An in-silico Neural Model of Dynamic Routing through Neuronal Coherence
Authors: Devarajan Sridharan, Brian Percival, John Arthur, Kwabena A. Boahen
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: We describe a neurobiologically plausible model to implement dynamic routing using the concept of neuronal communication through neuronal coherence. The model has a three-tier architecture: a raw input tier, a routing control tier, and an invariant output tier. The correct mapping between input and output tiers is re- alized by an appropriate alignment of the phases of their respective background oscillations by the routing control units. We present an example architecture, im- plemented on a neuromorphic chip, that is able to achieve circular-shift invariance. A simple extension to our model can accomplish circular-shift dynamic routing with only O(N) connections, compared to O(N 2) connections required by tradi- tional models.
1 Dynamic Routing Circuit Models for Circular-Shift Invariance
Dynamic routing circuit models are among the most prominent neural models for invariant recogni- tion [1] (also see [2] for review). These models implement shift invariance by dynamically changing spatial connectivity to transform an object to a standard position or orientation. The connectivity between the raw input and invariant output layers is controlled by routing units, which turn certain subsets of connections on or off (Figure 1A). An important feature of this model is the explicit rep- resentation of what and where information in the main network and the routing units, respectively; the routing units use the where information to create invariant representations.
Traditional solutions for shift invariance are neurobiologically implausible for at least two reasons. First, there are too many synaptic connections: for N input neurons, N output neurons and N possible input-output mappings, the network requires O(N 2) connections in the routing layer— between each of the N routing units and each set of N connections that that routing unit gates (Figure 1A). Second, these connections must be extremely precise: each routing unit must activate an input- output mapping (N individual connections) corresponding to the desired shift (as highlighted in Figure 1A). Other approaches that have been proposed, including invariant feature networks [3,4], also suffer from signiﬁcant drawbacks, such as the inability to explicitly represent where information [2]. It remains an open question how biology could achieve shift invariance without proﬂigate and precise connections.
In this article, we propose a simple solution for shift invariance for quantities that are circular or periodic in nature—circular-shift invariance (CSI)—orientation invariance in vision and key invari- ance in music. The visual system may create orientation-invariant representations to aid recognition under conditions of object rotation or head-tilt [5,6]; a similar mechanism could be employed by the auditory system to create key-invariant representations under conditions where the same melody
1
Figure 1: Dynamic routing. A In traditional dynamic routing, connections from the (raw) input layer to the (invariant) output layer are gated by routing units. For instance, the mapping from A to 5, B to 6, . . . , F to 4 is achieved by turning on the highlighted routing unit. B In time-division multiplexing (TDM), the encoder samples input channels periodically (using a rotating switch) while the decoder sends each sample to the appropriate output channel (based on its time bin). TDM can be extended to achieve a circular-shift transformation by altering the angle between encoder and decoder switches (θ), thereby creating a rotated mapping between input and output channels (adapted from [7]).
is played in different keys. Similar to orientation, which is a periodic quantity, musical notes one octave apart sound alike, a phenomenon known as octave equivalence [8]. Thus, the problems of key invariance and orientation invariance admit similar solutions.
Deriving inspiration from time-division multiplexing (TDM), we propose a neural network for CSI that uses phase to encode and decode information. We modulate the temporal window of commu- nication between (raw) input and (invariant) output neurons to achieve the appropriate input–output mapping. Extending TDM, any particular circular-shift transformation can be accomplished by changing the relative angle, θ, between the rotating switches of the encoder (that encodes the raw input in time) and decoder (that decodes the invariant output in time) (Figure 1B). This obviates the need to hardwire routing control units that speciﬁcally modulate the strength of each possible input- output connection, thereby signiﬁcantly reducing the complexity inherent in the traditional dynamic routing solution. Similarly, a remapping between the input and output neurons can be achieved by introducing a relative phase-shift in their background oscillations.
2 Dynamic Routing through Neuronal Coherence
To modulate the temporal window of communication, the model uses a ring of neurons (the oscilla- tion ring) to select the pool of neurons (in the projection ring) that encode or decode information at a particular time (Figure 2A). Each projection pool encodes a speciﬁc value of the feature (for exam- ple, one of twelve musical notes). Upon activation by external input, each pool is active only when background inhibition generated by the oscillation ring (outer ring of neurons) is at a minimum. In addition to exciting 12 inhibitory interneurons in the projection ring, each oscillation ring neuron excites its nearest 18 neighbors in the clockwise direction around the oscillation ring. As a result, a wave of inhibition travels around the projection ring that allows only one pool to be excitable at any point in time. These neurons become excitable at roughly the same time (numbered sectors, inner ring) by virtue of recurrent excitatory intra-pool connections.
