. This model included nine simulated visual cortical areas, as well as reward and motor systems, and its performance showed that reentrant connections facilitated the recognition and discrimination of multiple visual objects. Despite showing the capabilities of reentrant circuits, the model had several limitations. For example, the stimuli used were taken from a limited set and were of uniform scale. Furthermore, its behavior did not emerge in a rich and noisy environment of the kind confronted by behaving organisms.
In this paper, we address these limitations by embedding a simulated nervous system in a real-world device capable of engaging in rich exploratory and selective behavior. We describe the construction and performance of Darwin VIII, the latest in a series of brain-based devices (Edelman et al., 1992; Almassy et al., 1998
; Krichmar et al., 2000
; Krichmar and Edelman, 2002
). In Darwin VIII, synchronously active neuronal circuits are dynamically formed as the device engages in visually guided behavior that includes a discrimination task. Darwin VIII contains simulated neural areas analogous to the ventral stream of the visual system, areas that influence visual tracking and areas analogous to ascending neuromodulatory reward systems that modulate synaptic plasticity.
To represent the relative timing of neuronal activity in the simulation, the activity of each neuronal unit in Darwin VIII is described by both a firing rate variable and a phase variable. In line with the proposal that cortical neurons detect temporal coincidences among synaptic inputs (Abeles, 1982; Konig et al., 1996
; Azouz and Gray, 2000
), our model includes a mechanism that supports synchrony among coactive and connected neuronal units (Tononi et al., 1992
). We found that this mechanism provides for the emergence of multiple, differentiated, and globally distributed neuronal circuits that correspond to distinct objects in Darwin VIIIs visual field.
As it moves autonomously in the laboratory environment, Darwin VIII approaches and views multiple objects that share visual features. It becomes conditioned to prefer one target object over multiple distracters by association of the target with an innately preferred auditory cue. Darwin VIII demonstrates this preference behaviorally by orienting toward the target. While observing the devices behavior during this task, we recorded the state of every neuronal unit and synaptic connection in its simulated nervous system. As we have noted previously (Krichmar and Edelman, 2002), such data would be impossible to obtain and compare in animal experiments. By enabling behavioral and brain responses to be followed in detail at all levels of control, brain-based devices such as Darwin VIII have heuristic value in interpreting data obtained from behaving animals.
Our results support the hypothesis that visual binding results from the dynamic synchronization of neural activity mediated by reentrant connections among widely dispersed neural areas. The performance of Darwin VIII also suggests that specific timing relations and mean firing rates can act in a complementary fashion to regulate behavior, and that synchrony among groups of neurons as distinct from synchrony between pairs of individual neurons may play a significant role in adaptive neural function.
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Materials and Methods |
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Neuroanatomy
In the present experiments, the simulated nervous system contained 28 neural areas, 53 450 neuronal units, and 1.7 million synaptic connections. It includes a visual system, a tracking system, neural areas that respond to auditory cues, and a value or reward system. Figure 2 shows a high-level diagram of the simulated nervous system including the various neural areas and the arrangement of feedforward and reentrant synaptic connections. Specific parameters relating to each area and to patterns of connectivity are given in Tables 1 and 2.
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The visual system is modeled on the primate occipitotemporal or ventral cortical pathway (in our model V1 V2
V4
IT), in which neurons in successive areas have progressively larger receptive fields until, in inferotemporal cortex, receptive fields cover nearly the entire visual field (Ungerleider and Haxby, 1994
). Visual images from NOMADs CCD camera are filtered for color and edges (see Appendix A) and the filtered output directly influences neural activity in area V1. V1 is divided into subregions each having neuronal units that respond preferentially to green (V1-green), red (V1-red), horizontal line segments (V1-horizontal), vertical line segments (V1-vertical), 45° lines (V1-diagonal-right) and 135° lines (V1-diagonal-left). By assuming only these response properties and omitting the detailed microcircuitry of V1 (Raizada and Grossberg, 2003
), this model provides a computationally tractable foundation for analyzing higher-level interactions within the visual system and between the visual system and other cortical areas.
