nips nips2004 nips2004-28 knowledge-graph by maker-knowledge-mining

28 nips-2004-Bayesian inference in spiking neurons

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Author: Sophie Deneve

Abstract: We propose a new interpretation of spiking neurons as Bayesian integrators accumulating evidence over time about events in the external world or the body, and communicating to other neurons their certainties about these events. In this model, spikes signal the occurrence of new information, i.e. what cannot be predicted from the past activity. As a result, ﬁring statistics are close to Poisson, albeit providing a deterministic representation of probabilities. We proceed to develop a theory of Bayesian inference in spiking neural networks, recurrent interactions implementing a variant of belief propagation. Many perceptual and motor tasks performed by the central nervous system are probabilistic, and can be described in a Bayesian framework [4, 3]. A few important but hidden properties, such as direction of motion, or appropriate motor commands, are inferred from many noisy, local and ambiguous sensory cues. These evidences are combined with priors about the sensory world and body. Importantly, because most of these inferences should lead to quick and irreversible decisions in a perpetually changing world, noisy cues have to be integrated on-line, but in a way that takes into account unpredictable events, such as a sudden change in motion direction or the appearance of a new stimulus. This raises the question of how this temporal integration can be performed at the neural level. It has been proposed that single neurons in sensory cortices represent and compute the log probability that a sensory variable takes on a certain value (eg Is visual motion in the neuron’s preferred direction?) [9, 7]. Alternatively, to avoid normalization issues and provide an appropriate signal for decision making, neurons could represent the log probability ratio of a particular hypothesis (eg is motion more likely to be towards the right than towards the left) [7, 6]. Log probabilities are convenient here, since under some assumptions, independent noisy cues simply combine linearly. Moreover, there are physiological evidence for the neural representation of log probabilities and log probability ratios [9, 6, 7]. However, these models assume that neurons represent probabilities in their ﬁring rates. We argue that it is important to study how probabilistic information are encoded in spikes. Indeed, it seems spurious to marry the idea of an exquisite on-line integration of noisy cues with an underlying rate code that requires averaging on large populations of noisy neurons and long periods of time. In particular, most natural tasks require this integration to take place on the time scale of inter-spike intervals. Spikes are more efﬁciently signaling events ∗ Institute of Cognitive Science, 69645 Bron, France than analog quantities. In addition, a neural theory of inference with spikes will bring us closer to the physiological level and generate more easily testable predictions. Thus, we propose a new theory of neural processing in which spike trains provide a deterministic, online representation of a log-probability ratio. Spikes signals events, eg that the log-probability ratio has exceeded what could be predicted from previous spikes. This form of coding was loosely inspired by the idea of ”energy landscape” coding proposed by Hinton and Brown [2]. However, contrary to [2] and other theories using rate-based representation of probabilities, this model is self-consistent and does not require different models for encoding and decoding: As output spikes provide new, unpredictable, temporally independent evidence, they can be used directly as an input to other Bayesian neurons. Finally, we show that these neurons can be used as building blocks in a theory of approximate Bayesian inference in recurrent spiking networks. Connections between neurons implement an underlying Bayesian network, consisting of coupled hidden Markov models. Propagation of spikes is a form of belief propagation in this underlying graphical model. Our theory provides computational explanations of some general physiological properties of cortical neurons, such as spike frequency adaptation, Poisson statistics of spike trains, the existence of strong local inhibition in cortical columns, and the maintenance of a tight balance between excitation and inhibition. Finally, we discuss the implications of this model for the debate about temporal versus rate-based neural coding. 1 Spikes and log posterior odds 1.1 Synaptic integration seen as inference in a hidden Markov chain We propose that each neuron codes for an underlying ”hidden” binary variable, xt , whose state evolves over time. We assume that xt depends only on the state at the previous time step, xt−dt , and is conditionally independent of other past states. The state xt can switch 1 from 0 to 1 with a constant rate ron = dt limdt→0 P (xt = 1|xt−dt = 0), and from 1 to 0 with a constant rate roﬀ . For example, these transition rates could represent how often motion in a preferred direction appears the receptive ﬁeld and how long it is likely to stay there. The neuron infers the state of its hidden variable from N noisy synaptic inputs, considered to be observations of the hidden state. In this initial version of the model, we assume that these inputs are conditionally independent homogeneous Poisson processes, synapse i i emitting a spike between time t and t + dt (si = 1) with constant probability qon dt if t i xt = 1, and another constant probability qoﬀ dt if xt = 0. The synaptic spikes are assumed to be otherwise independent of previous synaptic spikes, previous states and spikes at other synapses. The resulting generative model is a hidden Markov chain (ﬁgure 1-A). However, rather than estimating the state of its hidden variable and communicating this estimate to other neurons (for example by emitting a spike when sensory evidence for xt = 1 goes above a threshold) the neuron reports and communicates its certainty that the current state is 1. This certainty takes the form of the log of the ratio of the probability that the hidden state is 1, and the probability that the state is 0, given all the synaptic inputs P (xt =1|s0→t ) received so far: Lt = log P (xt =0|s0→t ) . We use s0→t as a short hand notation for the N synaptic inputs received at present and in the past. We will refer to it as the log odds ratio. Thanks to the conditional independencies assumed in the generative model, we can compute this Log odds ratio iteratively. Taking the limit as dt goes to zero, we get the following differential equation: ˙ L = ron 1 + e−L − roﬀ 1 + eL + i wi δ(si − 1) − θ t B. A. xt ron .roff dt qon , qoff st xt ron .roff i t st dt s qon , qoff qon , qoff st dt xt j st Ot It Gt Ot Lt t t dt C. E. 2 0 -2 -4 D. 500 1000 1500 2000 2500 2 3000 Count Log odds 4 20 Lt 0 -2 0 500 1000 1500 2000 2500 Time Ot 3000 0 200 400 600 ISI Figure 1: A. Generative model for the synaptic input. B. Schematic representation of log odds ratio encoding and decoding. The dashed circle represents both eventual downstream elements and the self-prediction taking place inside the model neuron. A spike is ﬁred only when Lt exceeds Gt . C. One example trial, where the state switches from 0 to 1 (shaded area) and back to 0. plain: Lt , dotted: Gt . Black stripes at the top: corresponding spikes train. D. Mean Log odds ratio (dark line) and mean output ﬁring rate (clear line). E. Output spike raster plot (1 line per trial) and ISI distribution for the neuron shown is C. and D. Clear line: ISI distribution for a poisson neuron with the same rate. wi , the synaptic weight, describe how informative synapse i is about the state of the hidden i qon variable, e.g. wi = log qi . Each synaptic spike (si = 1) gives an impulse to the log t off odds ratio, which is positive if this synapse is more active when the hidden state if 1 (i.e it increases the neuron’s conﬁdence that the state is 1), and negative if this synapse is more active when xt = 0 (i.e it decreases the neuron’s conﬁdence that the state is 1). The bias, θ, is determined by how informative it is not to receive any spike, e.g. θ = i i i qon − qoﬀ . By convention, we will consider that the ”bias” is positive or zero (if not, we need simply to invert the status of the state x). 