Introduction to Neuromorphic Communication

Lande, Tor Sverre

doi:10.1007/978-0-585-28001-1_8

Cited by 2 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such systems would need to account for, or be invariant to, the concurrent amplitude modulation of the PC/IN network. The fact that the output rhythms are sparse (i.e., only a fraction of PCs spike on every cycle) makes the signal energy efficient and potentially suitable for use in neuromorphic engineering [ 40 , 41 ].…”

Section: Discussionmentioning

confidence: 99%

Flexible resonance in prefrontal networks with strong feedback inhibition

et al. 2018

View full text Add to dashboard Cite

Oscillations are ubiquitous features of brain dynamics that undergo task-related changes in synchrony, power, and frequency. The impact of those changes on target networks is poorly understood. In this work, we used a biophysically detailed model of prefrontal cortex (PFC) to explore the effects of varying the spike rate, synchrony, and waveform of strong oscillatory inputs on the behavior of cortical networks driven by them. Interacting populations of excitatory and inhibitory neurons with strong feedback inhibition are inhibition-based network oscillators that exhibit resonance (i.e., larger responses to preferred input frequencies). We quantified network responses in terms of mean firing rates and the population frequency of network oscillation; and characterized their behavior in terms of the natural response to asynchronous input and the resonant response to oscillatory inputs. We show that strong feedback inhibition causes the PFC to generate internal (natural) oscillations in the beta/gamma frequency range (>15 Hz) and to maximize principal cell spiking in response to external oscillations at slightly higher frequencies. Importantly, we found that the fastest oscillation frequency that can be relayed by the network maximizes local inhibition and is equal to a frequency even higher than that which maximizes the firing rate of excitatory cells; we call this phenomenon population frequency resonance. This form of resonance is shown to determine the optimal driving frequency for suppressing responses to asynchronous activity. Lastly, we demonstrate that the natural and resonant frequencies can be tuned by changes in neuronal excitability, the duration of feedback inhibition, and dynamic properties of the input. Our results predict that PFC networks are tuned for generating and selectively responding to beta- and gamma-rhythmic signals due to the natural and resonant properties of inhibition-based oscillators. They also suggest strategies for optimizing transcranial stimulation and using oscillatory networks in neuromorphic engineering.

show abstract

Section: Discussionmentioning

confidence: 99%

Flexible resonance in prefrontal networks with strong feedback inhibition

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Both excitatory and inhibitory synaptic plasticity can be modeled by our iono-neuromorphic design approach to significantly enhance the computational capacity of the on-chip system. Such iono-neuromorphic Hebbian learning systems may be applied to a variety of robotics, pattern recognition, machine learning, and nonlinear adaptive control problems (92,93) in a powerefficient, compact environment.…”

Section: Discussionmentioning

confidence: 99%

A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity

Rachmuth

Shouval²,

Bear

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

179

149

View full text Add to dashboard Cite

Current advances in neuromorphic engineering have made it possible to emulate complex neuronal ion channel and intracellular ionic dynamics in real time using highly compact and powerefficient complementary metal-oxide-semiconductor (CMOS) analog very-large-scale-integrated circuit technology. Recently, there has been growing interest in the neuromorphic emulation of the spike-timing-dependent plasticity (STDP) Hebbian learning rule by phenomenological modeling using CMOS, memristor or other analog devices. Here, we propose a CMOS circuit implementation of a biophysically grounded neuromorphic (iono-neuromorphic) model of synaptic plasticity that is capable of capturing both the spike rate-dependent plasticity (SRDP, of the Bienenstock-Cooper-Munro or BCM type) and STDP rules. The iono-neuromorphic model reproduces bidirectional synaptic changes with NMDA receptor-dependent and intracellular calcium-mediated long-term potentiation or long-term depression assuming retrograde endocannabinoid signaling as a second coincidence detector. Changes in excitatory or inhibitory synaptic weights are registered and stored in a nonvolatile and compact digital format analogous to the discrete insertion and removal of AMPA or GABA receptor channels. The versatile Hebbian synapse device is applicable to a variety of neuroprosthesis, brain-machine interface, neurorobotics, neuromimetic computation, machine learning, and neural-inspired adaptive control problems.iono-neuromorphic modeling | rate-based synaptic plasticity | silicon neuron | subthreshold microelectronics | VLSI circuit L earning and memory are emergent animal behaviors governed by activity-dependent neuronal adaptation rules in response to changing environments. A putative neuronal mechanism of learning and memory is Hebbian synaptic plasticity (1)-the adaptive modification of excitatory synaptic strength following paired activation of the pre-and postsynaptic neurons. Two classic paradigms for the induction of Hebbian synaptic plasticity in the mammalian hippocampus and neocortex are rate-based plasticity (2-4) [herein referred to as spike-rate-dependent plasticity (SRDP)] and spike-timing-dependent plasticity (STDP) (5-7). The SRDP induction protocols control presynaptic firing rate in order to vary the sign and magnitude of synaptic plasticity (8): a high-frequency (20-100 Hz) train of presynaptic pulses results in long-term potentiation (LTP) of the synaptic strength, whereas a low-frequency (1-5 Hz) train results in long-term depression (LTD). These protocols are consistent with the theoretical learning rule (BCM rule) proposed by Bienenstock, Cooper, and Munro (9), in which the sign and magnitude of synaptic plasticity are controlled solely by postsynaptic activity as determined by presynaptic firing rate: low postsynaptic activity weakens synaptic efficacy and high postsynaptic activity strengthens it. By contrast, the STDP induction protocol stipulates that precise timing of preand postsynaptic activities determines the direction and strength of synaptic pl...

show abstract

Introduction to Neuromorphic Communication

Cited by 2 publications

References 6 publications

Flexible resonance in prefrontal networks with strong feedback inhibition

Flexible resonance in prefrontal networks with strong feedback inhibition

A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity

Contact Info

Product

Resources

About