Subregions of V1 project topographically to corresponding subregions of V2. The receptive fields of neuronal units in V2 are narrow and correspond closely to pixels from the CCD camera image. V2 has both excitatory and inhibitory reentrant connections within and among its subregions. Each V2 subregion projects to a corresponding V4 subregion topographically but broadly, so that V4s receptive fields are larger than those of V2. V4 subregions project back to the corresponding V2 subregions with non-topographic reentrant connections. The reentrant connectivity within and among subregions of V4 is similar to that in V2. V4 projects in turn non-topographically to IT so that each neuronal unit in IT receives input from three V4 neuronal units randomly chosen from three different V4 subregions. Thus, while neuronal units in IT respond to a combination of visual inputs, the level of synaptic input into a given IT neuronal unit is fairly uniform; this prevents the activity of individual IT neuronal units from dominating the overall activity patterns. IT neuronal units project to other IT neuronal units through plastic connections, and back to V4 through non-topographic reentrant connections.
Tracking System
The tracking system allows Darwin VIII to orient towards auditory and visual stimuli. The activity of area C (analogous to the superior colliculus) dictates where Darwin VIII directs its camera gaze. Tracking in Darwin VIII is achieved by signals to Darwin VIIIs wheels based on the vector summation of the activity of the neuronal units in area C (Georgopoulos et al., 1986). Each neuronal unit in area C has a receptive field which matches its preferred direction, and the area has a topographic arrangement (Lee et al., 1988
) such that if activity is predominately on the left side of C, signals to Darwin VIIIs wheels are issued that evoke a turn towards the left. The auditory areas (A-left and A-right) have strong excitatory projections to the respective ipsilateral sides of C causing Darwin VIII to orient towards a sound source. V4 projects topographically to C, its activity causing Darwin VIII to center its gaze on a visual object. Both IT and the value system S project to C, and plastic connections in the pathways IT
C and IT
S facilitate target selection by creating a bias in activity, reflecting salient perceptual categories (see Value System, below). Prior to experience, because of a lack of bias, Darwin VIII directs its gaze predominately between two objects. After learning to prefer a visual object, changes in the strengths of the plastic connections result in greater activity in those parts of C corresponding to the preferred objects position.
Auditory System
This system converts inputs from microphones into simulated neuronal unit activity (see Appendix B. Auditory System and its Inputs). Neural areas Mic-left and Mic-right are respectively activated whenever the corresponding microphones detect a sound of sufficient amplitude within a specified frequency range. Mic-left/Mic-right project to neuronal units in areas A-left/A-right (see Fig. 2). Sound from one side results in activity on the ipsilateral side of the auditory system, which in turn produces activity on the ipsilateral side of C causing orientation towards the sound source.
Value System
Activity in the simulated value system (Area S, Fig. 2) signals the occurrence of salient sensory events and this activity contributes to the modulation of connection strengths in pathways IT S and IT
C. Initially, S is activated by sounds detected by Darwin VIIIs auditory system (see A-left
S and A-right
S in Fig. 2). Activity in S is analogous to that of ascending neuromodulatory systems in that it is triggered by salient events, influences large regions of the simulated nervous system (see Synaptic Plasticity, below), and persists for several cycles (Aston-Jones and Bloom, 1981
; Schultz et al., 1997
; Sporns et al., 2000
). In addition, due to its projection to the tracking area C, area S has a direct influence on behavior.
Neuronal Dynamics
A neuronal unit in Darwin VIII is simulated by a mean firing rate model. The state of each unit is determined by both a mean firing rate variable (s) and a phase variable (p). The mean firing rate variable of each unit corresponds to the average activity of a group of 100 real neurons during a time period of
100 ms. The phase variable, which specifies the relative timing of firing activity, provides temporal specificity without incurring the computational costs associated with modeling of the spiking activity of individual neurons in real-time (see Neuronal Unit Activity and Phase, below).