1.2 Generation of output spikes The spike train should convey a sparse representation of Lt , so that each spike reports new information about the state xt that is not redundant with that reported by other, preceding, spikes. This proposition is based on three arguments: First, spikes, being metabolically expensive, should be kept to a minimum. Second, spikes conveying redundant information would require a decoding of the entire spike train, whereas independent spike can be taken into account individually. And ﬁnally, we seek a self consistent model, with the spiking output having a similar semantics to its spiking input. To maximize the independence of the spikes (conditioned on xt ), we propose that the neuron ﬁres only when the difference between its log odds ratio Lt and a prediction Gt of this log odds ratio based on the output spikes emitted so far reaches a certain threshold. Indeed, supposing that downstream elements predicts Lt as best as they can, the neuron only needs to ﬁre when it expects that prediction to be too inaccurate (ﬁgure 1-B). In practice, this will happen when the neuron receives new evidence for xt = 1. Gt should thereby follow the same dynamics as Lt when spikes are not received. The equation for Gt and the output Ot (Ot = 1 when an output spike is ﬁred) are given by: ˙ G = Ot = ron 1 + e−L − roﬀ 1 + eL + go δ(Ot − 1) go 1. when Lt > Gt + , 0 otherwise, 2 (1) (2) Here go , a positive constant, is the only free parameter, the other parameters being constrained by the statistics of the synaptic input. 1.3 Results Figure 1-C plots a typical trial, showing the behavior of L, G and O before, during and after presentation of the stimulus. As random synaptic inputs are integrated, L ﬂuctuates and eventually exceeds G + 0.5, leading to an output spike. Immediately after a spike, G jumps to G + go , which prevents (except in very rare cases) a second spike from immediately following the ﬁrst. Thus, this ”jump” implements a relative refractory period. However, ron G decays as it tends to converge back to its stable level gstable = log roff . Thus L eventually exceeds G again, leading to a new spike. This threshold crossing happens more often during stimulation (xt = 1) as the net synaptic input alters to create a higher overall level of certainty, Lt . Mean Log odds ratio and output ﬁring rate ¯ The mean ﬁring rate Ot of the Bayesian neuron during presentation of its preferred stimulus (i.e. when xt switches from 0 to 1 and back to 0) is plotted in ﬁgure 1-D, together with the ¯ mean log posterior ratio Lt , both averaged over trials. Not surprisingly, the log-posterior ratio reﬂects the leaky integration of synaptic evidence, with an effective time constant that depends on the transition probabilities ron , roﬀ . If the state is very stable (ron = roﬀ ∼ 0), synaptic evidence is integrated over almost inﬁnite time periods, the mean log posterior ratio tending to either increase or decrease linearly with time. In the example in ﬁgure 1D, the state is less stable, so ”old” synaptic evidence are discounted and Lt saturates. ¯ In contrast, the mean output ﬁring rate Ot tracks the state of xt almost perfectly. This is because, as a form of predictive coding, the output spikes reﬂect the new synaptic i evidence, It = i δ(st − 1) − θ, rather than the log posterior ratio itself. In particular, the mean output ﬁring rate is a rectiﬁed linear function of the mean input, e. g. + ¯ ¯ wi q i −θ . O= 1I= go i on(oﬀ) Analogy with a leaky integrate and ﬁre neuron We can get an interesting insight into the computation performed by this neuron by linearizing L and G around their mean levels over trials. Here we reduce the analysis to prolonged, statistically stable periods when the state is constant (either ON or OFF). In this case, the ¯ ¯ mean level of certainty L and its output prediction G are also constant over time. We make the rough approximation that the post spike jump, go , and the input ﬂuctuations are small ¯ compared to the mean level of certainty L. Rewriting Vt = Lt − Gt + go 2 as the ”membrane potential” of the Bayesian neuron: ˙ V = −kL V + It − ∆go − go Ot ¯ ¯ ¯ where kL = ron e−L + roﬀ eL , the ”leak” of the membrane potential, depends on the overall ¯ level of certainty. ∆go is positive and a monotonic increasing function of go . A. s t1 dt s t1 s t1 dt B. C. x t1 x t3 dt x t3 x t3 dt x t1 x t1 x t1 x t2 x t3 x t1 … x tn x t3 x t2 … x tn … dt dt Lx2 D. x t2 dt s t2 dt x t2 s t2 x t2 dt s t2 dt Log odds 10 No inh -0.5 -1 -1 -1.5 -2 5 Feedback 500 1000 1500 2000 Tiger Stripes 0 -5 -10 500 1000 1500 2000 2500 Time Figure 2: A. Bayesian causal network for yt (tiger), x1 (stripes) and x2 (paws). B. A nett t work feedforward computing the log posterior for x1 . C. A recurrent network computing t the log posterior odds for all variables. D. Log odds ratio in a simulated trial with the net2 1 1 work in C (see text). Thick line: Lx , thin line: Lx , dash-dotted: Lx without inhibition. t t t 2 Insert: Lx averaged over trials, showing the effect of feedback. t The linearized Bayesian neuron thus acts in its stable regime as a leaky integrate and ﬁre (LIF) neuron. The membrane potential Vt integrates its input, Jt = It − ∆go , with a leak kL . The neuron ﬁres when its membrane potential reaches a constant threshold go . After ¯ each spikes, Vt is reset to 0. Interestingly, for appropriately chosen compression factor go , the mean input to the lin¯ ¯ earized neuron J = I − ∆go ≈ 0 1 . This means that the membrane potential is purely driven to its threshold by input ﬂuctuations, or a random walk in membrane potential. As a consequence, the neuron’s ﬁring will be memoryless, and close to a Poisson process. In particular, we found Fano factor close to 1 and quasi-exponential ISI distribution (ﬁgure 1E) on the entire range of parameters tested. Indeed, LIF neurons with balanced inputs have been proposed as a model to reproduce the statistics of real cortical neurons [8]. This balance is implemented in our model by the neuron’s effective self-inhibition, even when the synaptic input itself is not balanced. Decoding As we previously said, downstream elements could predict the log odds ratio Lt by computing Gt from the output spikes (Eq 1, ﬁg 1-B). Of course, this requires an estimate of the transition probabilities ron , roﬀ , that could be learned from the observed spike trains. However, we show next that explicit decoding is not necessary to perform bayesian inference in spiking networks. Intuitively, this is because the quantity that our model neurons receive and transmit, eg new information, is exactly what probabilistic inference algorithm propagate between connected statistical elements. 1 ¯ Even if go is not chosen optimally, the inﬂuence of the drift J is usually negligible compared to the large ﬂuctuations in membrane potential. 2 Bayesian inference in cortical networks The model neurons, having the same input and output semantics, can be used as building blocks to implement more complex generative models consisting of coupled Markov chains. Consider, for example, the example in ﬁgure 2-A. Here, a ”parent” variable x1 t (the presence of a tiger) can cause the state of n other ”children” variables ([xk ]k=2...n ), t of whom two are represented (the presence of stripes,x2 , and motion, x3 ). The ”chilt t dren” variables are Bayesian neurons identical to those described previously. The resulting bayesian network consist of n + 1 coupled hidden Markov chains. Inference in this architecture corresponds to computing the log posterior odds ratio for the tiger, x1 , and the log t posterior of observing stripes or motion, ([xk ]k=2...n ), given the synaptic inputs received t by the entire network so far, i.e. s2 , . . . , sk . 0→t 0→t Unfortunately, inference and learning in this network (and in general in coupled Markov chains) requires very expensive computations, and cannot be performed by simply propagating messages over time and among the variable nodes. In particular, the state of a child k variable xt depends on xk , sk , x1 and the state of all other children at the previous t t t−dt time step, [xj ]2

