Decoding Temporal Information: A Model Based on Short-Term Synaptic Plasticity

By Dean V. Buonomano. Journal of Neuroscience, 2000

350 citations recorded by the source corpus.

In the current paper it is proposed that short-term plasticity and dynamic changes in the balance of excitatory–inhibitory interactions may underlie the decoding of temporal information, that is, the generation of temporally selective neurons. Our initial approach was to simulate excitatory–inhibitory disynaptic circuits. Such circuits were composed of a single excitatory and inhibitory neuron and incorporated short-term plasticity of EPSPs and IPSPs and slow IPSPs. We first showed that it is possible to tune cells to respond selectively to different intervals by changing the synaptic weights of different synapses in parallel. In other words, temporal tuning can rely on long-term changes in synaptic strength and does not require changes in the time constants of the temporal properties. When the units studied in disynaptic circuits were incorporated into a larger single-layer network, the units exhibited a broad range of temporal selectivity ranging from no interval tuning to interval-selective tuning. The variability in temporal tuning relied on the variability of synaptic strengths. The network as a whole contained a robust population code for a wide range of intervals. Importantly, the same network was able to discriminate simple temporal sequences. These results argue that neural circuits are intrinsically able to process temporal information on the time scale of tens to hundreds of milliseconds and that specialized mechanisms, such as delay lines or oscillators, may not be necessary.

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Citations in the corpus

Computation Through Neural Population Dynamics (2020, background): “…state-dependent networks, which inherently keep track of time as they are composed of units that have time-dependent biological properties, and thus evolving states of these networks encode the passage of time (Buonomano 2000, Buonomano &Maass 2009, Karmarkar & Buonomano 2007, Maass et al. 2002).”
The Neural Basis of Timing: Distributed Mechanisms for Diverse Functions (2018, background): “Other models have proposed that short-term synaptic plasticity contributes to the formation of temporally selective neurons (Buonomano and Merzenich, 1995; Buonomano, 2000; Fortune and Rose, 2001).”
Neuron: A Tool for Neuroscientists (2001, background): “2000) encoding of temporal information (Buonomano 2000; Lytton and Lipton 1999)”
Memory and Information Processing in Neuromorphic Systems (2015, background): “It has been demonstrated that this mechanism can play a fundamental role in neural computation [81], [87], [88], e.”
Dedicated and intrinsic models of time perception. (2008, background): “Alternatively, timing might be ubiquitous and arise as part of modality-specific processing [ 37 ,38].”
The neural representation of time. (2004, background): “Consistent with this idea, physiological mechanisms such as slow inhibitory post-synaptic potentials (IPSPs) are ubiquitous in the nervous system, and could serve as the building block for temporal processing [49 ,52].”
State-dependent computations: spatiotemporal processing in cortical networks (2009, background): “Thus, the active state that is produced by a second stimulus event will be influenced by the time that has elapsed since the previous stimulus (because of the hidden state), and it can therefore be used to determine the interval between stimul”
The tempotron: a neuron that learns spike timing–based decisions (2006, background): “Solving such demanding tasks requires the addition of working memory mechanisms or slow synaptic dynamic”
The neural basis of temporal processing. (2004, background): “19 Jun 2004 14:40 AR AR217-NE27-12.tex AR217-NE27-12.sgm LaTeX2e(2002/01/18) P1: IKH 334 MAUK BUONOMANO Figure 10 shows results from a state-dependent network model capable of discriminating intervals as well as simple sequences (Buonomano 2000).”
NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail (2010, background): “This includes investigation of synchronous activity [13,14], oscillations [15–17], sensory representation [18,19], locomotion [20] and memory [21] together with”
Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems (2014, background): “The circuit time constant is set by Vτ while the synaptic efficacy, which determines the EPSC amplitude, depends on both Vw0 and Vthr [41].”
Memory without Feedback in a Neural Network (2009, unknown): “This may explain why previous studies of temporal processing by networks with random connectivities, such as those used to model temporal intervals (Buonomano, 2000; Karmarkar and Buonomano, 2007) and temporal classiﬁcation (Maass et al., 2002), were limited in applicability to time intervals on…”
Timing in the absence of clocks: encoding time in neural network states. (2007, methodology): “…networks (SDNs), propose that neural circuits are inherently capable of temporal processing as a result of the natural complexity of cortical networks coupled with the presence of time-dependent neuronal properties (Buonomano and Merzenich, 1995; Buonomano, 2000; Maass et al., 2002).”