Synaptic connections between neural units, both within and between neuronal areas, are set to be either voltage-independent or voltage-dependent, either phase-independent or phase-dependent, and either plastic or non-plastic. Voltage-independent connections provide synaptic input regardless of postsynaptic state. Voltage-dependent connections represent the contribution of receptor types (e.g. NMDA receptors) that require postsynaptic depolarization to be activated (Wray and Edelman, 1996). In Darwin VIII, all within-area excitatory connections and all between-area reentrant excitatory connections are voltage-dependent (see Fig. 2 and Table 2). These connections play a modulatory role in neuronal dynamics (Grossberg, 1999
). Phase-dependent connections influence both the activity and the phase of postsynaptic neuronal units, whereas phase-independent connections influence only the activity. All synaptic pathways in Darwin VIII are phase-dependent except those involved in motor output (see Table 2: A-left/A-right
C, C
C) or sensory input (see Table 2: Mic-left/Mic-right
A-left/A-right, A-left
A-right, V1
V2), since signals at these interfaces are defined by magnitude only. Plastic connections are either value-independent or value-dependent, as described below.
Neuronal Unit Activity and Phase
The mean firing rate (s) of each neuronal unit ranges continuously from 0 (quiescent) to 1 (maximal firing). The phase (p) is divided into 32 discrete bins representing the relative timing of activity by an angle ranging from 0 to 2. The state of a neuronal unit is updated as a function of its current state and contributions from voltage-independent, voltage-dependent, and phase-independent inputs. The voltage-independent input to unit i from unit j is:
where sj(t) is the activity of unit j, and cij is the connection strength from unit j to unit i. The voltage-independent postsynaptic influence on unit i is calculated by convolving this value into a cosine-tuning curve over all phases:
where M is the number of different anatomically defined connection types (see Table 2); Nl is the number of connections of type M projecting to unit i; pj(t) is the phase of neuronal unit j at time t; and tw is the tuning width, which in our experiments is set to 10 so that the width of the tuning curve is relatively sharp (5 phase bins).
The voltage-dependent input to unit i from unit j is:
where
where
is a threshold for the postsynaptic activity below which voltage-dependent connections have no effect (see Table 1).
The voltage-dependent postsynaptic influence on unit i is given by:
The phase-independent activation into unit i from unit j is:
The phase-independent postsynaptic influence on unit i is a uniform distribution based on all the phase-independent inputs divided by the number of phase bins (32).
A new phase, pi(t + 1), and activity, si(t + 1), are chosen based on a distribution created by linearly summing the postsynaptic influences on neuronal unit i (see Fig. 3):
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The phase threshold,
, of the neuronal unit is subtracted from the distribution POSTi and a new phase, pi(t + 1), is calculated with a probability proportional to the resulting distribution (Fig. 3; bottom row). If the resulting distribution has an area less than zero (i.e. no inputs are above the phase threshold), a new phase, pi(t + 1), is chosen at random. The new activity for the neuronal unit is the activity level at the newly chosen phase, which is then subjected to the following activation function:
where
where determines the persistence of unit activity from one cycle to the next, gi is a scaling factor and
is a unit specific firing threshold. Specific parameter values for neuronal units are given in Table 1, and synaptic connections are specified in Table 2.
In this model of a neuronal unit, postsynaptic phase tends to be correlated with the phase of the most strongly active presynaptic inputs (Abeles, 1982; Konig et al., 1996
; Azouz and Gray, 2000
). We found that this neuronal unit model facilitates the emergence of synchronously active neuronal circuits in both a simple network (see Neuronal Synchrony in a Simple Network Model, below) and in the full Darwin VIII, where such emergence involves additional constraints imposed by reentrant connectivity, plasticity and behavior.
Synaptic Plasticity
Synaptic strengths are subject to modification according to a synaptic rule that depends on the phase and activities of the pre- and postsynaptic neuronal units. Plastic synaptic connections are either value-independent (see IT IT in Fig. 2) or value-dependent (see IT
S, IT
C in Fig. 2). Both of these rules are based on a modified BCM learning rule (Bienenstock et al., 1982
) in which thresholds defining the regions of depression and potentiation are a function of the phase difference between the presynaptic and postsynaptic neuronal units (see Fig. 2, inset). Synapses between neuronal units with strongly correlated firing phases are potentiated and synapses between neuronal units with weakly correlated phases are depressed; the magnitude of change is determined as well by pre- and postsynaptic activities. This learning rule is similar to a spike-time dependent plasticity rule (Bi and Poo, 1998
; Senn et al., 2001
; Song and Abbott, 2001
) applied to jittered spike trains where the region of potentiation has a high peak and a thin tail, and the region of depression has a comparatively small peak and fat tail (Izhikevich and Desai, 2003
).