Reference: text

Summary: the most important sentenses genereted by tfidf model

sentIndex sentText sentNum sentScore

1 Bayesian inference in spiking neurons Sophie Deneve∗ Gatsby Computational Neuroscience Unit University College London London, UK WC1N 3AR sdeneve@gatsby. [sent-1, score-0.316]

2 uk Abstract We propose a new interpretation of spiking neurons as Bayesian integrators accumulating evidence over time about events in the external world or the body, and communicating to other neurons their certainties about these events. [sent-4, score-0.613]

3 In this model, spikes signal the occurrence of new information, i. [sent-5, score-0.251]

4 We proceed to develop a theory of Bayesian inference in spiking neural networks, recurrent interactions implementing a variant of belief propagation. [sent-9, score-0.194]

5 A few important but hidden properties, such as direction of motion, or appropriate motor commands, are inferred from many noisy, local and ambiguous sensory cues. [sent-11, score-0.167]

6 These evidences are combined with priors about the sensory world and body. [sent-12, score-0.09]

7 This raises the question of how this temporal integration can be performed at the neural level. [sent-14, score-0.051]

8 It has been proposed that single neurons in sensory cortices represent and compute the log probability that a sensory variable takes on a certain value (eg Is visual motion in the neuron’s preferred direction? [sent-15, score-0.528]

9 Alternatively, to avoid normalization issues and provide an appropriate signal for decision making, neurons could represent the log probability ratio of a particular hypothesis (eg is motion more likely to be towards the right than towards the left) [7, 6]. [sent-17, score-0.45]

10 Log probabilities are convenient here, since under some assumptions, independent noisy cues simply combine linearly. [sent-18, score-0.124]

11 Moreover, there are physiological evidence for the neural representation of log probabilities and log probability ratios [9, 6, 7]. [sent-19, score-0.35]

12 However, these models assume that neurons represent probabilities in their ﬁring rates. [sent-20, score-0.201]

13 Indeed, it seems spurious to marry the idea of an exquisite on-line integration of noisy cues with an underlying rate code that requires averaging on large populations of noisy neurons and long periods of time. [sent-22, score-0.425]

14 In particular, most natural tasks require this integration to take place on the time scale of inter-spike intervals. [sent-23, score-0.051]

15 Spikes are more efﬁciently signaling events ∗ Institute of Cognitive Science, 69645 Bron, France than analog quantities. [sent-24, score-0.043]

16 In addition, a neural theory of inference with spikes will bring us closer to the physiological level and generate more easily testable predictions. [sent-25, score-0.387]

17 Thus, we propose a new theory of neural processing in which spike trains provide a deterministic, online representation of a log-probability ratio. [sent-26, score-0.258]

18 Spikes signals events, eg that the log-probability ratio has exceeded what could be predicted from previous spikes. [sent-27, score-0.171]

19 This form of coding was loosely inspired by the idea of ”energy landscape” coding proposed by Hinton and Brown [2]. [sent-28, score-0.066]

20 Finally, we show that these neurons can be used as building blocks in a theory of approximate Bayesian inference in recurrent spiking networks. [sent-30, score-0.359]

21 Connections between neurons implement an underlying Bayesian network, consisting of coupled hidden Markov models. [sent-31, score-0.298]