Decoding is accomplished by a second tier of rings (Figure 2B). The projection ring of the ﬁrst (in- put) tier connects all-to-all to the projection ring of the second (output) tier. The two oscillation rings create a window of excitability for the pools of neurons in their respective projection rings. Hence, the most effective communication occurs between input and output pools that become excitable at the same time (i.e. are oscillating in phase with one another [9]).
The CSI problem is solved by introducing a phase-shift between the input and output tiers. If they are exactly in phase, then an input pool is simply mapped to the output pool directly above it. If their
2
Figure 2: Double-Ring Network for Encoding and Decoding. A The projection (inner) ring is divided into (numbered) pools. The oscillation (outer) ring modulates sub-threshold activity (wave- forms) of the projection ring by exciting (black distribution) inhibitory neurons that inhibit neigh- boring projection neurons. A wave of activity travels around the oscillation ring due to asymmetric excitatory connections, creating a corresponding wave of inhibitory activity in the projection ring, such that only one pool of projection neurons is excitable (spikes) at a given time. B Two instances of the double-ring structure from A. The input projection ring connects all-to-all to the output pro- jection ring (dashed lines). Because each input pool will spike only during a distinct time bin, and each output pool is excitable only in a certain time bin, communication occurs between input and output pools that are oscillating in phase with each other. Appropriate phase offset between input and output oscillation rings realizes the desired circular shift (input pool H to output pool 1, solid arrow). C Interactions among pools highlighted in B.
phases are different, the input is dynamically routed to an appropriate circularly shifted position in the output tier. Such changes in phase are analogous to adjusting the angle of the rotating switch at either the encoder or the decoder in TDM (see Figure 1B). There is some evidence that neural systems could employ phase relationships of subthreshold oscillations to selectively target neural populations [9-11].
3 Implementation in Silicon
We implemented this solution to CSI on a neuromorphic silicon chip [12]. The neuromorphic chip has neurons whose properties resemble that of biological neurons; these neurons even have intrin- sic differences, thereby mimicking heterogeneity in real neurobiological systems. The chip uses a conductance-based spiking model for both inhibitory and excitatory neurons. Inhibitory neurons project to nearby excitatory and inhibitory neurons via a diffusor network that determines the spread of inhibition. A lookup table of excitatory synaptic connectivity is stored in a separate random- access memory (RAM) chip. Spikes occurring on-chip are converted to a neuron address, mapped to synapses (if any) via the lookup table, and routed to the targeted on-chip synapse. A universal serial bus (USB) interface chip communicates spikes to and from a computer, for external input and
3
Figure 3: Traveling-wave activity in the oscillation ring. A Population activity (5ms bins) of a pool of eighteen (adjacent) oscillation neurons. B Increasing the strength of feedforward excitation led to increasing frequencies of periodic ﬁring in the θ and α range (1-10 Hz). Strength of excitation is the amplitude change in post-synaptic conductance due to a single pre-synaptic spike (measured relative to minimum amplitude used).
data analysis, respectively. Simulations on the chip occur in real-time, making it an attractive option for implementing the model.
We conﬁgured the following parameters:
• Magnitude of a potassium M-current: increasing this current’s magnitude increased the post-spike repolarization time of the membrane potential, thereby constraining spiking to a single time bin per cycle.
• The strength of excitatory and inhibitory synapses: a correct balance had to be established between excitation and inhibition to make only a small subset of neurons in the projection rings ﬁre at a time—too much excitation led to widespread ﬁring and too much inhibition led to neurons that were entirely silent or ﬁred sporadically.
• The space constant of inhibitory spread: increasing the spread was effective in preventing
runaway excitation, which could occur due to the recurrent excitatory connections.
We were able to create a stable traveling wave of background activity within the oscillation ring. We transiently stimulated a small subset of the neurons, which initiated a wave of activity that propagated in a stable manner around the ring after the transient external stimulation had ceased (Figure 3A). The network frequency determined from a Fourier transform of the network activity smoothed with a non-causal Gaussian kernel (FDHM = 80ms) was 7.4Hz. The frequency varied with the strength of the neurons’ excitatory connections (Figure 3B), measured as the amplitude of the step increase in membrane conductivity due to the arrival of a pre-synaptic spike. Over much of the range of the synaptic strengths tested, we observed stable oscillations in the θ and α bands (1-10Hz); the frequency appeared to increase logarithmically with synaptic strength.