Value-independent synaptic changes in cij are given by:
where si(t) and sj(t) are activities of post- and presynaptic units, respectively, is a fixed learning rate and
where pi(t) and pj(t) are the phases of post- and presynaptic units (0.0
p
1.0). A value of
p near 1.0 indicates that pre- and postsynaptic units have similar phases, a value of
p near 0.0 indicates that pre- and postsynaptic units are out of phase. The function BCM is implemented as a piecewise linear function, taking
p as input, that is defined by two thresholds (q1, q2, in radians), two inclinations (k1, k2) and a saturation parameter
(
= 6 throughout):
Specific parameter settings for fine-scale synaptic connections are given in Table 2.
The rule for value-dependent synaptic plasticity differs from the value-independent rule in that an additional term, based on the activity and phase of the value system, modulates the synaptic strength changes. Synaptic connections terminating on neuronal units that are in phase with the value system are potentiated, and connections terminating on units out of phase with the value system are depressed.
The synaptic change for value-dependent synaptic plasticity is given by:
where V(t) is the mean activity level in the value area S at time t. Note that the BCMv function is slightly different from the BCM function above in that it uses the phase difference between area S and the postsynaptic neuronal unit as input,
where pv(t) is the mean phase in area S. When both BCM and BCMv return a negative number, BCMv is set to 1 to ensure that the synaptic connection is not potentiated when both the presynaptic neuronal unit and value system are out of phase with the postsynaptic neuronal unit.
Simulation Cycle Computation
During each simulation cycle of Darwin VIII, sensory input is processed, the states of all neuronal units are computed, the connection strengths of all plastic connections are determined, and motor output is generated. In our experiments, execution of each simulation cycle required 100 milliseconds of real time (see Appendix C).
Experimental Conditions
Experimental Environment
Figure 4A shows a diagram of Darwin VIIIs environment. A photograph of Darwin VIII in such an environment is given in Figure 4B. The environment consisted of an enclosed area with black walls. Various pairs of shapes from a set consisting of a green diamond, a green square, a red diamond, and a red square were hung on two opposite walls. The floor was covered with opaque black plastic panels, and contained a boundary made of reflective construction paper. When this boundary was detected by the infrared detector attached to the front of NOMAD and facing toward the floor, Darwin VIII made one of two reflexive movements: (i) if an object was in its visual field, it backed up, stopped, and then turned 180°; (ii) if there was no object in its visual field, Darwin VIII turned
90°, thus orienting away from walls without visual stimuli. Near the boundary of walls containing visual shapes, infrared emitters (IR) on one side of the room were paired with IR sensors containing a speaker on the other side, to create an IR beam (see Fig. 4A). If Darwin VIIIs movement broke either beam, a tone was emitted. Detection of the tone by Darwin VIII elicited an orientation movement towards the source of the sound.
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Experiments were divided into two stages: training and testing (see Fig. 5). During both stages the activity and phase responses of all neuronal units were recorded for analysis.
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During testing (Fig. 5B), the speakers were turned off, and Darwin VIII was allowed to explore its enclosure for 15 000 simulation cycles. The first 10 000 cycles involved encounters with the same target and distracters present during the training stage. The final 5 000 cycles involved encounters with the target and the single shape of the set of four that did not share any features with the target (e.g. a pair consisting of a red diamond as target and a green square as distracter).
Training and testing was repeated with three different Darwin VIII subjects using each of the four shapes as a target (a total of 12 training and testing sessions). Each Darwin VIII subject consisted of the same physical device, but each possessed a unique simulated nervous system. This variability among subjects was a consequence of random initialization in both the microscopic details of connectivity between individual neuronal units and the initial connection strengths between those units. The overall connectivity among neuronal units remained similar among different subjects, however, inasmuch as that connectivity was constrained by the synaptic pathways, arborization patterns, and ranges of initial connection strengths (see Fig. 2 and Table 2 for specifics).