22 Propagation of spikes is a form of belief propagation in this underlying graphical model. [sent-32, score-0.251]

23 1 Synaptic integration seen as inference in a hidden Markov chain We propose that each neuron codes for an underlying ”hidden” binary variable, xt , whose state evolves over time. [sent-36, score-0.728]

24 We assume that xt depends only on the state at the previous time step, xt−dt , and is conditionally independent of other past states. [sent-37, score-0.341]

25 The state xt can switch 1 from 0 to 1 with a constant rate ron = dt limdt→0 P (xt = 1|xt−dt = 0), and from 1 to 0 with a constant rate roﬀ . [sent-38, score-0.868]

26 For example, these transition rates could represent how often motion in a preferred direction appears the receptive ﬁeld and how long it is likely to stay there. [sent-39, score-0.116]

27 The neuron infers the state of its hidden variable from N noisy synaptic inputs, considered to be observations of the hidden state. [sent-40, score-0.791]

28 In this initial version of the model, we assume that these inputs are conditionally independent homogeneous Poisson processes, synapse i i emitting a spike between time t and t + dt (si = 1) with constant probability qon dt if t i xt = 1, and another constant probability qoﬀ dt if xt = 0. [sent-41, score-1.743]

29 The synaptic spikes are assumed to be otherwise independent of previous synaptic spikes, previous states and spikes at other synapses. [sent-42, score-0.944]

30 The resulting generative model is a hidden Markov chain (ﬁgure 1-A). [sent-43, score-0.109]

31 However, rather than estimating the state of its hidden variable and communicating this estimate to other neurons (for example by emitting a spike when sensory evidence for xt = 1 goes above a threshold) the neuron reports and communicates its certainty that the current state is 1. [sent-44, score-1.493]

32 This certainty takes the form of the log of the ratio of the probability that the hidden state is 1, and the probability that the state is 0, given all the synaptic inputs P (xt =1|s0→t ) received so far: Lt = log P (xt =0|s0→t ) . [sent-45, score-0.993]

33 We use s0→t as a short hand notation for the N synaptic inputs received at present and in the past. [sent-46, score-0.307]

34 Thanks to the conditional independencies assumed in the generative model, we can compute this Log odds ratio iteratively. [sent-48, score-0.469]

35 Taking the limit as dt goes to zero, we get the following differential equation: ˙ L = ron 1 + e−L − roﬀ 1 + eL + i wi δ(si − 1) − θ t B. [sent-49, score-0.482]

36 roff i t st dt s qon , qoff qon , qoff st dt xt j st Ot It Gt Ot Lt t t dt C. [sent-53, score-1.56]

37 500 1000 1500 2000 2500 2 3000 Count Log odds 4 20 Lt 0 -2 0 500 1000 1500 2000 2500 Time Ot 3000 0 200 400 600 ISI Figure 1: A. [sent-56, score-0.327]

38 Schematic representation of log odds ratio encoding and decoding. [sent-59, score-0.534]

39 The dashed circle represents both eventual downstream elements and the self-prediction taking place inside the model neuron. [sent-60, score-0.065]

40 One example trial, where the state switches from 0 to 1 (shaded area) and back to 0. [sent-63, score-0.145]

41 Mean Log odds ratio (dark line) and mean output ﬁring rate (clear line). [sent-67, score-0.591]

42 Output spike raster plot (1 line per trial) and ISI distribution for the neuron shown is C. [sent-69, score-0.493]

43 Clear line: ISI distribution for a poisson neuron with the same rate. [sent-71, score-0.299]

44 wi , the synaptic weight, describe how informative synapse i is about the state of the hidden i qon variable, e. [sent-72, score-0.669]

45 Each synaptic spike (si = 1) gives an impulse to the log t off odds ratio, which is positive if this synapse is more active when the hidden state if 1 (i. [sent-75, score-1.115]

46 e it increases the neuron’s conﬁdence that the state is 1), and negative if this synapse is more active when xt = 0 (i. [sent-76, score-0.363]

47 e it decreases the neuron’s conﬁdence that the state is 1). [sent-77, score-0.109]

48 By convention, we will consider that the ”bias” is positive or zero (if not, we need simply to invert the status of the state x). [sent-81, score-0.109]

49 2 Generation of output spikes The spike train should convey a sparse representation of Lt , so that each spike reports new information about the state xt that is not redundant with that reported by other, preceding, spikes. [sent-83, score-1.13]

50 Second, spikes conveying redundant information would require a decoding of the entire spike train, whereas independent spike can be taken into account individually. [sent-85, score-0.77]

51 And ﬁnally, we seek a self consistent model, with the spiking output having a similar semantics to its spiking input. [sent-86, score-0.293]

52 To maximize the independence of the spikes (conditioned on xt ), we propose that the neuron ﬁres only when the difference between its log odds ratio Lt and a prediction Gt of this log odds ratio based on the output spikes emitted so far reaches a certain threshold. [sent-87, score-2.111]

53 Indeed, supposing that downstream elements predicts Lt as best as they can, the neuron only needs to ﬁre when it expects that prediction to be too inaccurate (ﬁgure 1-B). [sent-88, score-0.295]

54 In practice, this will happen when the neuron receives new evidence for xt = 1. [sent-89, score-0.505]

55 Gt should thereby follow the same dynamics as Lt when spikes are not received. [sent-90, score-0.251]

56 The equation for Gt and the output Ot (Ot = 1 when an output spike is ﬁred) are given by: ˙ G = Ot = ron 1 + e−L − roﬀ 1 + eL + go δ(Ot − 1) go 1. [sent-91, score-0.884]

57 when Lt > Gt + , 0 otherwise, 2 (1) (2) Here go , a positive constant, is the only free parameter, the other parameters being constrained by the statistics of the synaptic input. [sent-92, score-0.365]

58 As random synaptic inputs are integrated, L ﬂuctuates and eventually exceeds G + 0. [sent-95, score-0.342]

59 Immediately after a spike, G jumps to G + go , which prevents (except in very rare cases) a second spike from immediately following the ﬁrst. [sent-97, score-0.375]