4 Phase-based Encoding and Decoding
In order to assess the best-case performance of the model, the background activity in the input and output projection rings was derived from the input oscillation ring. Their spikes were delivered to the appropriately circularly-shifted output oscillation neurons. The asymmetric feedforward con- nections were disabled in the output oscillation ring. For instance, in order to achieve a circular shift by k pools (i.e. mapping input projection pool 1 to output projection pool k + 1, input pool 2 to output pool k + 2, and so on), activity from the input oscillation neurons closest to input pool 1 was fed into the output oscillation neurons closest to output pool k. By providing the appropriate phase difference between input and output oscillation, we were able to assess the performance of the model under ideal conditions. In the Discussion section, we discuss a biologically plausible mechanism to control the relative phases.
4
Figure 4: Phase-based encoding. Rasters indicating activity of projection pools in 1ms bins, and mean phase of ﬁring (×’s) for each pool (relative to arbitrary zero time). The abscissa shows ﬁring time normalized by the period of oscillation (which may be converted to ﬁring phase by multiplica- tion by 2π). Under constant input to the input projection ring, the input pools ﬁre approximately in sequence. Two cycles of pool activity normalized by maximum ﬁring rate for each pool are shown in left inset (for clarity, pools 1-6 are shown in the top panel and pools 7-12 are shown separately in the bottom panel); phase of background inhibition of pool 4 is shown (below) for reference. Phase-aligned average1 of activity (right inset) showed that the ﬁring times were relatively tight and uniform across pools: a standard deviation of 0.0945 periods, or equivalently, a spread of 1.135 pools at any instant of time.
We veriﬁed that the input projection pools ﬁred in a phase-shifted fashion relative to one another, a property critical for accurate encoding (see Figure 2). We stimulated all pools in the input pro- jection ring simultaneously while the input oscillation ring provided a periodic wave of background inhibition. The mean phase of ﬁring for each pool (relative to arbitrary zero time) increased nearly linearly with pool number, thereby providing evidence for accurate, phase-based encoding (Figure 4). The ﬁring times of all pools are shown for two cycles of background oscillatory activity (Figure 4 left inset). A phase-aligned average1 showed that the timing was relatively tight (standard deviation 1.135 pools) and uniform across pools of neurons (Figure 4 right inset).
We then characterized the system’s ability to correctly decode this encoding under a given circular shift. The shift was set to seven pools, mapping input pool 1 to output pool 8, and so on. Each input pool was stimulated in turn. We expected to see only the appropriately shifted output pool become highly active. In fact, not only was this pool active, but other pools around it were also active, though to a lesser extent (Figure 5A). Thus, the phase-encoded input was decoded successfully, and circularly shifted, except that the output units were broadly tuned.
To quantify the overall precision of encoding and decoding, we constructed an input-locked aver- age of the tuning curves (Figure 5B): the curves were circularly shifted to the left by an amount corresponding to the stimulated input pool number, and the raw pool ﬁring rates were averaged. If the phase-based encoding and decoding were perfect, the peak should occur at a shift of 7 pools.
1The phase-aligned average was constructed by shifting the pool-activity curves by the (# of the pool) ×
12 of the period) to align activity across pools, which was then averaged. ( 1
5
Figure 5: Decoding phase-encoded input. A In order to assess decoding performance under a given circular shift (here 7 pools) each input pool was stimulated in turn and activity in each output pool was recorded and averaged over 500ms. The pool’s response, normalized by its maximum ﬁring rate, is plotted for each stimulated input pool (arrows pointing to curves, color code as in Figure 4). Each input pool stimulation trial consistently resulted in peak activity in the appropriate output pool; however, adjacent pools were also active, but to a lesser extent, resulting in a broad tuning curve. B The best-ﬁt Gaussian (dot-dashed grey curve, σ = 2.30 pools) to the input-locked average of the raw pool ﬁring rates (see text for details) revealed a maximum between a shift of 7 and 8 pools (inverted grey triangle; expected peak at a shift of 7 pools).
Indeed, the highest (average) ﬁring rate corresponded to a shift of 7 pools. However, the activity corresponding to a shift of 8 pools was nearly equal to that of 7 pools, and the best ﬁtting Gaus- sian curve to the activity histogram (grey dot-dashed line) peaked at a point between pools 7 and 8 (inverted grey triangle). The standard deviation (σ) was 2.30 pools, versus the expected ideal σ of 1.60, which corresponds to the encoding distribution (σ = 1.135 pools) convolved with itself.