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Results |
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To illustrate how reentrant connections among neuronal units can lead to neuronal synchrony in a mean firing rate model with a phase parameter, we analyzed a simple network model consisting of three neuronal units (Fig. 6A). Units n1 and n2 receive steady phase-independent input and project via voltage-independent connections to a third neuronal unit n3. Units n1 and n2 project to each other, and unit n3 projects back to both n1 and n2 via reentrant voltage-dependent connections. All weights were chosen from a uniform random distribution of range 1.41.5. Figure 6B shows that in this model all neuronal units become synchronized within 10 simulation cycles. By contrast, if reentrant connections are removed (the dotted arrows in Fig. 6A) so that only feedforward projections remain, synchrony is not achieved (Fig. 6C).
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To explore whether specific connection strengths are important for network behavior, we repeated the above analysis several times using different random seeds, and we also compared a network in which all weights were set to a mean value (1.45). In all cases we observed networks for 10 000 cycles and noted qualitatively identical results to those shown in Figure 6. We also repeated our analysis for networks in which value-independent plasticity was enabled for the feed-forward projections n1 n3 and n2
n3. As before, we tested networks with randomly selected weights as well as networks with all weights initially set to a mean value (1.45), and again, in both cases we observed synchrony in intact networks and no synchrony in lesioned networks (data not shown). Also, since pre- and postsynaptic units were correlated in activity and phase, plastic connections in the intact networks increased in strength by nearly 100% over 1000 cycles. In lesioned networks, however, because pre- and postsynaptic units were not in phase with each other these connections were depressed to
10% of their initial values over the same duration.
The results from this reduced model show that the presence of reentrant connections can facilitate synchronous activity among neural areas, that this synchrony does not depend on specific or differential connection strengths, and that the absence of reentry is not compensated by synaptic plasticity. The full Darwin VIII model has three major differences from this reduced model. It has a large-scale reentrant neuroanatomy based on the vertebrate visual cortex, it involves value-dependent and value-independent synaptic plasticity, and it behaves autonomously in a real-world environment.
Target Tracking Behavior
The discrimination performance of each Darwin subject was assessed by how well that subject tracked toward target objects in the absence of auditory cues. This was calculated as the fraction of time for which the target was centered in Darwin VIIIs visual field during each approach to a pair of visual objects.
Figure 7A shows that all subjects successfully tracked the four different targets over 80% of the time. It should be noted that successful performance on this task is not trivial. Targets and distracters appeared in the visual field at many different scales and at many different positions as Darwin VIII explored its environment. Moreover, because of shared properties, targets cannot be reliably distinguished from distracters on the basis of color or shape alone.
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Neural Dynamics during Behavior
During Darwin VIIIs behavior, circuits comprised of synchronously active neuronal groups were distributed throughout different areas in the simulated nervous system. Multiple objects were distinguishable by the differences in phase between the corresponding active circuits. A snapshot of Darwin VIIIs neural responses during a typical behavioral run is given in Figure 8. The figure shows Darwin VIII during an approach to a red diamond target and a green diamond distracter towards the end of a training session. Each pixel in the depicted neural areas represents the activity and phase of a single neuronal unit. The phase is indicated by the color of each pixel and the activity is indicated by brightness of the pixel (black is no activity; very bright is maximum activity). The figure shows two neural circuits which are differentiated by their distinct phases and which were elicited respectively by the red diamond and the green diamond stimuli. As shown in the figure, Darwin VIII has not yet reached the beam that triggers the speaker to emit a tone. The activity of area S was nonetheless in phase with the activity in areas V2 and V4 corresponding to the target, and was therefore predictive of the targets saliency or value. Area IT has two patterns of activity, indicated by the two different phase colors, which reflect two perceptual categories. These patterns were brought about by visual input that is generated during Darwin VIIIs movement. Finally, area C has more activity on the side that facilitates orientation towards the target (i.e. the red diamond).