60 However, ron G decays as it tends to converge back to its stable level gstable = log roff . [sent-99, score-0.375]

61 Thus L eventually exceeds G again, leading to a new spike. [sent-100, score-0.068]

62 This threshold crossing happens more often during stimulation (xt = 1) as the net synaptic input alters to create a higher overall level of certainty, Lt . [sent-101, score-0.277]

63 Mean Log odds ratio and output ﬁring rate ¯ The mean ﬁring rate Ot of the Bayesian neuron during presentation of its preferred stimulus (i. [sent-102, score-0.891]

64 when xt switches from 0 to 1 and back to 0) is plotted in ﬁgure 1-D, together with the ¯ mean log posterior ratio Lt , both averaged over trials. [sent-104, score-0.539]

65 Not surprisingly, the log-posterior ratio reﬂects the leaky integration of synaptic evidence, with an effective time constant that depends on the transition probabilities ron , roﬀ . [sent-105, score-0.702]

66 If the state is very stable (ron = roﬀ ∼ 0), synaptic evidence is integrated over almost inﬁnite time periods, the mean log posterior ratio tending to either increase or decrease linearly with time. [sent-106, score-0.789]

67 In the example in ﬁgure 1D, the state is less stable, so ”old” synaptic evidence are discounted and Lt saturates. [sent-107, score-0.404]

68 ¯ In contrast, the mean output ﬁring rate Ot tracks the state of xt almost perfectly. [sent-108, score-0.463]

69 This is because, as a form of predictive coding, the output spikes reﬂect the new synaptic i evidence, It = i δ(st − 1) − θ, rather than the log posterior ratio itself. [sent-109, score-0.815]

70 In particular, the mean output ﬁring rate is a rectiﬁed linear function of the mean input, e. [sent-110, score-0.193]

71 O= 1I= go i on(oﬀ) Analogy with a leaky integrate and ﬁre neuron We can get an interesting insight into the computation performed by this neuron by linearizing L and G around their mean levels over trials. [sent-113, score-0.724]

72 Here we reduce the analysis to prolonged, statistically stable periods when the state is constant (either ON or OFF). [sent-114, score-0.229]

73 In this case, the ¯ ¯ mean level of certainty L and its output prediction G are also constant over time. [sent-115, score-0.265]

74 We make the rough approximation that the post spike jump, go , and the input ﬂuctuations are small ¯ compared to the mean level of certainty L. [sent-116, score-0.531]

75 Rewriting Vt = Lt − Gt + go 2 as the ”membrane potential” of the Bayesian neuron: ˙ V = −kL V + It − ∆go − go Ot ¯ ¯ ¯ where kL = ron e−L + roﬀ eL , the ”leak” of the membrane potential, depends on the overall ¯ level of certainty. [sent-117, score-0.622]

76 ∆go is positive and a monotonic increasing function of go . [sent-118, score-0.144]

77 x t1 x t3 dt x t3 x t3 dt x t1 x t1 x t1 x t2 x t3 x t1 … x tn x t3 x t2 … x tn … dt dt Lx2 D. [sent-122, score-1.006]

78 x t2 dt s t2 dt x t2 s t2 x t2 dt s t2 dt Log odds 10 No inh -0. [sent-123, score-1.267]

79 A nett t work feedforward computing the log posterior for x1 . [sent-128, score-0.151]

80 A recurrent network computing t the log posterior odds for all variables. [sent-130, score-0.549]

81 Log odds ratio in a simulated trial with the net2 1 1 work in C (see text). [sent-132, score-0.479]

82 t The linearized Bayesian neuron thus acts in its stable regime as a leaky integrate and ﬁre (LIF) neuron. [sent-135, score-0.357]

83 The membrane potential Vt integrates its input, Jt = It − ∆go , with a leak kL . [sent-136, score-0.177]

84 The neuron ﬁres when its membrane potential reaches a constant threshold go . [sent-137, score-0.591]

85 Interestingly, for appropriately chosen compression factor go , the mean input to the lin¯ ¯ earized neuron J = I − ∆go ≈ 0 1 . [sent-139, score-0.414]

86 This means that the membrane potential is purely driven to its threshold by input ﬂuctuations, or a random walk in membrane potential. [sent-140, score-0.262]

87 Indeed, LIF neurons with balanced inputs have been proposed as a model to reproduce the statistics of real cortical neurons [8]. [sent-143, score-0.431]

88 This balance is implemented in our model by the neuron’s effective self-inhibition, even when the synaptic input itself is not balanced. [sent-144, score-0.221]

89 Decoding As we previously said, downstream elements could predict the log odds ratio Lt by computing Gt from the output spikes (Eq 1, ﬁg 1-B). [sent-145, score-0.931]

90 Of course, this requires an estimate of the transition probabilities ron , roﬀ , that could be learned from the observed spike trains. [sent-146, score-0.47]

91 However, we show next that explicit decoding is not necessary to perform bayesian inference in spiking networks. [sent-147, score-0.302]

92 Intuitively, this is because the quantity that our model neurons receive and transmit, eg new information, is exactly what probabilistic inference algorithm propagate between connected statistical elements. [sent-148, score-0.312]

93 1 ¯ Even if go is not chosen optimally, the inﬂuence of the drift J is usually negligible compared to the large ﬂuctuations in membrane potential. [sent-149, score-0.246]

94 2 Bayesian inference in cortical networks The model neurons, having the same input and output semantics, can be used as building blocks to implement more complex generative models consisting of coupled Markov chains. [sent-150, score-0.274]

95 Here, a ”parent” variable x1 t (the presence of a tiger) can cause the state of n other ”children” variables ([xk ]k=2. [sent-152, score-0.14]

96 The ”chilt t dren” variables are Bayesian neurons identical to those described previously. [sent-156, score-0.165]

97 The resulting bayesian network consist of n + 1 coupled hidden Markov chains. [sent-157, score-0.255]

98 Inference in this architecture corresponds to computing the log posterior odds ratio for the tiger, x1 , and the log t posterior of observing stripes or motion, ([xk ]k=2. [sent-158, score-0.835]

99 n ), given the synaptic inputs received t by the entire network so far, i. [sent-161, score-0.335]

100 0→t 0→t Unfortunately, inference and learning in this network (and in general in coupled Markov chains) requires very expensive computations, and cannot be performed by simply propagating messages over time and among the variable nodes. [sent-167, score-0.173]

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We will address these and other problems in Section 3, but now we report on our simulation results on demultiplexing. 2.1 Demultiplexing spike trains. An interesting possibility in the brain is that ‘patterns’ are embedded in spatially distributed spike timings that are input to neurons. Several patterns could be embedded in single input trains. This is called multiplexing. To extract and propagate these patterns, the neurons must demultiplex these inputs using its threshold nonlinearity. Demultiplexing is the ‘point process’ analog of the unmixing of independent inputs in ICA. We have been able to robustly achieve demultiplexing, as we now report. We simulated a feed-forward network with 3 integrate-and-ﬁre neurons and inputs from 3 presynaptic neurons. Learning followed (11) where we replace the inverse by the pseudoinverse computed on the spikes generated during 0.5 s. The pseudo-inverse is necessary because even though on average, the learning matches number of output spikes to number of input spikes, the matrix T is still not usually square and so its actual inverse cannot be taken. In addition, in these simulations, an additional term is introduced in the learning to make sure all the output neurons ﬁre with equal probability. This partially counters the ignoral of the ‘which neuron’ information, which we explained above. Assuming Poisson spike count ni for the ith output neuron with equal ﬁring rate ni it is easy to derive in an approximate ¯ term that will control the spike count, i (¯ i − ni ). The target ﬁring rates ni were set to n ¯ match the “source” spike train in this example. The network learns to demultiplex mixed spike trains, as shown in Figure 2. This demultiplexing is a robust property of learning using (11) with this new spike-controlling term. Finally, what about the spike-timing dependendence of the observed learning? Does it match experimental results? The comparison is made in Figure 3, and the answer is no. There is a timing-dependent transition between depression and potentiation in our result Spike Trains mixing mixed input trains 1 1 0.8 2 0.6 3 0 50 100 150 200 250 300 350 400 450 0.4 500 0.2 output 1 0 2 3 synaptic weights 0 50 100 150 200 250 300 350 400 450 500 original spike train 1 1 0.5 2 0 3 0 50 100 150 200 250 time in ms 300 350 400 450 500 −0.5 Figure 2: Unmixed spike trains. The input (top lef) are 3 spike trains which are a mixture of three independent Poison processes (bottom left). The network unmixes the spike train to approximately recover the original (center left). In this example 19 spikes correspond to the original with 4 deletion and 2 insertions. The two panels at the right show the mixing (top) and synaptic weight matrix after training (bottom). in Figure 3B, but it is not a sharp transition like the experimental result in Figure 3A. In addition, it does not transition at zero (ie: when tk − tl = 0), but at a time offset by the rise time of the EPSPs. In earlier experiments, in which we tranformed the gradient in (11) by an approximate inverse Hessian, to get an approximate Natural Gradient method, a sharp transition did emerge in simulations. However, the approximate inverse Hessian was singular, and we had to de-emphasise this result. It does suggest, however, that if the Natural Gradient transform can be usefully done on some variant of this learning rule, it may well be what accounts for the sharp transition effect of STDP. 3 Discussion Although these derivations started out smoothly, the reader possibly shares the authors’ frustration at the approximations involved here. Why isn’t this simple, like ICA? Why don’t we just have a nice maximum spikelihood model, ie: a density estimation algorithm for multivariate point processes, as ICA was a model in continuous space? We are going to be explicit about the problems now, and will propose a direction where the solution may lie. The over-riding problem is: we are unable to claim that in maximising log |T|, we are maximising the mutual information between inputs and outputs because: 1. The Invertability Problem. Algorithms such as ICA which maximise log Jacobians can only be called Infomax algorithms if the network transformation is both deterministic and invertable. The Spike Response Model is deterministic, but it is not invertable in general. When not invertable, the key formula (considering here vectors of input and output timings, tin and tout )is transformed from simple to complex. ie: p(tout ) = p(tin ) becomes p(tout ) = |T| solns tin p(tin ) d tin |T| (12) Thus when not invertable, we need to know the Jacobians of all the inputs that could have caused an output (called here ‘solns’), something we simply don’t know. 2. The ‘Which Neuron’ Problem. Instead of maximising the mutual information I(tout , tin ), we should be maximising I(tiout , tiin ), where the vector ti is the timing (A) STDP (B) Gradient 100 ∆ w (a.u.) 150 100 ∆ w / w (%) 150 50 0 −50 −100 −100 50 0 −50 −50 0 ∆ t (ms) 50 100 −100 −20 0 20 40 60 ∆ t (ms) 80 100 Figure 3: Dependence of synaptic modiﬁcation on pre/post inter-spike interval. Left (A): From Froemke & Dan, Nature (2002)]. Dependence of synaptic modiﬁcation on pre/post inter-spike interval in cat L2/3 visual cortical pyramidal cells in slice. Naturalistic spike trains. Each point represents one experiment. Right (B): According to Equation (11). Each point corresponds to an spike pair between approximately 100 input and 100 output spikes. vector, t, with the vector, i, of corresponding neuron indices, concatenated. Thus, ‘who spiked?’ should be included in the analysis as it is part of the information. 