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The Influence of Reentry on Neural Dynamics
Lesioning of reentrant connections interfered significantly with interactions between local and global processes. Even in a very simple network model, removal of reentrant connections can prevent the emergence of neural synchrony (see Fig. 6). On a larger scale, Figure 9B shows approaches by the same Darwin subject depicted in Figure 9A to the same target/distracter pair, following lesions of inter-areal excitatory reentrant connections. While some individual areas continued to show peaks in their phase distribution (e.g. V4R), many did not, and the phase correlations between areas were severely diminished. This occurred not only among the various V4 areas, but also among V4 and areas S, IT, and C. The dynamically formed and globally coherent circuits, which were clearly evident in the intact subject, were almost entirely absent in the lesioned subjects. For example, Figure 9B shows that activity in area S no longer correlates uniquely with a single trace in V4, instead, it alternates between two distinct states. The absence of a dominant trace in IT and C is also evident.
Phase correlations between neural areas were significantly higher for subjects with intact reentrant connections than for subjects in either lesion group (P << 0.0001; Wilcoxon sign ranked test). The overall median Spearmans rank correlation coefficient was 0.36 for the intact subjects, 0.21 for the subjects with lesions only during the test stage, and 0.17 for the subjects with lesions in both the training and test stages. It is also notable that subjects with lesions only during testing had significantly higher correlation coefficients than subjects with lesions during both training and testing (P << 0.0001; Wilcoxon sign ranked test). This reflects the contribution of reentrant connections to the formation of global circuits during training. All of these findings are consistent with the drop in behavioral performance in the absence of reentrant connections (see Fig. 7).
A representative example of the correlation of phases among neural areas is given in Figure 10 for Subject 1 after conditioning to prefer red diamond targets. The figure is color coded (dark blue denotes no correlation, dark red denotes high correlation), and each colored area shows the correlation coefficient between the mean phases of a given pair of neural areas. Figure 10A shows correlation coefficients when reentrant connections were intact. In agreement with the data shown in Figure 9, strong phase correlations were found between areas associated with specific target features (V4D and V4R), and among these areas and areas S, IT and C. The correlations among neural areas for the same subject with reentrant connections lesioned during testing (Fig. 10B) and with reentrant connections lesioned during both conditioning and testing (Fig. 10C) were both considerably weaker.
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Because images of the visual objects varied considerably in size and position as Darwin VIII explored its enclosure, successful discrimination required invariant object recognition. In order to analyze this capacity, we examined the value system, area S, which, after conditioning, responded preferentially to target objects over distracters due to plasticity in the pathway ITS. In a typical approach, as Darwin moved from one side of the enclosure to the other, area S responded briskly and in phase with neuronal units in areas V2, V4 and IT corresponding to attributes of the target. Calculating average values over all Darwin VIII subjects and all target shapes, we found that area S responded reliably to target images which appeared within ±20° of the center of Darwin VIIIs field of view (the range of the visual field was
±35°) and as the apparent target size ranged from 8 to 27° of visual angle (see Fig. 11). Thus, Darwin VIIIs object recognition was both position and scale invariant.
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As a result of value-dependent synaptic plasticity during conditioning, the visual attributes of target objects became predictive of value. Figure 12 illustrates neural dynamics from a single, intact Darwin VIII subject during both early and late stages of conditioning. As shown in Figure 12 (left), during early conditioning area S does not become active until the UCS (unconditioned stimulus; i.e. the tone) is present. The UCS also evokes biases in areas IT and C, as shown by the rapid abolition of the initially bimodal phase distributions in these areas.
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Discussion |
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As with previous brain-based devices (Edelman et al., 1992; Almassy et al., 1998
; Krichmar et al., 2000
; Krichmar and Edelman, 2002
), Darwin VIII had innately specified behavior (i.e. tracking towards auditory or visual stimuli) and innately specified value or salience for certain environmental signals (e.g. positive value of sound). Darwin VIII learned autonomously to associate the value of the sound with the attributes of the visual stimulus closest to the sound source. In every test trial, it successfully oriented towards the target object based on visual attributes alone (see Fig. 7A).
The physical embodiment of Darwin VIII was essential for incorporating many of the challenging aspects of this discrimination task, such as variations in the position, scale and luminosity of visual images, sound reflections and slippages during movement. Reliance on elaborate computer simulations risks introducing a priori biases in the form of implicit instructions governing interactions between an agent and its environment (Brooks, 1991; Krichmar and Edelman, 2002
). By the use of a real-world environment, not only is the risk of introducing such biases avoided, but also the experimenter is freed from the substantial burden of constructing a highly complex simulated environment (Edelman et al., 1992
).