3. The Predictive Information Problem. In ICA, since there was no time involved, we did not have to worry about mutual informations over time between inputs and outputs. But in the spiking model, output spikes may well have (predictive) mutual information with future input spikes, as well as the usual (causal) mutual information with past input spikes. The former has been entirely missing from our analysis so far. These temporal and spatial infomation dependencies missing in our analysis so far, are thrown into a different light by a single empirical observation, which is that Spike TimingDependent Plasticity is not just a feedforward computation like the Spike Response Model. Speciﬁcally, there must be at least a statistical, if not a causal, relation between a real synapse’s plasticity and its neuron’s output spike timings, for Figure 3B to look like it does. It seems we have to confront the need for both a ‘memory’ (or reconstruction) model, such as the T we have thus far dealt with, in which output spikes talk about past inputs, and a ‘prediction’ model, in which they talk about future inputs. This is most easily understood from the point of view of Barber & Agakov’s variational Infomax algorithm [3]. They argue for optimising a lower bound on mutual information, which, for our neurons’, would be expressed using an inverse model p, as follows: ˆ I(tiin , tiout ) = H(tiin ) − log p(tiin |tiout ) ˆ p(tiin ,tiout ) ≤ I(tiin , tiout ) (13) In a feedforward model, H(tiin ) may be disregarded in taking gradients, leading us to the optimisation of a ‘memory-prediction’ model p(tiin |tiout ) related to something supposˆ edly happening in dendrites, somas and at synapses. In trying to guess what this might be, it would be nice if the math worked out. We need a square Jacobian matrix, T, so that |T| = p(tiin |tiout ) can be our memory/prediction model. Now let’s rename our feedforˆ → − ward timing Jacobian T (‘up the dendritic trees’), as T, and let’s fantasise that there is ← − some, as yet unspeciﬁed, feedback Jacobian T (‘down the dendritic trees’), which covers → − electrotonic inﬂuences as they spread from soma to synapse, and which T can be combined with by some operation ‘⊗’ to make things square. Imagine further, that doing this → ← − − yields a memory/prediction model on the inputs. Then the T we are looking for is T ⊗ T, → ← − − and the memory-prediction model is: p(tiin |tiout ) = T ⊗ T ˆ → − → − Ideally, the entries of T should be as before, ie: T kl = ∂tk /∂tl . What should the entries ← − ← − ← − of T be? Becoming just one step more concrete, suppose T had entries T lk = ∂cl /∂tk , where cl is some, as yet unspeciﬁed, value, or process, occuring at an input synapse when spike l comes in. What seems clear is that ⊗ should combine the correctly tensorised forms → − ← − → ← − − of T and T (giving them each 4 indices ijkl), so that T = T ⊗ T sums over the spikes k and l to give a I × J matrix, where I is the number of output neurons, and J the number of input neurons. Then our quantity, T, would represent all dependencies of input neuronal activity on output activity, summed over spikes. ← − Further, we imagine that T contains reverse (feedback) electrotonic transforms from soma ← − to synapse R lk that are somehow symmetrically related to the feedforward Spike Re→ − sponses from synapse to soma, which we now rename R kl . Thinking for a moment in terms of somatic k and synaptic l, voltages V , currents I and linear cable theory, the synapse to → − → − soma transform, R kl would be related to an impedance in Vk = Il Z kl , while the soma ← − ← − to synapse transform, R lk would be related to an admittance in Il = Vk Y lk [8]. The → − ← − symmetry in these equations is that Z kl is just the inverse conjugate of Y lk . Finally, then, what is cl ? And what is its relation to the calcium concentration, [Ca2+ ]l , at a synapse, when spike l comes in? These questions naturally follow from considering the experimental data, since it is known that the calcium level at synapses is the critical integrating factor in determining whether potentiation or depression occurs [5]. 4 Appendix: Gradient of log |T| for the full Spike Response Model. Here we give full details of the gradient for Gerstner’s Spike Response Model [7]. This is a general model for which Integrate-and-Fire is a special case. In this model the effect of a presynaptic spike at time tl on the membrane potential at time t is described by a post synaptic potential or spike response, which may also depend on the time that has passed since the last output spike tk−1 , hence the spike response is written as R(t − tk−1 , t − tl ). This response is weighted by the synaptic strength wl . Excitatory or inhibitory synapses are determined by the sign of wl . Refractoriness is incorporated by adding a hyper-polarizing contribution (spike-afterpotential) to the membrane potential in response to the last preceding spike η(t − tk−1 ). The membrane potential as a function of time is therefore given by u(t) = η(t − tk−1 ) + wl R(t − tk−1 , t − tl ) . (14) l We have ignored here potential contributions from external currents which can easily be included without modifying the following derivations. The output ﬁring times t k are deﬁned as the times for which u(t) reaches ﬁring threshold from below. We consider a dynamic threshold, ϑ(t − tk−1 ), which may depend on the time since that last spike tk−1 , together then output spike times are deﬁned implicitly by: t = tk : u(t) = ϑ(t − tk−1 ) and du(t) > 0. dt (15) For this more general model Tkl is given by Tkl = dtk =− dtl ∂u ∂ϑ − ∂tk ∂tk −1 ˙ ∂u wkl R(tk − tk−1 , tk − tl , ) = , ˙ ∂tl u(tk ) − ϑ(tk − tk−1 ) ˙ (16) ˙ ˙ where R(s, t), u(t), and ϑ(t) are derivatives with respect to t. The dependence of Tkl on ˙ tk−1 should be implicitly assumed. It has been omitted to simplify the notation. Now we compute the derivative of log |T| with respect to wkl . For any matrix T we have ∂ log |T|/∂Tab = [T−1 ]ba . Therefore: ∂ log |T| ∂Tab ∂ log |T| ∂Tab = [T−1 ]ba . (17) ∂wkl ∂Tab ∂wkl ∂wkl ab ab Utilising the Kronecker delta δab = (1 if a = b, else 0), the derivative of (16) with respect to wkl gives: ˙ ∂Tab ∂ wab R(ta − ta−1 , ta − tb ) = ˙ ˙ ∂wkl ∂wkl η(ta − ta−1 ) + wac R(ta − ta−1 , ta − tc ) − ϑ(ta − ta−1 ) c ˙ R(ta − ta−1 , ta − tb ) = δak δbl ˙ u(ta ) − ϑ(ta − ta−1 ) ˙ ˙ ˙ wab R(ta − ta−1 , ta − tb )δak R(ta − ta−1 , ta − tl ) − 2 ˙ u(ta ) − ϑ(ta − ta−1 ) ˙ = δak Tab Therefore: ∂ log |T| ∂wkl Tal δbl − wab wal . (18) δbl Tal − wab wal [T−1 ]ba δak Tab = ab = Tkl wkl [T−1 ]lk − [T−1 ]bk Tkl b (19) = Tkl [T−1 ]lk − 1 . wkl (20) Acknowledgments We are grateful for inspirational discussions with Nihat Ay, Michael Eisele, Hong Hui Yu, Jim Crutchﬁeld, Jeff Beck, Surya Ganguli, Sophi` Deneve, David Barber, Fabian Theis, e Tony Zador and Arunava Banerjee. AJB thanks all RNI colleagues for many such discussions. References [1] Amari S-I. 1997. Natural gradient works efﬁciently in learning, Neural Computation, 10, 251-276 [2] Banerjee A. 2001. On the Phase-Space Dynamics of Systems of Spiking Neurons. Neural Computation, 13, 161-225 [3] Barber D. & Agakov F. 2003. The IM Algorithm: A Variational Approach to Information Maximization. Advances in Neural Information Processing Systems 16, MIT Press. [4] Bell A.J. & Sejnowski T.J. 1995. An information maximization approach to blind separation and blind deconvolution, Neural Computation, 7, 1129-1159 [5] Dan Y. & Poo M-m. 2004. Spike timing-dependent plasticity of neural circuits, Neuron, 44, 23-30 [6] Froemke R.C. & Dan Y. 2002. Spike-timing-dependent synaptic modiﬁcation induced by natural spike trains. Nature, 28, 416: 433-8 [7] Gerstner W. & Kistner W.M. 2002. Spiking neuron models, Camb. Univ. Press [8] Zador A.M., Agmon-Snir H. & Segev I. 1995. The morphoelectrotonic transform: a graphical approach to dendritic function, J. Neurosci., 15(3): 1669-1682

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An elegant alternative for the processing of sensor-array signals, normally performed with statistical pattern recognition techniques [1], involves adopting solutions from the biological olfactory system. The use of neuromorphic approaches provides an opportunity for formulating new computational problems in machine olfaction, including mixture segmentation, background suppression, olfactory habituation, and odor-memory associations. A biologically inspired approach to machine olfaction involves (1) identifying key signal processing primitives in the olfactory pathway, (2) adapting these primitives to account for the unique properties of chemical sensor signals, and (3) applying the models to solving specific computational problems. 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This article presents a model that captures the first three abovementioned primitives: population coding, chemotopic convergence and contrast enhancement. The model operates as follows. First, a large population of cross-selective pseudosensors is generated from an array of metal-oxide (MOS) gas sensors by means of temperature modulation. Next, a self-organizing model of convergence is used to cluster these pseudo-sensors according to their relative selectivity. This clustering generates an initial spatial odor map at the GL layer. Finally, a lattice of spiking neurons with center on-off surround lateral connections is used to transform the GL map into identity- and intensity-specific attractors. The model is validated using a database of temperature-modulated sensor patterns from three analytes at three concentration levels. The model is shown to address the first problem in biologically-inspired machine olfaction: intensity and identity coding of a chemical stimulus in a manner consistent with neurobiology [4, 5]. 2 M o d e l i n g c h e m o t opi c c o n v e r g e n c e The projection of sensory signals onto the olfactory bulb is organized such that ORNs expressing the same receptor gene converge onto one or a few GLs [3]. This convergence transforms the initial combinatorial code into an organized spatial pattern (i.e., an olfactory image). In addition, massive convergence improves the signal to noise ratio by integrating signals from multiple receptor neurons [6]. When incorporating this principle into machine olfaction, a fundamental difference between the artificial and biological counterparts must be overcome: the input dimensionality at the receptor/sensor level. 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To model chemotopic convergence, these temperature-modulated pseudo-sensors (referred to as ORNs in what follows) must be clustered according to their selectivity [8]. As a first approximation, each ORN can be modeled by an affinity vector [9] consisting of the responses across a set of C analytes: r K i = K i1 , K i2 ,..., K iC (1) [ ] where K ia is the response of the ith ORN to analyte a. The selectivity of this ORN r is then defined by the orientation of the affinity vector Κ i . A close look at the OB also shows that neighboring GLs respond to similar odors [10]. Therefore, we model the ORN-GL projection with a Kohonen self-organizing map (SOM) [11]. In our model, the SOM is trained to model the distribution of r ORNs in chemical sensitivity space, defined by the affinity vector Κ i . Once the training of the SOM is completed, each ORN is assigned to the closest SOM node (a simulated GL) in affinity space, thereby forming a convergence map. 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This results in a spreading of the ORN projections to neighboring units and, therefore, significantly reduces the number of dormant units. We measure the performance of the convergence mapping with the entropy across the lattice, H = −∑ Pi log Pi , where Pi is the fraction of ORNs that project to SOM node i [13]. To compare Kohonen and conscience learning, we built convergence mappings with 3,000 pseudo-sensors and 400 GL units (refer to section 4 for details). The theoretical maximum of the entropy for this network, which corresponds to a uniform distribution, is 8.6439. When trained with Kohonen’s algorithm, the entropy of the SOM is 7.3555. With conscience learning, the entropy increases to 8.2280. Thus, conscience is an effective mechanism to improve the spreading of ORN projections across the GL lattice. 3 M o d e l i n g t h e o l f a c t o r y b u l b n e t wo r k Mitral cells, which synapse ORNs at the GL level, transform the initial olfactory image into a spatio-temporal code by means of lateral inhibition. Two roles have been suggested for this lateral inhibition: (a) sharpening of the molecular tuning range of individual M cells with respect to that of their corresponding ORNs [10], and (b) global redistribution of activity, such that the bulb-wide representation of an odorant, rather than the individual tuning ranges, becomes specific and concise over time [3]. More recently, center on-off surround inhibitory connections have been found in the OB [14]. These circuits have been suggested to perform pattern normalization, noise reduction and contrast enhancement of the spatial patterns. We model each M cell using a leaky integrate-and-fire spiking neuron [15]. 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