The simulated nervous system of Darwin VIII contained cortical areas analogous to the ventral occipito-temporal stream of the visual system (areas V2, V4 and IT) and the motor system (area C), as well as reward or value systems (area S) analogous to diffuse ascending neuromodulatory systems. However, none of these specialized areas or preferential directions of information flow (e.g. top-down or bottom-up) were by themselves sufficient for binding the features of visual objects. Rather, visual binding in Darwin VIII was achieved through the interaction of local processes (i.e. activity in each simulated neural area) and global processes (i.e. emergent functional circuits characterized by synchronous activity distributed throughout the simulated nervous system). Reentrant connections among distributed neural areas were found to be essential for the formation of these circuits (see Figs 9, 10, and 12) and for successful performance in a task requiring discrimination between multiple objects with shared features (see Fig. 7). It was striking that reliable discriminations were achieved by Darwin VIII despite the continual changes in the scale and position of stimuli in the visual field which resulted from self-generated movement in a rich real-world environment (see Fig. 11).
The state of each neuronal unit in Darwin VIII was described by both a firing rate variable and a phase variable, where postsynaptic phase tends to be correlated with the phase of the most strongly active presynaptic inputs. This modeling strategy provided the temporal precision needed to represent neural synchrony, without incurring the computational costs associated with modeling of the spiking activity of individual neurons. While representation of precise spike timing is necessary for modeling certain neuronal interactions (Senn et al., 2001; Song and Abbott, 2001
), our model suggests that for the purposes of illustrating a possible mechanism for visual binding, such detail is not required. It is also important to emphasize that phase in our model is not intended as a reflection of possible underlying oscillatory activity; specifically, it should not be taken to imply that regular brain oscillations at specific frequencies are an essential component of the neural mechanisms of binding (Gray and Singer, 1989
).
Although local regions in Darwin VIIIs simulated nervous system had segregated functions based on their input and connectivity, object recognition and discriminative behavior was an emergent property of the whole system, not of any individual area. Darwin VIIIs neural responses during an orienting movement toward a target showed this global property in terms of synchronized activity among a dynamic set of neuronal units in different neural areas (see Figures 8 and 9A). The simultaneous viewing of two objects clearly evoked two distinct sets of circuits that were distributed throughout the simulated nervous system and distinguished by differences in the relative timing of their activity. When the reentrant connections between neural areas were removed via simulated lesions, coherent interactions among Darwin VIIIs neural areas were disrupted (see Figs 9B and 10B,C) resulting in failures in both perceptual categorization and discriminative behavior (see Fig. 7B).
Both experience and value shape the global properties of the simulated nervous system. This is clearly shown in Figure 12, where, during early training, area S showed no activity and area C showed no bias toward the target object until the onset of the auditory cue. Late in the training, area S became active well before the auditory cue onset as a result of the value-dependent plastic connections from IT to S. Activity in area S therefore became predictive of the unconditioned stimuli (i.e. the auditory tone). Value-dependent plastic connections from IT to C and excitatory connections from S to C ensured that this shift in the timing of value-related activity resulted in a bias in the activity of area C which favored movement toward the target in preference to the distracter. These observations emphasize the role of value systems in modifying the efficacy of distributed neural connections to assure adaptive behavior.
Successful performance in the discrimination task required the complementary action of neural synchrony and experience-dependent changes in neuronal firing rates (see Table 3). Neuronal synchrony, which was indicated by groups of neuronal units sharing a similar phase, was necessary for the formation of multiple global circuits corresponding to each object in view. At the same time, the activity of the neuronal units within these circuits influenced activity levels in areas V4, IT and C, causing Darwin VIII to favor the target over the distracters. These observations suggest that mean firing rate codes (Shadlen and Movshon, 1999) and synchrony-based codes (Gray, 1999
; Singer, 1999
) need not be considered as mutually exclusive explanations of neuronal function.
A prediction of our model, in which neuronal units represent the activity of small groups of neurons, is that neural synchrony at the group level, rather than zero phase lag among individual neurons, may be sufficient for sensory binding. Although some single-unit recording studies have shown that neurons activated by attended stimuli are more synchronized than neurons activated by unattended stimuli (Steinmetz et al., 2000; Fries et al., 2001
), synchronous activity among single units has been difficult to detect in tasks requiring binding (Thiele and Stoner, 2003
). Also, microelectrode recordings from primate prefrontal cortex have shown higher levels of correlated firing among local, inhibitory neurons than among excitatory, long-range pyramidal neurons (Constantinidis and Goldman-Rakic, 2002
). On the other hand, neuromagnetic recordings of human subjects during binocular rivalry have shown an increase in the intra- and inter-hemispheric coherence of signals associated with a perceptually dominant stimulus, as compared to a stimulus which is not consciously perceived (Srinivasan et al., 1999
). However, neuromagnetic signals do not reflect reentrant relations between single neurons; rather, they represent averages across large neuronal populations. This evidence is therefore consistent with our model in suggesting that synchrony can operate at a neuronal group level as well as at the single neuron level.
Higher brain function depends on the cooperative activity of the entire nervous system, reflecting its morphology, its dynamics, and its interactions with the body and the environment. In accord with theoretical views emphasizing the importance of binding through synchrony (Edelman, 1993; Singer, 1999
; Engel et al., 2001
), we found that visual binding and object discrimination can arise as a result of the constraints reentry and behavior impose on interactions between local processes (activity in particular neural areas) and global processes (synchronously active and broadly distributed neural circuits). This interaction between these processes was essential, and neither specialized areas nor deterministic preferential directions of information flow were alone sufficient to achieve visual binding.
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Appendix: Specifics of Sensory Input and Computation in Darwin VIII |
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The CCD camera sent 320 x 240 pixel RGB video images, via an RF transmitter, to an ImageNation PXC200 frame grabber attached to one of the workstations running the neural simulation (see Appendix C). The image was spatially averaged to produce an 80 x 60 pixel image. Gabor filters were used to detect edges of vertical, horizontal, and diagonal (45 and 135°) orientations. The output of the Gabor function mapped directly onto the neuronal units of the corresponding V1 sub-area. Color filters (red positive center with a green negative surround, or red negative center with a green positive surround) were applied to the image. The outputs of the color filters were mapped directly onto the neuronal units of V1-red and V1-green. V1 neuronal units projected retinotopically to neuronal units in V2 (see Fig. 2 and Table 2).
B. Auditory System and its Inputs
Microphone input was amplified and filtered in hardware. An RMS (root mean square) chip measured the amplitude of the signal and a comparator chip produced a square waveform which allowed frequency to be measured. Every millisecond, the microcontroller on NOMAD calculated the overall microphone amplitude by averaging the current signal amplitude measurement with the previous three measurements. The microcontroller calculated the frequency of the microphone signal at each time point by inverting the average period of the last eight square waves. Mic-left and Mic-right responded only to tones between 2.9 and 3.5 kHz having an amplitude of at least 40% of the maximum. The activity of a neuronal unit in Mic-left or Mic-right was given by
where
is the previous value of a neuronal unit i in Mic-left or Mic-right, and
is the current amplitude of the microphone output. This equation served to smooth out spurious noise in the auditory signal.
C. Computation
The simulated nervous system was implemented on a Beowulf cluster of 12 x 1.4 GHz Pentium IV workstations running Message Passing Interface (MPI) parallel software under the Linux operating system. One of the workstations received visual input via RF video transmission from a CCD camera mounted on NOMAD, the mobile base (see Appendix A). The workstation also received auditory (see Appendix B) and infrared detector information, and transmitted motor and actuator commands via an RF modem. A microcontroller (PIC17C756A) onboard NOMAD sampled input and status from the sensors and controlled RS-232 communication between the NOMAD base and the workstations running the neural simulation.
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Notes |
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Address correspondence to Anil K. Seth, W.M. Keck Laboratory, The Neurosciences Institute, 10640 John Jay Hopkins Drive, San Diego, CA 92121, USA. Email: seth{at}nsi.edu.
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References |
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